Best Practices in Developing G Suite Business Apps (Cloud Next ’19)


[MUSIC PLAYING] SATHEESH NANNIYUR:
In this session, we are going to talk about
developing applications on G Suite, and some of dos
and don’ts of developing applications and how to set
up organizations in this particular session. So my name is Satheesh. I am your host for
the session today. Along with me, my co-presenters
are Monica from Genentech, and we are going to have
Sambit from Google Cloud talk in this session as well. So we will start with
giving an overview of what kind of challenges
our enterprise customers face when it comes to developing
applications on G Suite, and how we are driving certain
industry trends with some of our product lines,
followed by talking about our specific products. And then we’ll have Monica
present how they’re organized, and what are some of the best
practices in their organization with respect to app
development on G Suite. Then we’ll talk about, what
is the future looking like, what are the key trends that we
are seeing in the industry when it comes to developing apps
in the productivity space generally, and within G Suite
as well more specifically. And then we’ll wrap
up this session with an overview of some
of the exciting features that we are announcing today
with respect to G Suite development platforms. So before we get
started, a quick note on how to submit questions. So all of you must
have the mobile app. So you will be able
to submit questions through your mobile
app on this Dory. So you can go to this
particular session details, and then submit
[INAUDIBLE] there. Towards the end, we will try
to address your questions. We’ll also be able
to– hopefully we’ll have time to take some
live questions from this room as well. Sounds good? Perfect. So, without further
delay, let’s get started. So imagine yourself to
be a salesperson, an HR professional,
financial analyst– many different roles
in an enterprise. Your key responsibility
is in driving the business with
respect to your role, with respect to your
area of expertise. When you do this,
you’re obviously using many different
applications. You’re using G Suite, plus
you’re using other enterprise applications as part of this. That means you need some
customizations in order to run your business process. You need all those apps. When it comes to
getting those apps, there are some key
challenges that the business users in an organization face. Number one challenge is
what we call the skills gap. As the business
process owner, you are the expert in
your business process. You know how your
process should run. You know how your
business is run. You know your role
better than anybody else in your organization. When you want to
get those apps, you need to go to your IT
developers, and talk to them, and educate them about
the business process. And then they have the
technical expertise to develop those applications. Do you see the problem there? So on one hand,
the business users are really proficient
with their processes. They have the right
skills for that, but they lack the
technical expertise. On the other hand, the
technical developers have the right
technical knowledge, but they don’t know all
the business processes. This is what we refer
to as a skills gap. This requires communication
back and forth in order to get the right
application that you need. This is the number
one challenge. The second challenge
is, the IT developers, now they have to work with
many different business users in the organization,
across the enterprise, understand those
business processes, and then develop the
applications for them. That leads to scaling challenges
for the IT developers. The resources become
limited as a result of that. Any organization, this
is a very common problem. The third challenge
that we see is that technology keeps evolving. Business processes
also keep evolving. And as a result of that,
it’s hard to keep pace with those changes. Everybody’s always
playing catch-up in order to stay in tune
with these changes that are happening. That leads to delays in
getting the apps that you need. That leads to getting
the updates to the apps that you’re using. That’s the number
three challenge. The net result of all
of these challenges is that the involved
stakeholders get frustrated. There are many
different stakeholders. I have identified three
stakeholders here. Number one is the business user
who needs these applications. Number two is IT developer,
the technically proficient developers. Our number three
is somebody who is tracking the cost,
and the schedules, and managing all these
programs and costs, what I call the IT director here. The business users are
frustrated because they are not able to get the
right apps that they need on time or the updates
that they need on time. The IT developers are frustrated
because their project backlog just keeps growing
as they try to work with many different
organizations in enterprise. The net result of
the IT director is that now they
are facing the cost and the scheduled [INAUDIBLE]. Those are the challenges
that everybody faces– all the stakeholders face. How are enterprises
addressing this? What are the shifts that are
happening in the industry to address this? So number one shift
that is happening is that the application
development itself is being moved to
the business users– closer to the
business users– where they have the right expertise
on the business process and they are in
the best position to find out what
is the application that they need, and
better still, actually develop these applications. This is happening on no-code
tools for the business users to develop those applications. The second shift that is
happening in this industry is that there are lot
of SaaS applications that are coming up– Software as a
Service applications. When there is a
need, it’s probably most efficient to go
and buy an application, as long as there is one
that meets the need. That has led to the growth of
the enterprise marketplaces and the growth of
the ecosystems, where ISVs and third-party
developers develop these apps and make them available to
the different businesses. The third shift is
within the enterprise IT. IT’s role itself is evolving. It’s evolving from one of being
an app developer and an app development
organization to being an enabler for development of
applications, by the businesses and by the business users. The business users in
this slide are also called the knowledge workers. So the knowledge workers
are now developing the apps, and the IT organizations are
became becoming the enablers. They’re providing
the right tools, they are providing
the right data access. They’re providing the right
guardrails– security, et cetera– to make sure
that those applications meet the organization’s needs and
comply with the organization’s policies. In Google and in G Suite,
we are at the forefront of driving these shifts. Number one, with respect
to the first shift, we are providing the
low-code and no-code tools to enable the business users
to develop these applications. Number two, we are
building this ecosystem and we are building
this marketplace where business users in
an organization can go and find
applications that they need and start deploying and
using those applications. With respect to
the third shift, we are also providing the
right tools and technologies that data administrators
need in order to ensure that all these
apps that are being built by the knowledge workers
across an organization remain secure, the enterprise’s
data remains secure, and that the IT administrators
can go on and monitor the application usage and
make sure that they’re able to whitelist the apps
that they allow the enterprise users and the business users
to install and use that. That’s how we are driving these
shifts in the G Suite Developer platform. Getting into the
specifics, I want to talk about the five products
in G Suite Developer platform, give an overview, so
that you can go out and explore further
details on this. So the number one developer tool
that we provide is Apps Script. Apps Script is a
low-code platform. How many of you here
have already heard about Apps Script. That kind of shows how
popular Apps Script is. You can actually
see that there are over three billion weekly
executions on Apps Script. So it’s a low-code
developer platform, and it enables the business
users to quickly build apps. How so? Because it provides
an integrated document environment. It provides APIs for
