Innovate Faster & More Flexibly: How Our Commitment to Open Source Unlocks AI and ML Innovation
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At Google, we believe anyone should be able to quickly and easily turn their artificial intelligence (AI) idea into reality. Open source software (OSS) has become increasingly important to this goal, heavily influencing the pace of innovation in AI and machine learning (ML) ecosystems. Over the last two decades, ML has transformed Google services including Search, YouTube, Assistant, and Maps, and the basis for this transformation has always been our “open first” approach through investments in projects and ecosystems like TensorFlow, Jax, and PyTorch.
These OSS efforts are important because many AI technologies rely on closed or exclusive approaches. This wall-garden approach creates high barriers to entry for developers; limits efforts to make AI explainable, ethical, equitable; and stunts innovation. We’re committed to open ecosystems, as we firmly believe no one company should own AI/ML innovation. In this blog post, we’ll explore some of Google’s most significant OSS AI and ML contributions from recent years, as well as how our commitment to open technologies can help organizations innovate faster and more flexibly.
Openness is the way to operate as an ecosystem, not a single project
Google’s OSS initiatives extend and enable AI initiatives according to three pillars:
- Access — OSS allows developers, researchers and organizations of all sizes to leverage the latest ML technology. It is a key part of democratizing innovation in ML, fostering software diversity and choice for customers, and lowering operating cost while accelerating scale for everyone.
- Transparency — Open source datasets, ML algorithms, training models, frameworks, and compilers ensure due diligence and validation by the larger community. This is paramount when it comes to ML as it bolsters reproducibility, interpretability, ensures equity, and boosts security.
- Innovation — With more access and transparency, more innovation comes naturally. Our customers and partners take advantage of open source ML toolsets and frameworks to create more innovation in the field by contributing their own OSS.
Google’s ongoing commitment to open source AI
Google’s commitment to open standards spans over two decades of OSS contributions like TensorFlow, JAX, TFX, MLIR, KubeFlow, and Kubernetes, as well as sponsorship for critical OSS data science initiatives like Project Jupyter and NumFOCUS. Initiatives like these have helped Google become the leading Cloud Native Computing Foundation (CNCF) contributor—and by building on these efforts, Google Cloud seeks to be the best platform for the OSS AI community and ecosystem.
The perils of closed technologies can emerge at many points across ML pipelines, which is why Google’s OSS strategy encompasses the entire “idea-to-production” lifecycle, from acquiring data, to training models, to managing infrastructure, to facilitating experimentation and model refinement:
Data acquisition: starting the journey from idea to production-ready ML model
The journey from an idea to a production ML model starts with data. TensorFlow Datasets not only help users acquire ready-to-use, customizable, and highly-optimized datasets (including image, audio, and text), but also provides a set of helpful APIs that make it easy for users to organize their own datasets, regardless of whether they build with TensorFlow, Jax, or other ML frameworks.
Model development and training: shortening the path from data to useful ML
OSS libraries help developers and researchers design, implement, train, test, and debug ML algorithms. Our contributors on this front include:
- The TensorFlow core framework, which offers APIs to help data scientists and developers build and train production-grade ML models on distributed and accelerated infrastructure powered by GPUs or TPUs;
- Google’s founding membership of the PyTorch Foundation, which positions us to increase adoption of ML by building an ecosystem of open-source projects with PyTorch;
- Keras, a simple and powerful ML framework, well integrated with TensorFLow, that makes it easy for developers to quickly build and train ML models, or to leverage pre-trained AI applications;
- Model Garden, which provides implementations of many state-of-the-art computer vision and natural language processing models, maintained by Google and accessible to all, alongside APIs to accelerate training and experiments;
- Jax, a lean, intuitive, and composable system that brings together automatic differentiation (Autograd) and the Accelerated Linear Algebra (XLA) optimizing compiler to offer high-performance ML for fast research and production;
- TensorFlow Hub, a repository of trained ML models ready for fine-tuning and deployment; and,
- MediaPipe open source cross-platform, which lets users leverage customizable ML solutions for live and streaming media, including text and video.
ML infrastructure management: scaling valuable models with powerful backends
Accessing and managing infrastructure for ML, especially at scale, can be a blocker for many organizations, which is why Google has invested in initiatives including:
- The TFX (or TensorFlow Extended) platform, which offers software frameworks and tooling for full MLOps deployments, helping developers with data automation, model tracking, performance monitoring, and model retraining;
- Kubeflow, which makes deployments of ML workflows on Kubernetes simple, portable and scalable; and,
- TRC (TPU Research Cloud), which gives access to a cluster of more than 1,000 Cloud TPU devices at no charge to selected researchers who publish peer-reviewed papers and/or open source code.
