Gartner® named Google as a Leader in the 2022 Magic Quadrant™ for Cloud AI Developer Services report. This evaluation covered Google’s language, vision and structured data products including AutoML, all of which we deliver through Google Cloud. We believe this recognition is a reflection of the confidence and satisfaction that customers have in our language, vision, and AutoML products for developers. Google remains a Leader for the third year in a row, based upon the completeness of our vision and our ability to execute.

Developers benefit in many ways by using Cloud AI services and solutions. Customers recognize the advantages of Google’s AI and ML services for developers, such as Vertex AI, BigQuery ML, AutoML and AI APIs. In addition, customers benefit from the pace of progress in the field of Responsible AI and actionable ethics processes applied to all customer and partner solutions leveraging Google Cloud technology, as well as our core architecture including the Vertex AI platform, vision, conversational AI, language and structured data, and optimization services and key vertical industry solutions.

We believe that our ‘Leader’ placement validates this vision for AI developer tools. Let’s take a closer look at some of the report findings.

ML tools purpose-built for developers

Google’s machine learning tools have been built by developers, for developers, based on the groundbreaking research generated from Google Research and DeepMind. This developer empathy drives product development, which supports the developer community to achieve deep value from Google’s AI and ML services. An example of this is the unification of all of the tools needed for building, deploying and managing ML models into one ML platform, Vertex AI, resulting in accelerated time to production. They also cite BigQuery ML, AutoML for language, vision video and tabular data) and prebuilt ML APIs (such as speech and translation) as having high utility for developers at all levels of ML expertise to build custom AI and quickly infuse AI into their applications.

Leading organizations like OTOY, Allen Institute for AI and DeepMind (an Alphabet subsidiary) choose Google for ML, and enterprises like Twitter, Wayfair and The Home Depot shared more about their partnership with Google in their recent sessions at Google Next 2021.

Responsible AI principles and practices

Responsible AI is a critical component of successful AI. A 2020 study commissioned by Google Cloud and the Economic Intelligence Unit highlighted that ethical AI does not only prevent organizations from making egregious mistakes, but that the value of responsible AI practices for competitive edge, as well as talent acquisition and retention are notable. At Google, we not only apply our ethics review process to first party platforms and solutions, to ensure that our services design-in responsible AI from the outset, we also consult with customers and partners based on AI principles to deliver accountability and avoid unfair biases. In addition, our best-in-class tools provide developers with the functionality they need to evaluate fairness and biases in datasets and models. Our Explainable AI tools such as model cards provide model transparency in a structured, accessible way, and the What-If Tool is essential for developers and data scientists to evaluate, debug and improve their ML models.

Clear and understandable product architecture

Google Cloud’s investment in our ML product portfolio has led to a comprehensive, integrated and open offering that spans breadth (across vision, conversational AI, language and structured data, and optimization services) and depth (core AI services, with features such as Vertex AI Pipelines and Vertex Explainable AI built on top). Industry-specific solutions tailored by Google for retail, financial services, manufacturing, media and healthcare customers, such as Recommendations AI, Visual Inspection AI, Media Translation, Healthcare Data Engine, add another layer leveraging this foundational platform to help organizations and users adopt machine learning solutions more easily.

At Google Cloud, we refuse to make developers jump through hoops to derive value out of our technology; instead, we bring the value directly to them by ensuring that all of our AI and ML products and solutions work seamlessly together. To download the full report, click here. Get started on Vertex AI and talk with our sales team.

Disclaimer:

Gartner, Magic Quadrant for Cloud AI Developer Services, Van Baker, Arun Batchu, Erick Brethenoux, Svetlana Sicular, Mike Fang, May 23, 2022.

Gartner and Magic Quadrant are registered trademarks of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved.

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.

As one of the most innate and ubiquitous forms of expression, speech is a fundamental pillar of human interaction. It comes as no surprise, then, that Google Cloud’s Speech API  has become a crucial tool for enterprise customers, launched to general availability (GA) over six years ago and, now, processing over 1 billion voice minutes each month.

With the Speech API, we’ve been pleased to serve thousands of customers and provide industry-leading speech recognition quality and cost-effective products across a range of industries. We want to constantly evolve our offerings and bring new benefits to organizations, which is why today, we’re excited to announce the GA release of our new Speech-to-Text v2 API. 