all the G Suite apps. It also provides security,
in terms of OAuth, et cetera. And it provides an integrated
runtime environment, so that when you’re
building the app, you don’t have to look
elsewhere to think about where you’re going to run that app. So it has that integrated
runtime serverless environment that you can use to go ahead
and run the application. From a best practices
perspective, if you have an
application that is going to be used by, let’s
say, a few hundred users, Apps Script provides the
perfect platform to get started. Apps Script still
requires some coding and some proficiency in coding. This is for what we call
the advanced knowledge workers, or citizen developers. Let’s say you build an
app and it becomes very popular in your organization. Now it needs to be used by,
let’s say, a few thousand users as opposed to a
few hundred users. That’s the time when,
as a business user, you talk to the IT
organization and IT developers, figure out how to
scale up application. And also, potentially, you need
new features– maybe some email or AI capabilities, maybe some
data analytics capabilities. That’s when you can use
Google Cloud Platform, and scale that application,
and build the new features. The second tool that I want
to talk about is App Maker. App Maker is intended to
be a no-code platform, a no-code application
development tool. This is for pure business users,
knowledge workers who cannot code. At this point in
time, App Maker is great for building simple cloud
applications with the data that you are already using
for your business users. If you want something that
is a bit more advanced– if you want more
advanced customization– you can use Apps Script
to customize your app that is built using Apps Maker. So from a best
practices perspective, if you are a business user, you
will start building the app– as long as it’s a simple
cloud application, you should be able
to build with all the visual drag-and-drop tools,
as is illustrated on the slide here. If you want more
customizations, you would go to the IT
department and try to seek some of
their help in order to customize the application. The third tool that
I’m going to talk about is the G Suite Add-Ons. I literally know of
nobody who is just using one or two applications
in their business, right? They’re always using a
suite of applications. For example, if
you’re a salesperson, you’re using G Suite, Gmail,
Events, Calendar, et cetera. But chances are very
high that you are also using a Salesforce or a dynamic
CRM along with this G Suite. So Add-Ons provides the right
tools and the framework for you to get an integrated experience
with third-party apps. That’s what Add-Ons
is intended to do. It provides an
integrated experience. It also provides a
development environment in order to build those
Add-Ons so that they we use multiple applications along
with G Suite, in conjunction with G Suite, in an
integrated experience. So from a best
practices perspective, you’d look for these add-ons– to start with, on the G
Suite marketplace, where the chances are that you’ll
be able to find the right add-on that you need. Otherwise, this is not a
development tool intended for the knowledge workers. Rather it is a framework
intended for use by the knowledge workers. So from a development point
of view, you have to go to IT and ask them to
develop an add-on and make it available
to you, and potentially many of your colleagues
in the organization. The next one is the
G Suite Marketplace. There are over 6,000
ISV applications– both web applications,
productivity tools, add-ons, that are available on
G Suite Marketplace. So if you’re looking to
solve a particular problem, this is probably the
best place to start with. Look to see whether
there is already an application that’s available,
and use that application. And if not, then
you’ll have to look at how to build something in
collaboration with your IT. So if you are in
the IT organization, from a best practice
perspective, you should talk to
them about the app that you need so that, in IT,
you can actually make sure that the application
that you want to make available to the rest
of your organization is secure, it meets all your needs. And then that application can
be whitelisted, and make sure that all the other business
users can use that. The last tool that
I’m led to talk about is the Admin Console. So the Admin Console provides
a number of different tools and techniques to make sure that
the apps in your organization remains secure and the
data in your organization remains secure. With the shifts that
we talked about, now a lot of different
knowledge workers will be building
applications through their entire organization. The role of the IT
now is to become an enabler, a facilitator
for this kind of application development. As a result of that,
it’s very important that IT’s role is to keep the
security of the applications and the security of
the data, and make sure that these apps and data meet
the compliance requirements of the organization. In order to do this,
we are providing a number of different tools,
including whitelisting of the applications. Only the whitelisted
applications can be installed by the
users in your organization. Providing data access
controls via whitelisting APIs and enabling APIs, providing
some data guardrails, as well as monitoring
the app usage and ensuring that the resources
that are allocated from the app are maintained. So those are some
of the ways how we are enabling
the IT to go and be an enabler in your
organization in turn, for knowledge worker
apps to be built. This is an important area
of investment for us. We know that there is a lot
more work to be done here, and we are working on
bringing more capabilities to ensure that, as
IT organizations, you can empower your
business users to build apps and maintain the
security of those apps. So, so far, I gave
you an overview of the different
discrete developer tools and an overview of
what are the industry shifts. I want to take a moment to
summarize some of the best practices that we have learned
from many of the customers that we have spoken to. These best practices, I have
divided them by two personas. One is a knowledge worker, and
the other is IT administrator. So if you’re a
knowledge worker and you are looking to build
an app, your first step should be to identify
what kind of experience your app needs to deliver. Is it a web app that users
are going to access via URL? Is it an add-on that will
be available along with G Suite in the side panel? Or is it an automation–
automation meaning and event-drive app
that automatically does some task for
you in response to some kind of a system event? That would be the first step. The second step is to
lay out the resources that your application needs. These resources could
be, for example, certain compute resources or
certain storage resources. Or maybe you want some
access to resources such as ML models, et cetera. Depending on the
resources that you need, you can think about
what kind of platform that you want to build
your application on. The third is the data
sources and the retrieval. The data sources could
be– for instance, it could be some on-prem system
that you’re already using. If you are in IT, you
have to think about, how will I make this data
available to the knowledge workers so that they
can build our apps? The data sources could also
be some other third-party SaaS services that you
are looking at. And maybe what you want to
access is just a few data items, potentially using APIs. In some cases, you may
want a large amount of data that needs
to be analyzed by the application itself
using some kind of pretty analytics tools. So the fourth step is,
using all of these data from the first three
steps to make sure that you’re choosing the
appropriate G Suite developer tool. If you’re looking to
build a simple application with no code, you will
probably start using App Maker. If you want some customizations
that are a bit more advanced, than you would start
looking at Apps Script, and start using that particular
tool for building your app. If you are looking for much
more advanced data analytics– crunching a large amount
of data, using some email, or if you are looking for
advanced storage such as Cloud SQL, then you would be thinking
about building your app on GCP, the Google Cloud Platform. Those are the kind of things
that you should look at. So now, once you
build this app, you should also think
about how to share this app with the other
