Without AI, you’re not getting the most out of your data.
Without data, you risk stale, out-of-date, suboptimal models.

But most companies are still struggling with how to keep these highly interdependent technologies in sync and operationalize AI to take meaningful action from data.

We’ve learned from Google’s years of experience in AI development how to make data-to-AI workflows as cohesive as possible and as a result our data cloud is the most complete and unified data and AI solution provider in the market. By bridging data and AI, data analysts can take advantage of user-friendly, accessible ML tools, and data scientists can get the most out of their organization’s data. All of this comes together with built-in MLOps to ensure all AI work — across teams — is ready for production use.

In this blog we’ll show you how all of this works, including exciting announcements from the Data Cloud Summit:

Vertex AI Workbench is now GA bringing together Google Cloud’s data and ML systems into a single interface so that teams have a common toolset across data analytics, data science, and machine learning. With native integrations across BigQuery, Spark, Dataproc, and Dataplex data scientists can build, train and deploy ML models 5X faster than traditional notebooks.

Introducing Vertex AI Model Registry, a central repository to manage and govern the lifecycle of your ML models. Designed to work with any type of model and deployment target, including BigQuery ML, Vertex AI Model Registry makes it easy to manage and deploy models.

Use ML to get the most out of your data, no matter the format
Analyzing structured data in a data warehouse, like using SQL in BigQuery, is the bread and butter for many data analysts. Once you have data in a database, you can see trends, generate reports, and get a better sense of your business. Unfortunately, a lot of useful business data isn’t in the tidy tabular format of rows and columns. It’s often spread out over multiple locations and in different formats, frequently as so-called “unstructured data” — images, videos, audio transcripts, PDFs — can be cumbersome and difficult to work with.

Here, AI can help. ML models can be used to transcribe audio and videos, analyze language, and extract text from images—that is, to translate elements of unstructured data into a form that can be stored and queried in a database like BigQuery. Google Cloud’s Document AI platform, for example, uses ML to understand documents like forms and contracts. Below, you can see how this platform is able to intelligently extract structured text data from an unstructured document like a resume. Once this data is extracted, it can be stored in a data warehouse like BigQuery.

Bring machine learning to data analysts via familiar tools

Today, one of the biggest barriers to ML is that the tools and frameworks needed to do ML are new and unfamiliar. But this doesn’t have to be the case. BigQuery ML, for example, allows you to train sophisticated ML models at scale using SQL code, directly from within BigQuery. Bringing ML to your data warehouse alleviates the complexities of setting up additional infrastructure and writing model code. Anyone who can write SQL code can train a ML model quickly and easily.

Easily access data with a unified notebook interface

One of the most popular ML interfaces today are notebooks: interactive environments that allow you to write code, visualize and pre-process data, train models, and a whole lot more. Data scientists often spend most of their day building models within notebook environments. It’s crucial, then, that notebook environments have access to all of the data that makes your organization run, including tools that make that data easy to work with.

Vertex AI Workbench, now generally available, is the single development environment for the entire data science workflow. Integrations across Google Cloud’s data portfolio allow you to natively analyze your data without switching between services:

Cloud Storage: access unstructured data

BigQuery: access data with SQL, take advantage of models trained with BigQuery ML

Dataproc: execute your notebook using your Dataproc cluster for control

Spark: transform and prepare data with autoscaling serverless Spark

Below, you’ll see how you can easily run a SQL query on BigQuery data with Vertex AI Workbench.

But what happens after you’ve trained the model? How can both data analysts and data scientists make sure their models can be utilized by application developers and maintained over time?

Go from prototyping to production with MLOps

While training accurate models is important, getting those models to be scalable, resilient, and accurate in production is its own art, known as MLOps. MLOps allow you to:

• Know what data your models are trained on
• Monitor models in production
• Make training process repeatable
• Serve and scale model predictions
• A whole lot more! (See the “Practitioners Guide to MLOps” whitepaper for a full and detailed overview of MLOps)

Built-in MLOps tools within Vertex AI’s unified platform remove the complexity of model maintenance. Practical tools can help with everything from training and hosting ML models, managing model metadata, governance, model monitoring, and running pipelines – all critical aspects of running ML in production and at scale.

And now, we’re extending our capabilities to make MLOps accessible to anyone working with ML in your organization.

Easy handoff to MLOps with Vertex AI Model Registry

Today, we’re announcing Vertex AI Model Registry, a central repository that allows you to register, organize, track, and version trained ML models and is designed to work with any type of model and deployment target, whether that’s through BigQuery, Vertex AI, AutoML, custom deployments on GCP or even out of the cloud.