Experimentation and model optimization: encouraging discovery and iteration
Data, tools for model training, and infrastructure can achieve only so much without strong processes for experimentation and optimization—which is why we’ve contribution to projects like xManager, which enables anyone to run and keep track of ML experiments locally or on Vertex AI and Tensorboard, which simplifies tracking and visualizing of model performance metrics.
These areas of focus will help not only our customers but the open-source AI community as a whole, and we’re excited to share more OSS news in coming days and months. To start exploring why many organizations choose Google Cloud for their open-source AI needs, visit our “open cloud” page and be sure to register for Google Cloud Next ‘22 for all our latest news.
Thanks to all the contributors to this blog post: Matt Vasey, George Elissaios, Warren Barkley, Manvinder Singh, James Rubin, Abhishek Ratna, Thea Lamkin, Amin Vahdat, Andrew Moore, Max Sapozhnikov, Gandhi, Vikram Kasivajhula
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The Secret to Accelerated ML Model Training
As an infrastructure or a data science professional, it’s more critical than ever to keep abreast of the changes taking place to the infrastructure powering machine learning.
If we take a step back, we will realize that there’s been a tremendous amount of progress in machine learning in the last few years, resulting in multiple benefits including higher accuracy in speech and image recognition, for instance. This progress is in part due to the advances in the systems to train models.
One of these advances comes in the type of processors being leveraged to power ML. There’s been an evolution—from CPUs to GPUs to TPUs—each of which bring additional computational power.
Google Cloud TPUs allow data scientists to train their models at lightning speed on Google Cloud’s latest ML hardware.
In this video, you will see how to use your raw data to create different ML applications. More importantly, Zak Stone, Product Manager For TensorFlow and Cloud TPUs, Google Brain, will show you how to accelerate ML model training on TPUs using Google Cloud’s optimized, TensorFlow image classification code. Each step will be explained in detail.
redBus: Mastering Big Data with Google BigQuery
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In 2006, online travel agency redBus introduced internet bus ticketing in India, unifying tens of thousands of bus schedules into a single booking operation. (Think of it as Expedia for bus booking.) Using BigQuery, redBus crunches terabytes of booking and inventory data in mere seconds and at a fraction of the cost of other big data services. BigQuery also helps engineers fix glitches quickly, minimize lost sales, and improve customer service.
Challenge
Executives at the Bangalore-based redBus needed a powerful tool to analyze booking and inventory data across their system of hundreds of bus operators serving more than 10,000 routes. They considered using clusters of Hadoop servers to process the data but decided the system would take too much time to set up and would require a specialized staff to maintain it. It also would not provide the lightning-fast analysis they needed.
“It would have taken at least a couple of hours to analyze anything,” says Pradeep Kumar, a technical architect at redBus. “Crunching very large data sets would have been a day’s job. We needed something more powerful to get the real-time analysis we were looking for.”
Solution
Kumar and his colleagues learned about BigQuery and realized it was the right match for their data processing needs. The web-based service, which enables companies to analyze massive datasets using Google’s data processing infrastructure, is easy to set up and manage since its simple, SQL-like query language doesn’t require complex technology or specialized personnel. It also has low overhead costs.
The redBus team uses BigQuery as part of an intricate data collection and analysis process. Applications hosted on a range of servers continually pump information related to customer searches, seat inventory, and bookings into a centralized data collection system. Engineers upload the data to BigQuery, which provides answers to complex queries within seconds. For example, BigQuery helps redBus staff:
- Learn how many times customers searched for seats and found none or very few available, indicating more seats should be added to a route
- Investigate decreases in bookings and notify engineers if a technical problem is the cause
- Identify server problems by quickly analyzing data related to server activity
Results
BigQuery provides near real-time data analysis capabilities at 20% of the cost of maintaining a complex Hadoop infrastructure. Queries that would have required a day to analyze on a Hadoop framework take less than 30 seconds using Google’s web-based service.
“We explored several data analytics solutions. Nothing comes remotely close to the sheer power of Google BigQuery,” Kumar says. “It made large-scale data collection and crunching possible with little effort, which has translated to a significant business advantage.”