users in the organization. This could be, for
example, even IT, providing some amount
of pre-built code for the other knowledge
workers to develop apps on. Or it could be IT enabling
all the enterprise users to use this app via a private
listing on the enterprise marketplace– on the
G Suite marketplace. Or if you’re even looking– if you are a third-party
developer or an ISV, you can use the G
Suite Marketplace as your distribution
platform so that you can reach many different
enterprise customers and have them use
your application. Now, looking at it from an IT
administrator’s perspective, the best practices
are– number one is, make sure that your
business users, the knowledge workers in your
organization, are empowered and they’re aware of the tools. Make the right tools available. For example, you
may want to make App Maker available
for all your business users in the organization–
enable it for them. Or you may want to build some
community around the knowledge workers so that they can
collaborate with each other, share information
with each other, and build the
application on their own. This is important for
you as an IT organization because that reduces
the load on your– and the stress on
your organization, by moving the applications
closer to the business users. The second best practice
is to establish data access and connectivity. If you want your
knowledge workers to be able to build apps
around on-prem data, make sure that
data is available. And the third best
practices is to enforce security and governance. Now when you’re looking
at enabling your business users to install applications
from the marketplace, make sure that they’re secure. If you are enabling
your knowledge workers to build those
applications, then make sure that
those apps are also secured before they are widely
used in your organization. Or you may want to establish
some data guardrails or some quartiles in
the compute to make sure that those apps comply
with those limitations that you enforce. So those are some of the best
practices that we have learned from many different customers. So at this point, I would
like to invite Monica onstage to talk about application
development, and Genentech, and how they’re organized. MONICA KUMAR: Thanks, Satheesh. Hi, everyone, welcome
to the session, and it’s great to be here. Thanks to Sambit and Satheesh
for inviting us here. I have a couple of my colleagues
from Roche and Genentech. So– glad we could
get a team together. So I want to start off with
talking about who we are. So maybe some of the US-based
folks may know Genentech, but Roche is basically a global
pharma company based in Basel, Switzerland, with around– you can see we have
over 100 locations worldwide, with around
95,000 employees. And Genentech, which is a
US-based biotech company, was acquired by Roche in 2009. And ever since, we have been
a member of the Roche group. Another fun fact–
I mean, it’s really a huge number– the 11 billion
Swiss francs in R&D investment. So we basically are focused
on four therapeutic areas– oncology, immunology,
neuroscience, and infectious diseases. And really, we have both
the diagnostics and pharma divisions under one roof. And this gives us the
unique opportunity to actually look at
the patient health care across the whole
spectrum, so right from prevention, diagnosis,
treatment, and then monitoring. And our mission
is really to find those unique and best solutions
to improve our patients’ lives. To really support our business
and to fulfill the mission to have the best solutions
for our patients, we are looking at,
from an IT perspective, how can we actually
simplify the landscape, empower teams with
the right tools, and also support these
new ways of working. Our business is going through
a major transformation today. And what you see on
the left hand side– and just to give
you a background, Roche migrated to
G Suite in 2013. And prior to that, because the
company had been in business for more than 20, 30
years, as some of you know, you tend to build
up on legacy applications, legacy platforms. And lots of custom solutions on
those platforms had been built. So we had sort of a messy
application landscape. And we also have– ever since we’ve
moved to the cloud, we also got these
third-party apps that were sort of
confusing our end users– when do I use this versus that? Microsoft was embedded
in the organization before we moved to G Suite. So a lot of the
questions is, when do I use SharePoint versus
Team Drive or Sites? And so our leadership looked
at this last year, and we said, there is a certain power
in offering our end users a default. And that
default is actually G Suite. So we believe G Suite offers
the right capabilities to make our end users as
productive as possible. But along with G Suite– So the G Suite ++, is really
about these third-party apps that we have also. So we use Smartsheet,
Box, Trello. All of these apps actually
add to that experience, they enhance, and
they meet the gaps that we have just in the
basic Collaboration Suite. So how are we organized to
support this very large, very complex organization? We have a global
IT team oversees that looks at where
is the business going, and what are the enterprise
solutions we need to provide our customers so that they
are not waiting for this and having to do all
this work on themselves. So for example, we are focused
on personalized health care, in the Roche science
infrastructure, ERP, and many cloud capabilities,
even around automation. So that’s something
that global IT provides, those platforms and tools. The functional IT is basically
embedded in the business. They actually have
the closest proximity to what’s going on in
each business division. So for example, our
business functions can be from research,
manufacturing, diagnostics, commercial. So each of these businesses have
their own individual demands, and they have their own
business-critical applications that they work with. And that team actually
sits, and delivers, and drive that global
IT strategy forward. And then, of course,
we wouldn’t be here and be able to do what
we do without hundreds of these knowledge workers
who are both developing, but they’re also
consuming these services. But they are the ones
that are actually building these solutions,
using some of the development platforms we have. And we have a wide spectrum. Given the application
landscape that we have and the complexity of
the business demand, too, we have every– low-code, to medium, to the very
complex apps, a wide spectrum there. And so in the low-code,
we have seen a lot of– because we’ve been on
G Suite for a while. We’ve seen lots and lots
of knowledge workers build app scripts for many,
many different solutions that they want. So for example, Apps Script
comes embedded within G Suite. It gives you the ability to
connect with the G Suite API. So anyone who has
curiosity to solve a problem within their
own group can just pick it up and get started. It offers the integrated
serverless runtime, and it’s no additional cost. So I think this is
something that we have seen grown very organically. We didn’t have to do a whole
lot to support the organization. This is something
people just ran with. In the medium complexity,
we have Apps Script or other web apps
that have evolved to a higher complexity,
where we are seeing the use of GCP and APIs. In fact, we
ourselves, in IT, have built lots of global solutions,
including our employee directory, which
is called Peeps. We have built that
on GCP, leveraging our identity management
systems, HR systems, bringing together the
data so that that Peeps app can be available
both on Chrome as well as on a mobile device. But I think in the last sort
year and a half, two years, we’ve seen a demand for more
intelligent, contextual apps that will reduce the friction
or the barrier of entry to use them. And these apps could be using
some of the cloud technologies like the natural language
processing, machine learning, and AI. We are actually signed
up with Dialogflow, which is part of the
Cloud AI stack on GCP. And we have about 70 digital
assistants and chatbots, either in a PoC or development stage. So there’s huge
interest and a demand from the business in this area. And again, we are
integrating with some of our big third-party systems,
like ServiceNow, Workday, and SAP as well. So today, I actually want to
talk about two use cases, both built with Apps Scripts. And both of these
actually come from our pharma technical business
