In May we announced Vertex AI, our new unified AI platform which provides options for everything from using pre-trained models to building your models with a variety of frameworks. In this post we’ll do a deep dive on training and deploying a custom model on Vertex AI. There are many different tools provided in Vertex AI, as you can see in the diagram below. In this scenario we’ll be using the products highlighted in green:

AutoML is a great choice if you don’t want to write your model code yourself, but many organizations have scenarios that require building custom models with open-source ML frameworks like TensorFlow, XGBoost, or PyTorch. In this example, we’ll build a custom TensorFlow model (built upon this tutorial) that predicts the fuel efficiency of a vehicle, using the Auto MPG dataset from Kaggle.

If you’d prefer to dive right in, check out the codelab or watch the two minute video below for a quick overview of our demo scenario: https://www.youtube.com/embed/bHoAXR26hWo?enablejsapi=1&

Environment setup 

There are many options for setting up an environment to run these training and prediction steps. In the lab linked above, we use the IDE in Cloud Shell to build our model training application, and we pass our training code to Vertex AI as a Docker container. You can use whichever IDE you’re most comfortable working with, and if you’d prefer not to containerize your training code, you can create a Python package that runs on one of Vertex AI’s supported pre-built containers.

If you would like to use Pandas or another data science library to do exploratory data analysis, you can use the hosted Jupyter notebooks in Vertex AI as your IDE. For example, here we wanted to inspect the correlation between fuel efficiency and one of our data attributes, cylinders. We used Pandas to plot this relationship directly in our notebook.

To get started, you’ll want to make sure you have a Google Cloud project with the relevant services enabled. You can enable all the products we’ll be using in one command using the gcloud SDK:

  gcloud services enable compute.googleapis.com         \
                       containerregistry.googleapis.com  \
                       aiplatform.googleapis.com

Then create a Cloud Storage bucket to store our saved model assets. With that, you’re ready to start developing your model training code.

Containerizing training code

Here we’ll develop our training code as a Docker container, and deploy that container to Google Container Registry (GCR). To do that, create a directory with a Dockerfile at the root, along with a trainer subdirectory containing a train.py file. This is where you’ll write the bulk of your training code:

To train this model, we’ll build a deep neural network using the Keras Sequential Model API:

  model = keras.Sequential([
  layers.Dense(64, activation='relu', input_shape=[len(train_dataset.keys())]),
  layers.Dense(64, activation='relu'),
  layers.Dense(1)
])

We won’t include the full model training code here, but you can find it in this step of the codelab. Once your training code is complete, you can build and test your container locally. The IMAGE_URI in the snippet below corresponds to the location where you’ll deploy your container image in GCR. Replace $GOOGLE_CLOUD_PROJECT below with the name of your Cloud project:

  IMAGE_URI="gcr.io/$GOOGLE_CLOUD_PROJECT/mpg:v1"
docker build ./ -t $IMAGE_URI
docker run $IMAGE_URI

All that’s left to do is push your container to GCR by running docker push $IMAGE_URI. In the GCR section of your console, you should see your newly deployed container:

Running the training job 

Now you’re ready to train your model. You can select the container you created above in the models section of the platform. You can also specify key details like the training method, compute preferences (GPUs, RAM, etc.) and hyperparameter tuning if required.

Now you can hand over training your model and let Vertex do the heavy lifting for you.

Deploy to endpoint

Next, let’s get your new model incorporated into your app or service. Once your model is done training you will see an option to create a new endpoint. You can test out your endpoint in the console during your development process. Using the client libraries, you can easily create a reference to your endpoint and get a prediction with a single line of code:

  from google.cloud import aiplatform

endpoint = aiplatform.Endpoint(
         endpoint_name="projects/YOUR-PROJECT-NUMBER/locations/us-central1/endpoints/YOUR-ENDPOINT-ID"
)

. . .  

endpoint.predict(test_instances)

Start building today

Ready to start using Vertex AI? We have you covered for all your use cases spanning from simply using pre-trained models to every step of the lifecycle of a custom model. 