Speech-to-Text v2 modernizes our API interface and introduces several new features. It also migrates all of our existing functionality, so you can use the same models and features that you were using in STT v1 or v1p1beta1 APIs. This new version of our API also allows us to take advantage of significant cost savings in our serving path, and as such we are reducing our base price, as well as adding pricing incentives for large workloads and those willing to accept longer turnaround times.

This new infrastructure also allows us to serve a wide variety of new types of models, including Chirp , our latest 2B-parameter large speech model. All of these are Generally Available to Google Cloud Platform customers and users starting today.

Let’s have a more thorough look though at the enhanced features of Speech-to-Text API V2 and illustrate how your business can benefit from our new capabilities:

Expanding Speech-to-Text features with V2 API

Since the official launch of Speech-to-Text API back in 2017, we’ve utilized Google’s global infrastructure to host and monitor our production-facing transcription models. This robust, well-connected network has been the backbone of our offering for all of our customers. 

However, a unified view of our Speech-to-Text service has been a crucial request for our enterprise customers  who need to satisfy data residency and compliance requirements, especially in regulated industries like banking and public sector. We listened carefully to this feedback, and starting today, our Speech-to-Text v2 API supports full regionalization, allowing our customers to invoke identical copies of all our transcription models in the Google Cloud Platform region of their choice. 

In addition to giving users the flexibility to deploy in any region, we are adding a number of new features to help developers build on the API: 

• Recognizers: A user-defined named configuration that combines a model identifier, the language-locale of the audio to be transcribed, and the cloud region for the transcription model to run. Once-created, the recognizer can be referenced to every subsequent transcription request, eliminating the need for users to repeatedly define the same configuration parameters. This resourceful implementation of recognizers allows for greater flexibility in authentication and authorization, as users are not longer required to set up dedicated service accounts.
• Cloud Logging: Requests performed using a recognizer object automatically support cloud logging by default. Since the recognizers are defined as named entities, customers can partition traffic based on the recognizer of interest or collectively. 
• Audio Format Auto-Detection: Instead of having our users analyze and manually define the audio configuration settings to pass in a transcription request, the new Speech-to-Text V2 API detects settings like encoding, sampling rate, and channel count, then automatically populates the request configuration parameters. 

Increasing accuracy at Enterprise Scale with Chirp

As part of our continuous investment in foundational speech models, in March 2023 we released research results for our Universal Speech Mode (USM), a family of state-of-the-art speech models with 2B parameters and support for transcriptions of 300+ languages. In May 2023, at Google I/O, we announced Chirp in Private Preview, the latest version of the USM family, fine-tuned for our Cloud-specific use-cases. 

Chirp is now GA through the Speech-to-Text v2 API. Following extensive testing and feedback from our customers, we are making the power of pre-trained large models accessible through a simple enterprise-grade API surface. Our early adopters have seen major strides in customer engagement, thanks to the market-leading accuracy and language coverage of the new model, and we cannot wait to see what opportunities our enterprise customers will unlock.

Introducing new pricing, tiers, and options

We have heard from customers that price can be just as important as quality for many workloads. That’s why the Speech-to-Text API v2 features totally new pricing. We have lowered the cost of real-time and batch transcription from $0.024 per minute to $0.016 per minute. Additionally, we know that pricing can be a concern for those that have very large transcription workloads. For that reason, we are also introducing standard volume tiers, allowing costs as low as $0.004 per minute. As always, additional discounts are available to those with even larger workloads.

Staying true to our commitment on flexibility and choice in pricing, we noticed that even though the majority of our customers are interested in our models for real-time transcription scenarios, many are also interested in non-real time transcriptions for data at rest. With our new API v2 infrastructure, we can take greater advantage of capacity that goes unused at certain times. With our new Dynamic Batch pricing, we are passing this savings on to customers that are less latency sensitive. Aimed at users who can wait up to 24 hours for transcription results, Dynamic Batch is a new discounted pricing tier that offers 75% lower price per minute for transcription audio relative to our Standard tier.

For more information on Dynamic Batch and all our new STT v2 API pricing, check out our pricing page.

Learn more and start you Speech-to-Text journey

For more information to help get you started on your migration journey from V1 to V2, head over to the detailed documentation and try our walkthroughs to make the most of regionalization and recognizers.

If you are curious to learn more about how to use Chirp, you can get  started by following our tutorial. You can also read more about how we built it in our research blog post.