operations team, which is basically manufacturing. So this team actually has two
manufacturing pilot plants here in South San Francisco. And they really
wanted to have a tool that enabled to do some
sort of workforce planning– so for both technicians
to be able to plan, like, the next weeks and what’s in
the pipeline, and for management to have oversight over
the activities happening in these plants. And so they looked
at– obviously, there are third-party
tools available. There’s a cost associated
with that as well. But given that our
Genentech processes are so customized to the molecules
and the experiments that are being run
in these plants. Just to buy an off-the-shelf
product wouldn’t work. And they could also
have gone to IT. But IT also adds to the
overhead in the sense they need to explain– firstly, get the resource,
explain all their business processes, the roles. And it takes time to
actually deliver it to the pilot plant workers. And so Scott Linnell, who’s
here with us today, the author and the person responsible
for this app script, actually is very much
like some of the knowledge workers in our organization. He saw this problem. And he’s not a
computer science major. He comes from the life
sciences background. He was an intern at
Genentech in 2017, and just dabbled in Apps Script. And along with another
intern, and then later on, as a full-time employee, took
this on and built the app script to address this need. And I think it’s a great
example of what’s possible. You don’t need to wait to
solve a business problem just because you don’t
have IT resources. And again, there’s another
example from the same team, but for a different use case. So there are these
different equipments. There are five different labs
within our manufacturing team. And they have different
equipment based on the roles that the people have. And earlier, it used to be a
very tedious manual process. People would go to the
equipment, sign up on a sheet, like, hey, I want to use
the equipment from 10 to 11 tomorrow– really manual process. And they actually–
this equipment can’t be booked by just anyone. So they’re booked by the role
that you have on the team. And so again, Apps
Script came super handy. Because they could
actually not only see the availability of the
equipment, book the equipment, it’ll show up on
their calendar, there would be an email
sent to remind them, hey, your equipment
is due for return now, and they could also say the
equipment is broken– they’ve used it and it’s not working. They could just schedule
a maintenance right there, through this tool. They have colleagues now,
in Germany, the same team. And they said, we would
like to use this tool, too. And so they’ve localized
that same app script and used it for their German
colleagues and counterparts. Again, a great example
of how empowering your organization and your
knowledge workers to use what’s at their fingertips today. And we’re really proud of
the work that Scott is doing. He even, in fact, ran Apps
Script training for his team there, to help them build more. I want to leave you with
some best practices. Obviously, we’re
not a small company. So some of our best practices
are really centered around how we can scale and support
a very large organization. And the first one is
the enterprise strategy. And this is not just
about seeing technology for technology’s sake. It’s about how can we deliver
platforms and services that actually meet our
business demand. So we look at a
two- to three-year and see how are we positioning
ourselves with the cloud capabilities, with
infrastructure services, application
development services, to enable and
drive that forward? Because the business is
relying on us to do that. And the second thing we
actually really value a lot is this customer experience. So when we think of IT
services, most people just don’t like going to IT. It takes long. You have to open 10 tickets. You have to go here, go there. We try to bundle these services. So we look at what does
an application developer need when they come to us? What does a DevOps person need? What do these
researchers need when they want to quickly
spin up applications? And so we look at how people
are consuming our service, what are they telling about it,
what is their feedback, where can we do better, and
continuously have this cycle with them to improve it. And then the third thing
that we have to drive is the compliance within
all the products, platforms, and services we provide. And this is a proactive, close
collaboration with security, with legal, with
COREMAP, to make sure that anything we
recommend and anything we say, this is
supported by IT, it’s actually complying with Roche
data and privacy standards. So essentially we
are making sure that the heavy lifting
is already done, so that when end users go into
the application landscape, they can actually pick a product
knowing that IT has vetted it, it’s safe to use. The second piece is around
empowering the organization. And this, the first part,
business partnership is essential for us because of
how diverse and geographically dispersed we are. It’s very important to have– we call them IT
business partners. They’re basically
embedded in the business, but they understand
the IT landscape. They can connect the
dots for the business. They can point them
to the right people. They can point them, hey,
you don’t need to build this; there’s already a solution
available for this. So there is this
cross-sharing of ideas, but also solutions
on how business can solve their problem. We also make a very
concerted effort to make sure that
anything that we introduce into the organization,
there’s full transparency on the roadmap, so there
is nothing unexpected or a surprise. So we make sure that we
have our sounding boards, with our stakeholders
and customers internally. We also have user adoption
services regionally, spread across, who are
actually our channels. And they are
communicating new changes that are coming in our
pipeline to all of the users. We also run a lot of pilots. So we’re very– because we want
the organization to be prepared for change, we make
sure that, for example, whether it’s Team Drive,
or it was Hangouts Meet, or they’re a new
docs API, things like that, that are coming. If we open this, run
pilots in our test domain, give early access to developers
so that they are prepared for changes that they need
to make in their applications or in the way they work. And this actually gives us the
early access to their feedback. And we’ve been actually lucky
to have really great partnership with Google to funnel that
feedback back into the product teams so that this feedback
goes there early and often, and they actually know
what doesn’t work for us and what works for us. And the last thing
is around learning. So I think this is also
very critical, especially as technology is changing. There are new
emerging technologies coming, where our business and
our IT is actually ramping up. So we run hackathons. In fact, procurement just had a
Procure-a-thon two weeks back. This is really to say,
let’s bring our top two, three business problems here. Let’s get a team of developers,
UX, business analysts, all of us come together,
and let’s try and solve this in maybe one or two days. And this is a great
way to understand that you’re pushing the
limits of the APIs available, you’re pushing the
limits of how can we address this problem, can
we address this problem, are we too early,
should we then request more feature updates
from the product teams and come back to this later? This really gives us this cycle
of understanding and learning to be prepared to
do it in production. Part about learning is
definitely knowledge sharing. Again, we are huge or heavy
users of Google+ communities. I can tell you that a lot
of our Google+ users rely– in fact, I met Scott through
one of these communities. I just posted something on Apps
Script, and Scott responded. So there are lots
and lots of people that are connecting
with each other, sharing learnings, sharing even
their failures, like, hey, this didn’t work for me, has
anyone else tried this? And so these network communities
are ones where a lot of folks rely on them for learning
and understanding what’s going on in
the organization for specific subjects. And then centers of excellence– we have Roche experts
in specific domains. So for example G Suite app
development, API integration, we now have one on
conversational platforms. So what we do is we