Google Cloud Platform results
- Analyzes data sets as large as 2 terabytes in less than 30 seconds using a simple, SQL-like language
- Saves time analyzing technical problems and customer booking trends
- Spends 80% less than they would have on a Hadoop infrastructure and avoids setting up and maintaining a complex infrastructure in-house
- Strengthens the company by improving customer service and engineering quality
The fast insights gained through BigQuery are also making redBus a stronger company. By minimizing the time it takes staff members to solve technical problems, BigQuery has helped improve customer service and reduce lost sales.
“Getting to the root of problems used to be really time-consuming,” Kumar says. “By the time we figured it out, customers might have given up. Now if there are booking problems, BigQuery helps us understand the reason right away. Choosing Google BigQuery was the right decision for our company.”
AI-powered Business Messages for Timely, Engaging and Helpful Conversations with Customers
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Over the last two years, we’ve seen a significant uptick in the number of people using messaging to connect with businesses. Whether it was checking hours of operation, verifying what was in stock, or scheduling a pick-up, the pandemic caused a significant shift in consumer behavior.
- 45% of users are spending more time on messaging services because of the pandemic
- 85% of consumers would like to message brands directly
With the rise in demand for messaging, consumers expect communication with businesses to be speedy, simple, and convenient. For businesses, keeping up with customer inquiries can be a labor-intensive process, and offering 24/7 support outside of store hours can be costly.
To help businesses seamlessly deliver helpful, timely, and engaging conversations with customers when and where they need help, we introduced AI-powered Business Messages.
https://youtube.com/watch?v=fcgP3RHjBLY%3Fenablejsapi%3D1%26
What is AI-powered Business Messages?
With AI-powered Business Messages, you can connect with your customers in their moment of need, in the places they’re looking for answers—such as Google Search, Google Maps, or any brand-owned channel. For instance, check out how Walmart customers in the US are able to receive real-time information on product availability, straight from a search results page.
People turn to Google when they are searching for answers to their questions, looking to buy something, or trying to accomplish a particular task with one of our many tools. In fact, 68% of all online experiences begin with a search engine.
At Google, we know how important it is for interactions with a brand to be personalized, helpful, and simple. With AI-powered Business Messages, customers are able to chat with virtual agents that understand, interact, and respond in natural ways.
We are also combining smart automation with the ability for customers to chat with live agents when it’s really needed. This approach saves your customers precious time, while also saving you money. And with Business Messages automatically handling many customer inquiries in the background, businesses have the option to distribute their human customer service agents to address other needs.
Getting started with conversational AI is easy with Bot-in-a-Box
We know it can be difficult to get started with AI. That’s why we are utilizing existing Google AI tools like Dialogflow—part of Google Cloud Contact Center AI—to create the capability within Google’s Business Messages called Bot-in-a-Box, which makes getting started with Conversational AI easy. Bot-in-a-Box allows for fast and effective adoption of automation for businesses of all sizes.
Enabling Business Messages with Bot-in-a-Box can be as simple as leveraging an existing customer FAQ document you already have, whether it’s from a web page or an internal document. And since the conversational AI is powered by Business Messages and Dialogflow working together, your chat bot is able to understand and respond to customer questions automatically without the need to write code.
Bot-in-a-Box also supports other critical journeys like “Custom Intents.” That means that your bot is able to understand the different ways customers express a similar question and respond accurately by using machine learning capabilities.
Finding success
In April 2021, Wake County courthouses in North Carolina partnered with Tango Technology to implement Business Messages when it became apparent that being able to provide the public and attorneys with around-the-clock access to information would significantly reduce the pressure on courthouse staff. Using Bot-in-a-Box, Tango Technology was able to customize a solution for Wake County Courthouse, Justice Center, and Clerk of Superior Court in just four days.
“With the combination of Google’s Business Messages, GCP, and Dialogflow, we were able to spin up an AI-driven bot for the courts in days. And the technology stack allows us to continually improve by adding functionality in an agile process.”— Mike Lotz, Co-Founder, Tango Technology
With Business Messages, North Carolina courthouses saw a 37% decrease in the call volume handled by courthouse staff. With 398,298 fewer phone calls during the first year of operation, the AI-based messages helped Wake County Courthouse work more efficiently and productively.
We’ve seen many brands benefiting from AI-powered Business Messages. For instance, Levi’s saw a 30% increase in off-hours shoppers and surpassed 85% customer satisfaction scores after implementing Business Messages. They also drove 30x more store-related questions than Levi’s website chat.