We are very excited to bring all these improvements to our developer ecosystem and provide you with the tools to leverage the power of voice for your businesses, programs, and applications.

Posted by Jeff Dean, Senior Fellow and SVP of Google Research, on behalf of the entire Google Research community

Over the last several decades, I’ve witnessed a lot of change in the fields of machine learning (ML) and computer science. Early approaches, which often fell short, eventually gave rise to modern approaches that have been very successful. Following that long-arc pattern of progress, I think we’ll see a number of exciting advances over the next several years, advances that will ultimately benefit the lives of billions of people with greater impact than ever before. In this post, I’ll highlight five areas where ML is poised to have such impact. For each, I’ll discuss related research (mostly from 2021) and the directions and progress we’ll likely see in the next few years.

· Trend 1: More Capable, General-Purpose ML Models
· Trend 2: Continued Efficiency Improvements for ML
· Trend 3: ML Is Becoming More Personally and Communally Beneficial
· Trend 4: Growing Benefits of ML in Science, Health and Sustainability
· Trend 5: Deeper and Broader Understanding of ML

Trend 1: More Capable, General-Purpose ML Models

Researchers are training larger, more capable machine learning models than ever before. For example, just in the last couple of years models in the language domain have grown from billions of parameters trained on tens of billions of tokens of data (e.g., the 11B parameter T5 model), to hundreds of billions or trillions of parameters trained on trillions of tokens of data (e.g., dense models such as OpenAI’s 175B parameter GPT-3 model and DeepMind’s 280B parameter Gopher model, and sparse models such as Google’s 600B parameter GShard model and 1.2T parameter GLaM model). These increases in dataset and model size have led to significant increases in accuracy for a wide variety of language tasks, as shown by across-the-board improvements on standard natural language processing (NLP) benchmark tasks (as predicted by work on neural scaling laws for language models and machine translation models).

Many of these advanced models are focused on the single but important modality of written language and have shown state-of-the-art results in language understanding benchmarks and open-ended conversational abilities, even across multiple tasks in a domain. They have also shown exciting capabilities to generalize to new language tasks with relatively little training data, in some cases, with few to no training examples for a new task. A couple of examples include improved long-form question answering, zero-label learning in NLP, and our LaMDA model, which demonstrates a sophisticated ability to carry on open-ended conversations that maintain significant context across multiple turns of dialog.

Transformer models are also having a major impact in image, video, and speech models, all of which also benefit significantly from scale, as predicted by work on scaling laws for visual transformer models. Transformers for image recognition and for video classification are achieving state-of-the-art results on many benchmarks, and we’ve also demonstrated that co-training models on both image data and video data can improve performance on video tasks compared with video data alone. We’ve developed sparse, axial attention mechanisms for image and video transformers that use computation more efficiently, found better ways of tokenizing images for visual transformer models, and improved our understanding of visual transformer methods by examining how they operate compared with convolutional neural networks. Combining transformer models with convolutional operations has shown significant benefits in visual as well as speech recognition tasks.

The outputs of generative models are also substantially improving. This is most apparent in generative models for images, which have made significant strides over the last few years. For example, recent models have demonstrated the ability to create realistic images given just a category (e.g., “irish setter” or “streetcar”, if you desire), can “fill in” a low-resolution image to create a natural-looking high-resolution counterpart (“computer, enhance!”), and can even create natural-looking aerial nature scenes of arbitrary length. As another example, images can be converted to a sequence of discrete tokens that can then be synthesized at high fidelity with an autoregressive generative model.

Because these are powerful capabilities that come with great responsibility, we carefully vet potential applications of these sorts of models against our AI Principles.

Beyond advanced single-modality models, we are also starting to see large-scale multi-modal models. These are some of the most advanced models to date because they can accept multiple different input modalities (e.g., language, images, speech, video) and, in some cases, produce different output modalities, for example, generating images from descriptive sentences or paragraphs, or describing the visual content of images in human languages. This is an exciting direction because like the real world, some things are easier to learn in data that is multimodal (e.g., reading about something and seeing a demonstration is more useful than just reading about it). As such, pairing images and text can help with multi-lingual retrieval tasks, and better understanding of how to pair text and image inputs can yield improved results for image captioning tasks. Similarly, jointly training on visual and textual data can also help improve accuracy and robustness on visual classification tasks, while co-training on image, video, and audio tasks improves generalization performance for all modalities. There are also tantalizing hints that natural language can be used as an input for image manipulation, telling robots how to interact with the world and controlling other software systems, portending potential changes to how user interfaces are developed. Modalities handled by these models will include speech, sounds, images, video, and languages, and may even extend to structured data, knowledge graphs, and time series data.