look at the emerging technologies and the
business demand and say, hey, we need a set of
experts on these technologies that are ready to
jump into projects and to help the
business when they need. And so they are at hand to
advise and guide our business as need be. So we’re still
learning, obviously. This is not set in stone. We are learning and
adapting, and we are continuing to do this
to fulfill the need that– basically address what
our patients need next. And with that, I want to
hand it off to Sambit. Thank you. [APPLAUSE] SAMBIT SAMAL: All right. Thank you, Monica. What I’m going to
do is I’m going to talk about the future
of app development, some of the key trends that
at least we see and we hope that you see the same way. So a few things– so if you look at any
productivity platform, everybody provides the
standard mechanism, the same way of sending mail,
calendar, chats, writing docs, receipts, and things like that. But fundamentally, we see three
different market trends or tech trends which is going to
impact this productivity space in next five to 10 years. So what are those three? The first thing that we see
is we have, now, capability to understand the user context. What do I mean by that? So everybody has
a mobile device. So at any point in time,
systems know where you are. And depending on where
you are, the experience can be customized. So that is the context– an example of the context. The second thing that the
systems are good at today is capturing the usage pattern. So what I mean by that
is how you do your work, the systems nor how you
are doing that work. So things can be
customized as per that. For example, if you’re
always offlining something, the systems can know. And based on how and
when you are doing it, we can take actions on that. And the third thing
that happens is, when you go to a
new organization, the way to learn about that
particular organization is you go and ask people. The knowledge in
the organization is there in people’s heads. It’s sort of the
tribal knowledge. Wouldn’t it be better for you
to know in a systemic way? There are some people who
have tried this using sort of structured data analysis. But given the fact that
today we have this knowledge scattered across different
chat exchanges, different email exchanges, different docs, a
way to synthesize that knowledge will become important. And that’s what we’re
calling enterprise knowledge. Using these three,
you can potentially categorize the experiences
that are going to come into three broad categories. I’ve called this as
assistive experience, knowledge visibility,
and process automation. Let’s look at each of these. So this will give you an idea
of what I’m talking about. So if you drive any new
car today, what you can see is there is blind spot
detection in most of the cars. What is that doing? It’s helping you drive better. It’s providing an assistive
capability on driving. You can see the same pattern
emerging in software. So if you look at a chat, and
the moment some chat comes in, it suggests to you some
option based on the context. And why does that help you? Especially on a
mobile device, it helps you give a response
which is relevant. So that is assisting
you in responding. You can see that
if you have used Gmail auto-compose– the
same kind of mechanism. The opportunity here is bring
that to the developer platform so that you can use that
or the knowledge workers can use that to build
assistive experiences. The next thing I’m
going to talk about is this whole idea of
enterprise knowledge. Now, with the
structured data, you can go to your analytics
system and know, for example, who the best customer
is, and is he being spoken to by the best
customer service representative in your organization. Who is the expert in
a particular area? But with enterprise knowledge,
it will be possible for you to, without having any
structured analysis, know who is the expert
and who do we reach out to if we need some help, be
it usual things like 401(k) or anything of that sort. So think about it. When an average worker
spends 20% of the time– if you say that instead
of working for five days, you’re walking for
four days, that’s 20%. Or you can use that day
to do your 20% project. Whichever way you look at
it, that’ll help you do that. The third thing I’m going
to talk about is automation. This use case, all
of us go through. We want to have a discussion,
and we want to have a chat. And what happens
is, before we know, five or 10 email
chats gets exchanged before we set up a meeting. The system recognizes that. So let’s do some
time slots by looking at your calendar
and your ability. And you click– just one click–
and the meeting is set up. Not only that, based
on conversation, maybe it can set up
the agenda, figure out which are there the
documents that are important, and attaches that to
the Calendar invite. All those things will be
possible by automating processes and tasks. So that is the third
big trend you will see. Most of the productivity
improvement and the ensuing developer tools will
capture these three trends. Now to the final section. So what’s new in G Suite? I’m going to talk
about three things. So we are launching a
new Add-Ons platform. Add-Ons has been
there for a long time. But we are going to do
a new Add-Ons platform. What that will help you do is,
instead of driving an add-on for each of the G Suite
apps, you write it once, and it works across all
the different G Suite apps. It will have the user
context, and you can have that customized user context. It will make the
development easier, it will make the
management easier. It’s that uniform experience
across G Suite instead of per host app. The second thing that
we are announcing today is Alpha for data connectors. So what this means
is most of you, as you tried to move
your workload to cloud, you have this hybrid
scenario where you wanted the cloud to work
with your on-prem system. So with this Alpha,
what we are doing is we are integrating Sheets
with the on-prem relational Datastore you have on
your on-prem data center. This could be SQL Server,
this could Oracle, this could be MySQL. So you can have all that
data come in to Sheets and be used in
Sheets, and you can have that hybrid experience. The Third thing that I’m going to
talk about an announce today is what we’re calling G Suite
Marketplace Security Assessment Program. The GSM Marketplace, it
has more than 6,000 apps, as was talked about. It becomes very,
very challenging for people to know
which apps to rely on, which apps not rely on, and
it’s a big challenge for admin. We have partnered with some of
the industry-leading security analysts. And the publisher
of these apps, they can go and have their
apps security assessed. And if they pass the test,
we’ll send them a badge. Then that becomes easy
for the administrator to facilitate an
[INAUDIBLE] buying process. So those are the
three announcements. With that, I’ll
end this session. But your feedback
is super important. It’s a gift for us. So please provide the
feedback, and that will help us improve the system. [MUSIC PLAYING]

Automating Visual Inspections in Energy and Manufacturing with AI (Cloud Next '19)



my name is Mandi 4-h and I lead the industrial AI initiative for Google cloud thank you so much for joining really delighted to have you here at Google we believe that the goal of every technology should be to enrich our lives to take our societies our collective humanity forward and do so in a responsible manner so we're constantly thinking of ways in which technology and particularly AI can help us realize this bright and promising future so we've been thinking how can we apply our advanced computer vision technology for solving some of the very hard incumbent problems in the industrial sectors and how can we make these sectors more efficient and more sustainable so in the next 50 minutes we'll be talking about how with industrial inspection AI that is powered by the auto ml vision technology can help make industrial inspections more easier faster accurate and more importantly more safer and we'll also look at how to leading companies are applying this technology to the energy and manufacturing sector so let's get started so AI hold great promise for solving some real world problems from detecting glaucoma with retinal images to processing millions or even billions of documents to understand their content to automatically moderating unsafe and inappropriate content we are applying this technology across all of these