• Use Jupyter notebooks for a development experience that combines text, code and data
• Fewer lines of code required for custom modeling
• Use MLOps to manage your data with confidence and scale

Get started today by trying out this codelab yourself or watching this one hour workshop. 

As organizations continue to produce vast quantities of data, they increasingly need platforms that allow them to analyze, store, and extract meaningful insights from that data. And research from analyst firms like Gartner offer an important way for organizations to evaluate and compare cloud data warehouse providers.

Earlier this year, Gartner named Google a Leader in the 2019 Gartner Magic Quadrant for Data Management Solutions for Analytics (DMSA) (report available here). This evaluation covers Google Cloud’s core data analytics offerings, including BigQuery, a serverless, managed data warehouse, Cloud Dataproc, a managed Spark and Hadoop service, and Cloud Dataflow, which enables you to stream and batch-process your data. Here are a few takeaways:

Simplicity and speed

BigQuery’s performance permits complex queries on large-scale data sets to return in seconds, and a substantial number of BigQuery customers maintain data warehouses that store more than 50 terabytes (and a few customers now use more than 100 petabytes). More than half of these customers are loading data either continuously or many times per day. These customers value the ability to extract, transform, load, and analyze their data on a serverless platform, all without maintaining any underlying infrastructure.

A versatile serverless data warehouse

One of BigQuery’s major advantages is its ability to allow customers to address a wide variety of use cases—from a traditional data warehouse to data science. Over the past year, we’ve worked hard to introduce new features in BigQuery like data types for financial and monetary uses, BigQuery GIS for geospatial data, and machine learning capabilities through BigQuery ML. BigQuery’s continuous ingest capabilities make it suitable for an operational—or a real-time—serverless data warehouse.

An expanding ecosystem

With overall increased market adoption, our analytics offerings continue to benefit from a fast-growing partner ecosystem of service providers, and business intelligence (BI) and data integration vendors. In particular, in 2018 we expanded our partnerships with established industry providers, including Confluent, Dell Boomi, Informatica, Looker, Reltio, Tableau, and ThoughtSpot.

More and more organizations are finding value in Google Cloud’s serverless data warehouse and analytics offerings. If you’d like to learn more, you can download a complimentary copy of the Gartner Magic Quadrant for Data Management Solutions for Analytics on the Google site (requires an email address).

_{Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.}

In the last year, there’s been an upward trend in cloud adoption in India. In fact, NASSCOM finds that cloud spending in India is estimated to grow at 30% per annum to cross the US$7 billion mark by 2022.

At Google, in our conversations with customers, discussions have evolved beyond cost savings and efficiencies. While those are still very relevant reasons for adopting cloud technologies, Indian enterprises are looking to Google Cloud to help them drive digital transformation, identify new revenue generating business models, reach previously untapped consumer markets, and build customer loyalty through greater insight and personalization.

Here are some companies and their stories.

Tata Steel: Mining data and maximizing its power

Tata Steel is a great example of an established enterprise from a traditional industry that is modernizing and embracing cloud computing. With an ambition to be a leader in manufacturing in India and a digital-first organization by 2022, Tata Steel believes smart analytics is key to enhancing operational efficiency and gaining business advantage. 

To organize data from siloed systems across the organization and make it easily accessible to all employees, Tata Steel is using Cloud Search and plans to scale it to more than one million documents and 28 disparate enterprise content sources including enterprise resource planning (ERP) and SharePoint. In fact, Tata Steel is one of the first Indian enterprises to harness the power of Cloud Search to meet some of the most aggressive ingestion demands, with indexing durations reduced from weeks to seconds.