Often these models are trained using self-supervised learning approaches, where the model learns from observations of “raw” data that has not been curated or labeled, e.g., language models used in GPT-3 and GLaM, the self-supervised speech model BigSSL, the visual contrastive learning model SimCLR, and the multimodal contrastive model VATT. Self-supervised learning allows a large speech recognition model to match the previous Voice Search automatic speech recognition (ASR) benchmark accuracy while using only 3% of the annotated training data. These trends are exciting because they can substantially reduce the effort required to enable ML for a particular task, and because they make it easier (though by no means trivial) to train models on more representative data that better reflects different subpopulations, regions, languages, or other important dimensions of representation.

All of these trends are pointing in the direction of training highly capable general-purpose models that can handle multiple modalities of data and solve thousands or millions of tasks. By building in sparsity, so that the only parts of a model that are activated for a given task are those that have been optimized for it, these multimodal models can be made highly efficient. Over the next few years, we are pursuing this vision in a next-generation architecture and umbrella effort called Pathways. We expect to see substantial progress in this area, as we combine together many ideas that to date have been pursued relatively independently.

Cloud developers — you’ve got it all. You can code in your choice of languages, enjoy portability with containers, minimize complexity with serverless, and manage the entire software lifecycle by following DevOps principles. But let’s face it, building and onboarding new cloud applications still requires a lot of manual planning, synthesis, and, yes, hard work. You need to research and plan your deployment, create a workable, secure architecture, and of course, you need to write the actual code,

For the last several decades, the cloud has been primarily a “do it yourself” model with volumes of options that have made development more complicated. The cloud went from overwhelmingly exciting to…. a bit overwhelming.

What if we all could bring that excitement back? What if you had some help that was available whenever and wherever you needed it? 

Say hello to Duet AI for Google Cloud

Powered by Google’s state-of-the-art generative-AI foundation models, Duet AI for Google Cloud is an always-on AI collaborator that provides help to users of all skill levels where they need it. With Duet AI, we’re on a mission to deliver a new cloud experience that’s personalized and intent-driven, and can deeply understand your environment to assist you in building secure, scalable applications, while providing expert guidance. 

As we evolve Google Cloud with Duet AI, we are looking to build a cloud platform that is more human-centric, holistic, and helpful, with responsible AI at the center of the experience:

• Human-centric: With Duet AI, we are making Google Cloud more accessible and personal to any type of user at any skill level by providing them with support whenever they need it, from code recommendations for developers, to prompt-based data insights for data engineers, to chat-based app creation for business users.
• Holistic: With generative AI at the center of the cloud experience, cloud development can be more cohesive, with fewer silos across functions, services, and tech stacks, providing a holistic picture in the format you want, wherever you are in Google Cloud.
• Helpful: To deliver smarter, contextual recommendations for building and operating apps with Google Cloud, we pre-trained Codey, one of the foundation models that powers Duet AI, with Google Cloud-specific content like documentation and sample code, and fine-tuned it based on Google Cloud  user behaviors and patterns. 
• Responsible: Our AI Principles set out our commitment to developing technology responsibly. Your code and recommendations will not be reused for any model learning and development. This helps ensure the privacy of your data and code, and also the integrity of the knowledge space from which our AI models are trained.

New capabilities available in Duet AI for Google Cloud

Here are some of the new capabilities available to get us started on our mission to deliver a new personalized and intent-driven cloud experience:

• Code assistance provides AI-driven code assistance for cloud users such as application developers and data engineers. It gives code recommendations as they type in real time, generates full functions and code blocks, and identifies vulnerabilities and errors in the code, while suggesting fixes.

Code assistance auto-generates code for creating a Google Cloud Storage bucket

Code assistance will be available through multiple products and services across Google Cloud, such as in Cloud Workstations, our fully-managed secure development environment, and other code-editing experiences in the Google Cloud Console. Developers will also find code assistance in Cloud Shell Editor or via our Cloud Code IDE extensions for VSCode and JetBrains IDEs. It supports multiple languages including Go, Java, Javascript, Python, and SQL.