use case but we also recognize that developing this technology building these custom vision models is laborious and it's hard so we wanted to enable even the non programmers to be able to tap into the power of AI and that is precisely why we created Auto ml vision so while our standard ap eyes are a great powerhouse for pre-trained models on the massive google image datasets all ML allows you to train custom models that are specific to your industry needs to your use case needs how do we do that so in a very simple clean UI you are able to upload the images labeled images if you're looking to classify a problem or you can draw bounding boxes at we as we take a look to detect specific objects within those images once you've done that with a click of a button you've got a model trained and that model can be used to detect shark species in this case or you can use that to detect defects anomalies breakage in your specific industrial products we already seeing use cases with wind turbine degradation inspection with outages on solar panel forms or failures on electric poles and we'll be looking into some of these examples in more detail shortly at this point I want to take a moment to talk about data protection and privacy so your data sets your images are your images all of these custom trained models are used only on your use cases by you Google does not pull these images into any common deposit trees or use this across customers so your data sets your images we'll take a look at how this technology can be applied for aerial inspection in wind turbines and then an application of that on the production line in a manufacturing company but before I begin there we want to share Google's stance on the use of this technology so Google cares deeply that it's technology is used for creating a positive impact in the world and in that win Google created air principles in last June they set the standard of the application of these air technologies and we abide by these principles for any work that involves AI and similarly for the use of this technology and for this product we expect that this technology be applied in accordance to the air principle which prohibit explicitly the use of this technology for any nefarious purposes so we'll now take a look at how one of the leading energy companies in the world is applying this technology to create a brighter and greener future for us all let's take a look at global yes wind turbine inspections not the biggest you know several times now we really do have the technology to address the issue of carbon footprint greenhouse gases from the electric sector dey's corporation is one of the leaders in new technologies for renewables and energy storage it's a fortune 500 company our mission is accelerating a safer and greener energy future right now we have eight wind farms each farm has different capacity starting from 50 turbines up to 300 turrets they cover large spans of geography and land they're spread across hilltops and mountain sides all these turbines needs annual inspections originally it could take up to two weeks to do one inspection we partnered with leading drone service company measure right now with drones we can do it in two days and this is safe and quick for a wind turbine inspection we go out with our pilots and what we're looking for is cracks or defects things that may need to be prepared on a typical inspection we're coming back with 30,000 images spending four weeks reviewing images I don't think anyone's gonna argue that that the best use of a highly trained engineers time how do we speed that up and how they make it 10x more efficient that's where machine learning and AI comes in we've built a great and an solution using Google class tools and platform with the auto amount vision tool we've trained it to detect damage we're able to eliminate approximately half of the images from needing human review remaining 50% of their time can now be very focused on identifying that damage and really determining the right course of action to immediate it moving from reviewing images to training machine learning models it's a much higher order employment opportunity for people and one where we're trying to develop on our team Google cloud has been a great partner there technology's consistently among the world leaders and I'm just a great partner to work with person-to-person at the end of the day we won't reach the cleaner energy future without advanced tools like machine learning technology will allow the renewable energy to be cheaper than conventional ownership artificial intelligence robotics this is really where the future is all about please join me in welcoming Nico's born from it yes Thank You Mandy and thank you to the team that put that great video together the power industry is enormous it touches all of our lives and the impacts are felt around the world the industry investments are often quoted in the trillions of dollars the opportunities for improvement are often in the billions if not tens or even hundreds of billions of dollars the industry is also going through significant and profound change renewable energy is continuing to fall dramatically in price solar wind and battery energy storage are not just possible or practical the consumer is also driving change they are much more aware of both the opportunities and the costs associated with their energy use and the third megatrend are the new digital tools cloud AI and many others that are changing the economics of insight I'm here today to share one story where we've partnered with Google to improve lives by accelerating a safer cleaner energy future we call this our vision or aerial intelligence platform first a little bit about myself and the company I work for I'm Nick Osborne I'm the business leader focused on understanding and applying advanced analytic tools like artificial intelligence and machine learning to applied business cases jobs really quite simple I accelerate coordinate and facilitate the adoption of these new tools across the organization AES is a global power company were headquartered in India in the United States but operate in 15 countries around the world we've made a very significant commitment to reduce our carbon intensity by 70% by the year 2030 to help us achieve this we've made some very significant investments in new technologies we're the world leader in battery energy storage using lithium-ion batteries and we're also the largest owner of solar assessing and in the it states on a personal note it feels good to come home at the end of the day and know I'm working with a company that's putting its money where its mouth is to drive that change that is core to our mission applying new technologies is core to how we operate our business our drone program is considered world leading in the end of in the energy industry we developed this program by partnering with measure measure is a professional drone services organization and the measure ground control software is an enterprise caliber drone operations platform through this partnership we've improved the cost safety and performance of our inspections another consideration is that what we often hear about the threat of technology taking jobs or eliminating jobs that's clearly not the case with what we're seeing in our drone program and many other technologies that we're exploring we now have over a hundred and seventy pilots trained in our organization performing operations in over a hundred locations around the world these are employees with tremendous value for our company for their personal advancement and their broader career growth prior to drones these inspections were typically done manually so it was either someone climbing up the turbine and then rappelling down to inspect the blade or hiking around the turbine with a large telephoto lens trying to capture an angle and trying to see if they could detect damage neither of these were as effective or as efficient or as safe as what we're able to do with drones so using drones we're now able to take that partial inspection that was taking two weeks of time and do a full inspection in two days a much lower cost much higher quality and a much safer manner tremendous improvement in efficiency and velocity in our organization but there was one new workflow we're now when we do a single turbine inspection so single turbine has around 300 images when we do an entire field this means we're coming back with 30,000 or even 60,000 images this takes a