Vertex AI Model Registry is particularly beneficial for BigQuery ML. While BigQuery ML brings the powerful scalability of BigQuery for batch predictions, using a data warehouse engine for real-time predictions just isn’t practical. Furthermore, you might start to wonder how to orchestrate your ML workflows based in BigQuery. You can now discover and manage BigQuery ML models and easily deploy those models to Vertex AI for real-time predictions and MLOps tools.

End-to-End MLOps with pipelines

One of the most popular approaches to MLOps is the concept of ML pipelines: where each distinct step in your ML workflow from data preparation to model training and deployment are automated for sharing and reliably reproducing.

Vertex AI Pipelines is a serverless tool for orchestrating ML tasks using pre-built components or your own custom code. Now, you can easily process data and train models with BigQuery, BigQuery ML, and Dataproc directly within a pipeline. With this capability, you can combine familiar ML development within BigQuery and Dataproc into reproducible, resilient pipelines and orchestrate your ML workflows faster than ever.

See an example of how this works with the new BigQuery and BigQuery ML components.

Learn more and get started

We’re excited to share more about our unified data and AI offering today at the Data Cloud Summit. Please join us for the spotlight session on our “AI/ML strategy and product roadmap” or the “AI/ML notebooks ‘how to’ session.”

And if you’re ready to get hands on with Vertex AI, check out these resources:

Codelab: Training an AutoML model in Vertex AI

Codelab: Intro to Vertex AI Workbench

Video Series: AI Simplified: Vertex AI

GitHub: Example Notebooks

Training: Vertex AI: Qwik Start

For years, retailers have been told that they must embrace a litany of new technologies, trends, and imperatives like online shopping, mobile apps, omnichannel, and digital transformation. In search of growth and stability, retailers adopted many of these, only to realize that for every box they ticked, there was another one waiting.

And then the pandemic hit, along with rising social movements and increasingly harsh weather. Some retailers were more prepared to take on these disruptions than others, which crystallized a new universal truth across the industry: the ability to adapt on the fly became the most important trait to survive and thrive.

Today’s retail landscape has surfaced both existing and new challenges for specialty and department store retailers. Approximately 88% of purchases previously occurred within a store environment. Now, it’s closer to 59%, with the remainder done online or through other omni methods. 

With such constant change and upheaval, it can feel like the mantra now is ABP: always be pivoting. 

The big question isn’t just how to maintain constant momentum and agility—it’s how to do it without sapping your workforce, your inventory, or your profits in the process. The pivot is now a given. What matters is how you do it.

Adapting requires a flexible base of technology that allows retailers to shift and scale seamlessly with the needs of the moment. 

They need to be able to leverage real-time insights and enhance customer experiences rapidly, online and in the real world (not to mention the growing hybridization that’s AR and VR). They need to modernize their stores to power engaging consumer and associate experiences. They need to enhance operations for rapid scaling between full operations and digital-only offerings.

To help retailers achieve these goals and more, Google Cloud is honing a trio of essential innovations: demand forecasting that harnesses the power of data analytics and artificial intelligence; enhanced product discovery to improve conversion across channels; and the tools to help create the modern store experience.

In other words, here’s some of the biggest ways we’re ready to help you pivot.

Pivot point 1: Harnessing data and AI for demand forecasting with Vertex AI

One of the greatest challenges for retailers when building organizational flexibility is managing inventory and the supply chain. 

We are in the midst of one of the worst global supply chain crises, stemming from soaring demand and logistics issues brought on by the pandemic. This crisis has only heightened the challenge retailers face when assessing demand and product availability. Even in normal times, mismanagement of inventory can add up to a trillion-dollar problem, according to IHL Group (costing $634 billion in lost sales worldwide each year, while overstocks result in $472 billion in lost revenues due to markdowns). 

On the flipside, optimizing your supply chain can lead to greater profits. For instance, McKinsey predicts that a 10% to 20% improvement in retail supply chain forecasting accuracy is likely to produce a 5% reduction in inventory costs and a 2% to 3% increase in revenues.

Some of the challenges related to demand forecasting include:

• Low accuracy leads to excess inventory, missed sales, and pressure on fragile supply chains.
• Real drivers of product demand are not included, because large datasets are hard to model using traditional methods.
• Poor accuracy for new product launches and products that have sparse or intermittent demand.
• Complex models are hard to understand, leading to poor product allocation and low return on investment on promotions.
• Different departments use different methods, leading to miscommunication and costly reconciliation errors.