Bring Google’s conversational AI to your storefront with Business Messages
Google’s Business Messages makes it easier for businesses of all sizes to engage their existing or potential customers in a virtual conversation, when and where they need it.
To learn more, watch our Cloud Next session here or visit us at g.co/businessmessages. We have specialized services to help you get started and can share the wisdom of our channel partners and dedicated experts who specialize in unleashing the potential of conversational AI.
Google’s Intelligent Products Essentials Assist Manufacturers in Product Development Journey
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Expectations for both consumer and commercial products have changed. Consumers want products that evolve with their needs, adapt to their preferences, and stay up-to-date over time. Manufacturers, in turn, need to create products that provide engaging customer experiences not only to better compete in the marketplace, but also to provide new monetization opportunities.
However, embedding intelligence into new and existing products is challenging. Updating hardware is costly, and existing connected products do not have the capability to add new features. Furthermore, manufacturers do not have sufficient customer insights due to product telemetry and customer data silos, and may lack the AI expertise to quickly develop and deploy these features.
That’s why today we’re launching Intelligent Products Essentials, a solution that allows manufacturers to rapidly deliver products that adapt to their owners, update features over-the-air using AI at the edge, and provide customer insights using analytics in the cloud. The solution is designed to assist manufacturers in their product development journeys—whether developing a new product or enhancing existing ones.
With Intelligent Products Essentials, manufacturers can:
- Personalize customer experiences: Provide a compelling ownership experience that evolves over the lifetime of the product. For example, a chatbot that contextualizes responses based on product status and customer profile.
- Manage and update products over-the-air: Deploy updates to products in the field, gather performance insights and evolve capabilities over time with monetization opportunities.
- Predict parts and service issues: Detect operating thresholds, anomalies and predict failures to proactively recommend service using AI, reducing warranty claims, decreasing parts shortages and increasing customer satisfaction.
In order to help manufacturers quickly deploy these use cases and many more, Intelligent Products Essentials provides the following:
- Edge connections: Connect and ingest raw or time-series product telemetry from various device platforms utilizing IoT Core or Pub/Sub and enable deployment and management of firmware over-the-air and machine learning models with Vertex AI at the edge.
- Ownership App Template: Easily build connected product companion apps that work on smartphones, tablets, and computers. Use a pre-built API and accompanying sample app that can incorporate product or device registration, identity management, and provide application behavior analytics using Firebase.
- Product fleet management: Manage, update and analyze fleets of connected products via APIs, Google Kubernetes Engine, and Looker.
- AI services: Create new features or capabilities for your products using AI and machine learning products such as DialogFlow, Vision AI, AutoML, all from Vertex AI.
Enterprise data integration: Integrate data sources such as Enterprise Asset Management (EAM), Enterprise Resource Planning (ERP), Customer Relationship Management (CRM) systems and others using Dataflow and BigQuery.
Intelligent Products Essentials helps manufacturers build new features across consumer, industrial, enterprise, and transportation products. Manufacturers can implement the solution in-house, or work with one of our certified solution integration partners like Quantifi and Softserve.
“The focus on intelligent products that Google Cloud is deploying provides a digital option for manufacturers and users. At its heart, systems like Intelligent Product Essentials are all about decision making. IDC sees faster and more effective decision-making as the fundamental reason for the drive to digitize products and processes. It’s how you can make faster and more effective decisions to meet heightened customer expectations, generate faster cash flow, and better revenue realization,” said Kevin Prouty, Group Vice President at IDC. “Digital offerings like Google’s Intelligent Product Essentials potentially go the last mile with the ability to connect the digital thread all the way through to the final user.”
Customers adopting Intelligent Products Essentials
GE Appliances, a Haier company, are enhancing their appliances using new AI-powered intelligent features to enable:
- Intelligent cooking: Help cook the perfect meal to personal preferences, regardless of your expertise and abilities in the kitchen.
- Frictionless service: Build smart appliances that know when they need maintenance and make it simple to take action or schedule services.
- Integrated digital lifestyle: Make appliances useful at every step of the way by integrating them with digital lifestyle services – for example, automating appliance behaviors according to customer calendars, such as oven preheating or scheduling the dishwasher to run in the late evening.
“Intelligent Products Essentials enhances our smart appliances ecosystem, offering richer consumer habit insights. This enables us to develop and offer new features and experiences to integrate with their digital lifestyle.“ —Shawn Stover, Vice-president Smart Home Solutions at GE Appliances.https://www.youtube.com/embed/zaWAJN8aKOw?enablejsapi=1&
Serial 1, Powered by Harley-Davidson, is using Intelligent Product Essentials to manage and update its next generation eBicycles, and personalize its customers’ digital ownership experiences.