They are also leveraging Google Cloud Platform (GCP) services like Google Cloud Storage and BigQuery to build their data lake and enterprise data warehouse so they can take advantage of advanced analytics and machine learning. Managed services such as AI Platform further enable Tata Steel to manage end-to-end AI/ML workflows within the GCP console. This complements their existing on-premise reporting and analytics tools, and brings data management to the forefront of everything they do—from forecasting market demand to predictive equipment maintenance.

“Digital is not just a goal, it’s become a way of life. We are digitizing everything from the deployment of factory vehicles to improving material throughput to marketing and sales. As a result, we have petabytes of structured and unstructured data that is not only waiting to be mined, but that we can generate intelligence from to create opportunities across our multiple lines of business using GCP,” said Sarajit Jha, Chief Business Transformation & Digital Solutions at Tata Steel.

Helping L&T Financial Services reach customers in rural communities

In rural communities, quick access to financial services can make a tremendous difference to livelihoods. L&T Financial Services provides farm-equipment finance, micro loans and two-wheeler finance to consumers across rural India backed by a strong digital and analytics platform. Their digital-loan approval app, which runs on GCP, makes it significantly faster and easier for people to apply for financial assistance to purchase important things such as farming equipment and two-wheelers. It also helps rural women entrepreneurs get quicker access to funds for their businesses through micro loans.

L&T Financial found G Suite to be a far better collaborative tool to help staff work together efficiently. Employees can interact with each other in real time using Hangouts Meet, and the task of information sharing is more seamless and secure through Drive. BigQuery also helps L&T Financial Services generate behavior scorecards to track credit quality of its micro-loan customers.

“Cloud is the technology that enables us to achieve scale and reach. Today there are countless data points available about rural consumers which enable us to personalize our products to serve them better. With access to faster compute power, we can also on-board consumers more efficiently. Our rural businesses have clocked a disbursement CAGR of 60% over the past three years.” said Sunil Prabhune, Chief Executive-Rural Finance, and Group Head-Digital, IT and Analytics, L&T Financial Services.

Creating conversational connections for Digitate’s customers

Digitate, a venture of TCS (Tata Consultancy Services), has integrated Dialogflow into its flagship brand ignio, an award-winning artificial intelligence platform for driving IT operations, workload operations and ERP operations for diverse enterprises. This integration is the next step in ignio’s product development journey, and will enable users to chat or talk with ignio to detect issues, triage problems, resolve them and even predict system behavior.

“ignio combines its unique self-healing AIOps capabilities for enterprise IT and business operations with Dialogflow’s AI/ML-based, easy to use, natural and rich conversational capabilities to create an unparalleled, intuitive and feature-rich experience for our customers,” says Akhilesh Tripathi, Head of Digitate.

Indian enterprises going G Suite

The base of Indian enterprises that are making the switch to G Suite to streamline their productivity and collaboration also continues to grow. Sharechat, BookMyShow, Hero MotorCorp, DB Corp and Royal Enfield are now able to move faster within their organizations, using intelligent, cloud-based apps to transform the way they work.

A hybrid and multi-cloud future in India

IDC predicts that by 2023, 55% of India 500 organizations will have a multi-cloud management strategy that includes integrated tools across public and private clouds. (IDC FutureScape: Worldwide Cloud 2019 Predictions  — India Implications (# AP43922319). We look forward to sharing more success stories of Indian enterprises that have taken the next step in their digital transformation journey.

In 2013, long before the world was discussing clean energy and sustainable practices, two IIT Madras graduates — Swapnil Jain and Tarun Mehta — had an idea to develop India’s first-ever electrical scooter.

This was at a time when auto manufacturers were still focusing on fossil-fuel-driven vehicles and ‘eco-friendly’ mobility solutions were more a trendy alternative catering to a niche market.

The duo founded Ather Energy in 2013 and launched their first fully-electric scooter, the Ather S340, in Bengaluru in 2016. Since then, the company has released several new models into the market and is planning to expand to eight more cities by the end of the year.

To support the smooth running of their vehicles, lower costs, improve time to market, and create great customer experience, Ather turned to Google Cloud.

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