AI-based demand forecasting techniques can help. Vertex AI Forecast supports retailers in maintaining greater inventory flexibility by infusing machine learning into their existing systems. Machine learning and AI-based forecasting models like Vertex AI are able to digest large sets of disparate data, drive analytics and automatically adjust when provided with new information. 

With these machine learning models, retailers can not only incorporate historical sales data, but also use close to real-time data such as marketing campaigns, web actions like a customer clicking the “add to cart” button on a website, local weather forecasts, and much more. 

Pivot point 2: Enhanced product discovery through AI-powered search and recommendations

If customers can’t easily find what they are looking for, whether online or at the store, they will turn to someone else. That’s a simple statement, but one with profound impacts.

In research conducted by The Harris Poll and Google Cloud, we found that over a six month period, 95% of consumers received search results that were not relevant to what they were searching for on a retail website. And roughly 85% of consumers view a brand differently after an unsuccessful search, while 74% say they avoid websites where they’ve experienced search difficulties in the past.

Each year, retailers lose more than $300 billion dollars from search abandonment, or when a consumer searches for a product on a retailer’s website but does not find what they are looking for. Our product discovery solutions help you surface the right products, to the right customers, at the right time. These solutions include: 

• Vision Product Search, which is like bringing the augmented reality of Google Lens to a retailer’s own branded mobile app experience. Both shoppers and retail store associates can search for products using an image they’ve photographed or found online and receive a ranked list of similar items.
• Recommendations AI, which enables retailers to deliver highly personalized recommendations at scale across channels.
• Retail Search, which provides Google-quality search results on a retailer’s own website and mobile applications.

All three are powered by Google Cloud, leveraging Google’s advanced understanding of user context and intent, utilizing technology to deliver a seamless experience to every shopper. With these combined capabilities, retailers are able to reduce search abandonment and improve conversions across their digital properties. 

Pivot point 3: Building the modern store

Stores are no longer places for just browsing and buying. They must be flexible operation centers, ready to pivot to address changing circumstances. The modern store must be multiple things at once: a mini-fulfillment and return center, a recommendation engine, a shopping destination, a fun place to work, and more. 

Just as retail companies had to embrace omnichannel, stores are now becoming omnichannel centers on their own, mixing the digital and physical into a single location. Retailers can use physical stores as a vehicle to deliver superior customer experiences. This will demand heightened levels of collaboration and cooperation between stores, digital, and tech infrastructure teams, building on the agile ways they have worked together.

In many ways, it’s about allowing our physical spaces to function more like digital ones. Google Cloud can help by bringing the scalability, security, and reliability of the cloud to the store, allowing physical locations to upgrade infrastructure and modernize their internal and customer-facing applications. 

Think of it as when a new OS gets released for your phone. It’s the same small, hard box, but the experience can feel radically different. Now, extend that same idea to a digitally enabled store. With the right displays, interfaces, and tools at a given retail location, the team only needs to send an over-the-air update to create radically fresh experiences, ranging from sales displays to fulfillment or employee engagement.

Such an approach can enable streamlined experiences for both customers and store associates. For instance, when it comes to the modern store’s evolving role as a fulfillment or return center, cloud solutions can help drive efficiency in stores through automation of ordering, replenishment, and fulfillment of omnichannel order selection.  

Similar tools for personalized product discovery online can be applied to customers in the store, helping them to browse and explore, or even create a tailored shopping experience. 

The impact of store associates can be maximized by equipping them with technology to provide expertise that drives value-added customer service, as well as increasing productivity in stores by streamlining operations, thus lowering overhead cost. At the register, customers should be able to enjoy frictionless checkout while ensuring reliable, accurate, secure transactions.

Google Cloud can help retailers transform

The ability to adapt and pivot to meet today’s changing consumer needs requires that retailers rely on modern tools to obtain operational flexibility. We believe that every company can be a tech company. That every decision is data driven. That every store is physical and digital all at once. That every worker is a tech worker. 

Google Cloud works with retailers to help them solve their most challenging problems. We have the unique ability to handle massive amounts of unstructured data, in addition to advanced capabilities in AI and ML. Our products and solutions help retailers focus on what’s most important—from improving operations to capturing digital and omnichannel revenue.

In the past year, Google has worked to surface timely and reliable health information, amplify public health campaigns, and help nonprofits get urgent support to Indians in need. Now, we are continuing to focus on helping India’s businesses accelerate their digital transformation, deepening our commitment to India’s digitization and economic recovery. To support customers and the public sector in India and across Asia Pacific, we’re excited to announce that our new Google Cloud region in Delhi National Capital Region (NCR) is now open. 