lot of meticulous and detail review to complete the inspection work so we saw this as a great opportunity for artificial intelligence and this is really where our partnership with Google started to grow to understand our journey towards AI you need to understand with where where we started we started with an investment in talent we sent two classes of six people to Google's advanced to solution lab for intense training and supervised machine learning this cohort became the foundation for our work in AI internally we refer to this decision as a no regrets decision meaning that we were able to quickly move forward make this investment with little or no hesitation on our part a few keys for ROI is one is don't just send IT people to this training a lot of the value from data science in general and this program comes from the mixture of expertise and ideas that you get when you send multiple multiple types of people through the program the second piece of advice and this is maybe a bit selfish on my part is make sure you have a good commitment to work on your projects after this training we only sent high-performing individuals to the training and the risk with sending high-performing individuals is that they're going to get quickly pulled back into their day job and that's definitely something we had to work through as an organization so this investment set the groundwork to accelerate our progress that we were making as a company and is another example of where new technologies are increasing opportunities for our employees so from this foundation we got to work we went through a proof pilot production process with each step being a stage gate for further investment so starting with our proof we built a custom tensor flow model leveraging the openly available inception v3 vision model and it worked we were able to detect damage but it also showed us where our shortcomings were our data needed work and setting up the end-to-end platform was going to be difficult and we were going to need some help so in speaking with Google about our progress and our learnings we discussed the possibility of partnering on a pilot phase so in the pilot phase we are we are were labeling sorry we were using Google's data labeling service and Google's Auto mail vision tool to really accelerate our efforts and boost our efficiency and again it worked false negatives were seen as a key business risk for our organization so not detecting damage is something that we weren't willing to accept in our inspection process so using our most restrictive precision recall metrics during this pilot phase we were able to show that we could eliminate 30% of the images from needing any human review so that four-week review process was now down to three works three weeks really accelerating our velocity and our time to action time to action has really become one of those key metrics that we look at with this project so this gave us the commitment our yeah commitment and ability to move forward with our production environment so our production environment is a scalable platform for us to label images train new models and manage those models in production we're still iterating and refining on this model but we're again showing some very promising results we're now showing that we can eliminate 50% of the images from needing any human review and the remaining 50% of the images are now categorized and classified by type of damage further improving our time to action and focusing our engineers on the most important and most critical types of damage so going back to data one of the things that we had learned about early on was that our data all we had a lot of data that was not at the quality or level of consistency we needed for machine learning so working with measure we developed in nine category classification of damage this includes things like cracks gel coat damage different types of delamination and splitting as well as some non damage categories like serial numbers lightning protection points stickers and whatnot so we also worked with Google's data labeling team to iterate and walk through many many edge cases of different types of damage that are out there we started with a series of batches small in size doing a full and complete review of all the labels that were coming back but as the quality of labeling improved and our batch sizes improved we've moved towards a sample basis we also needed to develop a platform to manage the labeling effort model training prediction process working with Google we identified clear object to be a local GCP partner to help us architect and develop our platform using the latest thinking and cloud and serverless tools available from Google clear object has been a great partner and work to quickly develop this platform for us the platform leverages Auto ml for our core modeling engine cloud storage and cloud SQL for our image repository and metadata as well as cloud functions and app engine for to manage our interactions and orchestrations so now that we have this platform we're continuing to improve on the model or we're also looking to expand its use we're looking at new business cases solar transmission infrastructure and even safety as well as looking at new inspection modalities for example infrared and even lidar we're also looking at pushing the model to the edge or in this case the drone so I'm really excited to hear about what LG is going to be sharing next energy is a trillion dollar business it impacts lives in every day in every country around the world the challenge and the real-world impact are huge if you're interested in working with or for company that is improving lives by accelerating a safer cleaner energy future please come talk to me mandeep [Applause] thank you very much make for that great presentation so we saw how Auto ml version can be used for visual inspections to make them more easy faster accurate and safer in speaking with lot of experts from the industry we learned that there are some specific requirements for manufacturing use case a lot of a time this data sits on premise there's latency requirements and most of the image and data sets are in a format that requires it to be processed on the edge devices this be a mobile phone this be an edge TPU a CPU or a GPU so with our Auto ml version on edge solution you're able to take your custom trained model and then download them in an edge device and you can run those inferences from your edge devices I think you'd much rather see that in action and hear directly from a manufacturing company which has deployed these models on the production line so it's a great pleasure for me to invite mr. soon book leave from LG and share more about this initiative mr. Lee a good afternoon everyone I'm very thank you for your attention to our previous presentation my name is Tom Oakley and the vice-president of AI and picked a business unit at LG's Janice it seems there are many Isis fascists in our audience today I think if you are like me I expect we share many great hopes to apply AI tulear word assertions I also hope this short overview our collaboration with Google Auto ml will help you all in your AI work today we'll be looking at how additional send Coogler has successfully collaborated on hey I immediately commission technologies and how we have been apply our leisure to pigeon inspection systems and several manufacturing solutions let's begin with a little background over jeez Janice I think you may know the name of LG group but you don't know about it what kinds of companies in the energy group so I want to introduce some companies we have LG Electronics which produces the television and refrigerator and we have LG Display a produces world reading or LED panel and the LG Innotech produces a camera model so I think a half of you the have already the LG no text camera in your cell phone oh sorry smartphone and LG Chemical produces electric battery is another and world reading company under age group so you may know that almost all the LG group company is working in the manufacturing industry as LG sentences supplies the IT solutions for the LG group affiliates and other companies the working in the manufacturing industry we are constantly working on how to best apply a high technology to improve the manufacturing processes and we all know that it can be really challenged to use the big data and AI technology to ensure product quality on a largest scale production this is where our discussion of a Google or ml comes