“At Serial 1, we are dedicated to creating the easiest and most intuitive way to experience the fun, freedom, and adventure of riding a pedal-assist electric bicycle. Connectivity is a key component of delivering that mission, and working together to integrate Intelligent Product Essentials into our eBicycles will ensure that our customers enjoy the best possible user experience.”— Jason Huntsman, President, Serial 1.
Magic Leap, an augmented reality pioneer with industry-leading hardware and software, is building field service solutions with Intelligent Products Essentials with the goal of connecting manufacturers, dealers, and customers to more proactive and intelligent service.
“We look forward to using Intelligent Products Essentials to enable us to rapidly integrate manufacturers’ product data with dealer service partners into our field service solution. We’re excited to partner with Google Cloud as we continue to push the boundaries of physical interaction with the digital world.” — Walter Delph, Chief Business Officer, Magic Leap
Intelligent Product Essentials is available today. To learn more, visit our website.
This Chart, from Home Depot, Dramatically Demonstrates the Power of a Cloud Data Warehouse
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The Home Depot (THD) is the world’s largest home-improvement chain, growing to more than 2,200 stores and 700,000 products in four decades. Much of that success was driven through the analysis of data. This included developing sales forecasts, replenishing inventory through the supply chain network, and providing timely performance scorecards.
However, to compete in today’s business world, THD has taken this data-driven approach to an entirely new level of success on Google Cloud, providing capabilities not practical on legacy technologies.
The pressures of contemporary growth that drove much of the work are familiar to many businesses. In addition to everything it was doing, THD needed to better integrate the complexities in its related businesses, like tool rental and home services. It needed to better empower teams, including a fast-growing data analysis staff and store associates with mobile computing devices. It wanted to better use online commerce and artificial intelligence to meet customer needs, while maintaining better security.
Even before addressing these new challenges, THD’s existing on-premises data warehouse was under stress as more data was required for analytics and data analysts were utilizing the data with increasingly complex use cases. This drove rapid growth of the data warehouse, but also created constant challenges for the team in managing priorities, performance, and cost.
In order to add capacity to the environment, it was a major planning, architecture, and testing effort. In one case, adding on-premises capacity took six months of planning and a three-day service outage. Within a year, capacity was again scarce, impacting performance and ability to execute all the reporting and analytics workloads required. The capacity refresh cycles were shrinking, and the expecations for data were growing. There had to be a better way.
Still, THD did not take its move to the cloud lightly. A large-scale enterprise data warehouse migration involves tremendous effort among people, process, and technology. After careful consideration, THD chose Google Cloud’s BigQuery for its cloud enterprise data warehouse.
BigQuery, a scalable serverless data warehouse, was better on cost, infrastructure agility, and analytics capability, driving better insights with improved performance. There are no service interruptions when capacity is added, and that capacity can be added within a week (and soon same day). It doesn’t require complex system administration, and its standard SQL support means people can easily ramp up quickly. Valuable BigQuery products like Identity and Access Management meant THD could create many separate Google Cloud projects, while ensuring that different teams weren’t interfering with each other or accessing protected data.
THD also utilizes BigQuery’s flat-rate monthly pricing model that allows teams to budget their capacity based on need and provides billing predictability. The capacity not being used by a given project is available for enterprise use. This ensures no surprises when the monthly bill arrives and provides all analytical users access to significant computing power.
While THD’s legacy data warehouse contained 450 terabytes of data, the BigQuery enterprise data warehouse has over 15 petabytes. That means better decision-making by utilizing new datasets like website clickstream data and by analyzing additional years of data.
As for performance, look at this chart:
With the cloud EDW migration complete, and the legacy on-premises data warehouse retired, analysts now execute more complex and demanding workloads that they would not have been able to complete before, such as utilizing Datalab for orchestrating analytics through Python Notebooks, utilizing BigQuery ML for machine learning directly against the BigQuery data (no movement of large datasets), and AutoML to help determine the best model for predictions.
Additionally, engineers at THD have adapted BigQuery to monitor, analyze, and act on application performance data across all its stores and warehouses in real time, something that was not practical in the on-premises system.
With over 600 projects that THD now has on Google Cloud, the BigQuery story is just one of the many ways that Google Cloud is working with THD to deliver meaningful business results, every day.
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