Designed to help both Indian and global companies alike build highly available applications for their customers, the Delhi NCR region is our second Google Cloud region in India and 10th to open in Asia Pacific. 

What customers and partners are saying

Navigating this past year has been a challenge for companies as they grapple with changing customers demands and economic uncertainty. Technology has played a critical role, and we’ve been fortunate to partner with and serve people, companies, and government institutions around the world to help them adapt. The Google Cloud region in Delhi NCR will help our customers adapt to new requirements, new opportunities and new ways of working, like we’ve helped so many companies do in the region: 

• InMobi scaled a personalized AI platform to support 120+ million active users. “With the arrival of the Google Cloud Delhi NCR, InMobi Group sees the opportunity to continue closing the gap between our users and products,” says Mohit Saxena, Co-founder and Group CTO of Inmobi. “Glance, especially, has been serving AI-powered personalised content to over 120 million active users. We can’t wait to continue giving them truly meaningful experiences that are speedy, scale well, and are relevant to them, by expanding the use of our current tools working on Google Cloud with the opening of a new region.”
• Groww now supports a sizable user base. “Google Cloud provides great technology that enables us to build and scale infrastructure to millions of users, and the new Google Cloud region in Delhi NCR will continue to help more businesses and startups in India access powerful cloud-based infrastructure, products and services,” says Neeraj Singh, Co-founder and Chief Technology Officer, Groww.
• HDFC Bank is positioned for the future. “At HDFC Bank, we are harnessing technology platforms to both run and build the bank. As we progress to be future ready, the objective is to invest in future technologies that give us scale, efficiency and resiliency. Towards this the Google Cloud region in Delhi NCR will enable us to enhance our resiliency and help us in building an active-active design framework for our new generation applications on cloud,” says Ramesh Lakshminarayanan, CIO, HDFC Bank.  
• Dr. Reddy’s Lab built a modern data platform with Google Cloud. “At Dr Reddy’s, we pride ourselves in helping patients regain good health, acting quickly to provide innovative solutions to address patients’ unmet needs and in accelerating access to medicines to people worldwide. Our Google Cloud-powered data platform is helping us realize these objectives and we welcome Google’s investment in the new Delhi NCR region as helping us and other businesses in India make further contributions to our social and economic future,” says Mukesh Rathi, Senior Vice President & CIO, Dr. Reddy’s Laboratories.
• “To survive the disruption caused by the pandemic and to succeed in the long term, organizations need to become digital natives, so they can be more agile, explore new business models and build new capabilities that boost resilience. A cloud-first strategy plays a key role in enabling businesses to do this,” said Piyush N. Singh, Lead – India market unit & lead – Growth and Strategic Client Relationships, Asia Pacific and Latin America, Accenture. “Harnessing the potential of cloud requires the right data infrastructure and this expansion by Google Cloud will undoubtedly help Indian enterprises in their digital transformation journeys.”

A global network of regions

Delhi NCR joins 25 existing Google Cloud regions connected via our high-performance network, helping customers better serve their users and customers throughout the globe. As the second region in India, customers benefit from improved business continuity planning with distributed, secure infrastructure needed to meet IT and business requirements for disaster recovery, while maintaining data sovereignty.

With this new region, Google Cloud customers operating in India also benefit from low latency and high performance of their cloud-based workloads and data. Designed for high availability, the region opens with three availability zones to protect against service disruptions, and offers a portfolio of key products, including Compute Engine, App Engine, Google Kubernetes Engine, Cloud Bigtable, Cloud Spanner, and BigQuery. 

Supporting India’s recovery with training and education

Google and Google Cloud will also continue to support our customers with people and education programs. We’re investing in local talent and the local developer community to help enterprises digitally transform and support economic recovery. 

Through the India Digitization Fund, we expanded our efforts to support India’s recovery from COVID-19—in particular, through programs to support education and small businesses. In addition to expanding internet access, and investments to help start-ups accelerate India’s digital transformation, we’ve grown our Grow with Google efforts. Businesses can access digital tools to maintain business continuity, find resources like quick help videos, and learn digital skills—in both English and in Hindi.

Helping customers build their transformation clouds

Google Cloud is here to support businesses, helping them get smarter with data, deploy faster, connect more easily with people and customers throughout the globe, and protect everything that matters to their businesses. The cloud region in Delhi NCR offers new technology and tools that can be a catalyst for this change. To learn more, visit the Google Cloud locations page, and be sure to watch the region launch event here.

Let’s face it: in the globalized world, which is now more than ever a digital demand world, you need to scale and reach your customers right where they’re at. Translation is a critical piece of that, whether you’re translating a website in multiple languages or releasing a document, a piece of software, or training materials.