in today edition has started working with the Google team l in the summer of last year we started our collaboration after seeing the Google was achieving in their immediate recognition technology because we thought Google or ml could help to improve vision inspection for LG production processes and to our great satisfaction our collaboration has been a success okay before we work with Google ultramel actually we had already developed our own in-house AI system a photos of you familiar with the manufacturing process you will like clear recognize that the picture the left of the screen is the typical visual inspection system that reliance relies on the human operators while many production lines can't have a camera and IOT sensors and other detection technologies but non while the many production lines can use camera but it is still hard to find the rear defect efficiently sometimes non defective product open misjudged as defective because of minor factors like a small dust particles or low resolution images and it is still more effective rely on people to complete visual inspections and while people get better the Ridgid that monotony of a visual inspection made by workers also lead to many errors as well to solve this problem indigenous made a tradition we moved from the traditional visual inspection the left image II you can see to the AI inspection system shown on the right I'm sure many of you also working on the inspection technologies so you will be familiar the Trier and era Mossad we need to improve our system with artificial intelligence anyway within our with our in-house system we increase the accuracy and performance and even improved our process speed and efficiency it means that we could reduce our the operation costs or zone with our in-house AI system we were able to apply to over the three production lines only in age group some of these include the first picture as you can see we could improve the defect detection in LCD and LED panels and under the middle of the picture we could remove in pretties from the optical Trillium and even improving the quality control for the production so about automotive efflux can be made with our in-house AI system but even with this improvement our system wasn't working optimally because it still requires a lot of time and effort to perform well and now I will talk about a little about the downside of this system as it's often the case with the success we also ran into some obstacles as we expanded the application of our AI pigeon inspection into other area we have experienced a shortage of skilled AI developers it is very hard to hire the good AI developers for the company they're located in South Korea so it is very hard times when the one a I developers leave our company the parry impetus is so big to our company so and while we designed the AI model they need to spend a lot of time and effort to achieve high performance additionally as we develop the model using service located at the production site the compressed T of architecture has been increased so it is hard to be served so now we require the process to sentry design and this treat the model to the edgy and to centrally control the the performance of the deployed model in one integrity system collaboration with Google has been a critical to find the solution to these problems the performance of Google ultramel has been truly exciting even though our the AIS person doesn't like it one of the key areas we need to improve in our system was our productivity in terms of the moral development time as you can see in the diagram on the left our top arrow bar shows it took roughly seven days to complete our model before using ultramen but afterwards we brought that down to a mere two two hours with Google Reutimann the other area we need to improve was the accuracy of our system in addition to being faster from the diagram on the right pictured Google automates performance exceeded that of the AI experts in many times our test lizard showed the average is six percent improvement in terms of performance we can expect when using a Google chairman I think while we have made advances using Google or ml and integrating that with our visual inspection we are still facing several challenges in many cases we could not meet our clients requirement and you found that the many of them comes from the low image quality not from the model that we made with the Google so to solve this problem we listen to launch it immediate pre-processing lizards team the members of this team spend more time on exploratory data analysis and pre-processing data and try how to try hard to augment data for getting better machine learning models so they became to spend a lot of time on thinking how to the changing the inspection process itself I estimate now our members could use their time and effort for more strategy work now we are planning to expand our business into consulting services so we will provide expertise to enhanced in to enhance overall inspection processes as a one-stop solution we are hopeful that we will see the first manufacturing visual inspection area where humans and a I share areas the least panzerotti very optimally do you agree ok I would like to announce that we have built integrated AI a vision inspection architecture so our system and Google ultramel is connected seamlessly with this architecture we will be able to maximize humans capability and utilization of Google or ml this architecture starts from the data scientist past the bottom they will ensure a major quality so they will produce a clear image and will send to the Google or travel and Google Tom a text a clear image and produced a a model with efficiency and with effectiveness awesome the Morris will be completely managed with all the history data and performance status and automated learning processes with this architecture the elegiggle is now developed now can developed and managing thousands of a aia morris simultaneously in addition to vision inspection our goal is to expand the architecture to the other the manufacturing use cases to manage the whole factory equipment facilities and the safe things and so on i think you may think however many use cases we can expand this instrument architecture in the manufacturing industry to this point we have gone over how collaboration with google Tramel has improved our visual inspection systems now let's look at a to the future based on our AI integration success with within the edge group we will keep going to be positioned as leading AI visual inspection total service provider so we recover from the pre-processing area and we will cover learning the model and then we will manage all the Morris melt with Google attainment whether the cause of poor inspection quality is motion running attainment over the image quality or data labeling over the operators themselves working with Google ml we will strive to achieve our goal of 99.9 percent accuracy and the leak Lake of 0.001 percent under all conditions if you were experiencing the similar issues in your industry I hope that this session could be helpful I really appreciate your attention and thank you for listening thank you [Applause] Thank You mr. Lee so the goal that mr. Lee shared about LG is very much what we share for our product and for our roadmap as well which is to make our inferences faster our interfaces more intuitive and easier and our results more accurate within manufacturing we seeing many more use cases beyond automotive beyond electronics into the food into retail and many more categories and we are very excited to work on these new use cases with you we saw how a eye and visual inspection can be applied to the manufacturing use cases and we looked at how this can be applied for the aerial inspection use cases beyond the three use cases that we talked about on the aerial inspection side we are also exploring more work on the agriculture monitoring and construction site monitoring as of today this technology is available to use in Bera please visit cloud.google.com slash vision to register your interest you can use the technology right away but by registering at this site we are able to partner with you and work with you on our upcoming releases and our early access program so we look forward to hearing from you thank you so much for joining us in this shared vision and we really look forward to working with you in creating a more brighter more greener and more positive future thank you very much all [Applause]