Manual translation does not scale, which is why machine translation, powered by machine learning (ML), is becoming more important to our customers.  Machine translation has historically been challenging because of the sheer volume and breadth of content that can add value when translated into multiple languages. Companies acquire and share content in many languages and formats, and scaling translation to meet needs is a tall order due to multiple document formats, integrations with optical character recognition (OCR), and the need to correct for domain-specific terminology.

Our goal is to simplify translation services, while enabling flexibility and control for our customers’ unique needs across industries. Read on to learn more about recent features and updates. 

Formatting matters: Document Translation is now GA 

In many cases, the layout of a document dictates how it should be interpreted—e.g., readers navigate text and discern meaning based on formatting, like bold or italicized text, or markups for headers, paragraphs, and columns. Previously, to automate translation of documents, text needed to be separated from these layout attributes, meaning the document’s structure was either lost or needed to be recreated later in the developer pipeline, after the text had been translated. This required translation teams to do a lot of extra work and maintain a lot of additional code. But now, those steps are unnecessary. Formatting can be retained throughout the translation process, handled directly by the Translation API Advanced. 

This feature lets customers translate documents in 100+ languages and supports document types such as Docx, PPTx, XLSx, and PDF while preserving document formatting.

And if your needs go beyond Document Translation, we can help you translate audio as well. For real-time streaming translation, check out the Media Translation API, and for offline transcription translation, combine the Translation API with the Video Intelligence API.   

Real-Time translation when you need it, Batch when you don’t

One of the biggest differentiators for Translation API Advanced’s document translation capabilities is the ability to do real-time, synchronous processing for a single file. 

For example, if you are translating a business document such as HR documentation, online translation provides flexibility for smaller files and provides faster results. You can easily integrate with our APIs via REST or gRPC with mobile or browser applications, with instant access to 100+ language pairs so that content can be understandable in any supported language. 

Meanwhile, batch translation allows customers to translate multiple files into multiple languages in a single request. For each request, customers can send up to 100 files with a total content size of up to 1 GB or 100 million Unicode codepoints, whichever limit is hit first.

State of the Art (SOTA) accuracy, with flexibility for customization

In order to achieve the highest level of accuracy for your translation, we now support multiple options:

• Use Google’s SOTA translation models: Each year, Google heavily invests to improve the quality of our translations across Apps, Cloud APIs, and Chrome, as well to enable multilanguage answers in Search. A popular metric for automatic quality evaluation of Machine translation systems is the BLEU score, which is based on the similarity between machine translation and the reference translations that were generated by people. While we push out incremental improvements for individual models on a monthly cadence, there are also times where we make significant leaps. In the releases since 2019, we have improved our average BLEU score by 5pts on average across 100+ languages and 7pts on low resource languages.
• Leverage glossaries for specific terms and phrases: Glossary is our terminology control feature. It allows you to import source content to define preferred translations, such as product names or department names. Then, when calling the glossary in the API request, your preferred translations will be enforced. This will work for words as well as phrase translation.
• Pick a pre-trained model with model selection: If you create custom models for machine translation, we don’t think you should have multiple client libraries and multiple APIs to maintain in order for you to use the best model for your needs. Translation API Advanced now supports Model Selection. Pick your pretrained model or pick your custom ML model built on AutoML for any language pair you’ve created and use the same API and the same client library. 
• Build custom translation models with AutoML: AutoML Translation is a suite of ML products that enable you to build high quality models for your own use case or data, with limited-to-no ML expertise or coding required. Bring your past human-validated translations to improve translation specificity for your domain.

Keep localization local with Regional Endpoints

If you are a customer operating in the EU, we recently launched an endpoint specifically for EU regionalization. This is a configurable endpoint for customers to store and perform machine translation processing of customer data only in the EU multi region. For now, this only supports our pretrained translation models and glossary, but batch translations will be coming soon.

How Eli Lilly uses Cloud Translation to translate content globally

Historically, translations at Eli Lilly have been complicated: numerous translation vendors have been needed for different languages and organizations, all with their own processes and expectations. On top of that, translations have been costly and slow. 

To solve this, Eli Lilly took a codified approach to enable users and systems to spend less time and resources to safely generate quality translations. 

Learn more, and even catch a demo, from Thomas Griffin, Translation Tech Lead & Global Regulatory Architect for Eli Lilly.

Learn more 

• To get started using Cloud Translation – Advanced, complete the setup and then try the Translate text (Advanced edition) quickstart.
• Document Translation is priced per page. For more information, see pricing.