• Chat assistance allows people to use simple natural language to get answers on specific development or cloud-related questions. Users can engage with chat assistance to get real-time guidance on various topics, such as how to use certain cloud services or functions, or get detailed implementation plans for their cloud projects. It can also provide architectural or coding best practices, helping to reduce the need to go searching for relevant documents.

Use chat assistance to get the detailed steps for deploying an app on Cloud Run

Chat assistance will also be available across multiple Google Cloud surface areas, for example IDEs, the Cloud Console, and through products and services. Whether you’re a developer, operator, data engineer, or security professional, you’ll be able to leverage chat assistance to help get more work done faster. 

Looking to optimize these features further for developers specialized in one particular area? With Generative AI support in Vertex AI, enterprises can fine-tune Codey using their own code base. They can consume these customized Codey models directly from Vertex AI today, and later this year, they will be able to connect it to the built-in Duet AI experience. And don’t worry, if you choose to train Codey with your code, your private data is kept private, and not used in the broader foundation model training corpus. You will have transparency and control over where data is stored and how or if it is used. 

• Duet AI for AppSheet will let users create intelligent business applications,  connect their data, and build workflows into Google Workspace via natural language.  With no coding required, users will be able to build apps by describing their needs in a chat guided by AI-powered prompts. This makes app creation accessible to more users, which can allow developer teams to focus their time on other high-impact work.

Create business applications with Duet AI for AppSheet using natural language

Experiment with Duet AI for Google Cloud today

We believe that having an assistant who is constantly evolving by your side will not only reduce an already overwhelmed developer’s workload, but also bring back the excitement of cloud development. With Duet AI, you can navigate the cloud with more confidence, ease, and — dare we say it — fun. 

And this is just the beginning. The future of the cloud experience that we are shaping with Duet AI is full of possibilities. We believe the future of developer productivity is more targeted personalized assistance. Check here to see our vision for Duet AI for Google Cloud – the redefinition of productivity in the workplace through unique end-to-end AI assisted technologies. 

These early features of Duet AI for Google Cloud are available today for limited users and we will be expanding access very soon. Sign up here to join Google Cloud’s AI Trusted Tester Program.

Google Cloud’s Professional Services Organization (PSO) engages with customers to ensure effective and efficient operations in the cloud, from the time they begin considering how cloud can help them overcome their operational, business or technical challenges, to the time they’re looking to optimize their cloud workloads. 

We know that all parts of the cloud journey are important and can be complex.  In this blog post, we want to focus specifically on the migration process and how PSO engages in a myriad of activities to ensure a successful migration.

As a team of trusted technical advisors, PSO will approach migrations in three phases:

1. Pre-Migration Planning
2. Cutover Activities
3. Post-Migration Operations

While this post will not cover in detail all of the steps required for a migration, it will focus on how PSO engages in specific activities to meet customer objectives, manage risk, and deliver value.  We will discuss the assets, processes and tools that we leverage to ensure success.

Pre-Migration Planning

Assess Scope

Before the migration happens, you will need to understand and clarify the future state that you’re working towards.  From a logistical perspective, PSO will be helping you with capacity planning to ensure sufficient resources are available for your envisioned future state.

While migration into the cloud does allow you to eliminate many of the considerations for the physical, logistical, and financial concerns of traditional data centers and co-locations, it does not remove the need for active management of quotas, preparation for large migrations, and forecasting.  PSO will help you forecast your needs in advance and work with the capacity team to adjust quotas, manage resources, and ensure availability. 

Once the future state has been determined, PSO will also work with the product teams to determine any gaps in functionality.  PSO captures feature requests across Google Cloud services and makes sure they are understood, logged, tracked, and prioritized appropriately with the relevant product teams.  From there, they work closely with the customer to determine any interim workarounds that can be leveraged while waiting for the feature to land, as well as providing updates on the upcoming roadmap.  

Develop Migration Approach and Tooling

Within Google Cloud, we have a library of assets and tools we use to assist in the migration process.  We have seen these assets help us successfully complete migrations for other customers efficiently and effectively.

Based on the scoping requirements and tooling available to assist in the migration, PSO will help recommend a migration approach.  We understand that enterprises have specific needs; differing levels of complexity and scale; regulatory, operational, or organization challenges that will need to be factored into the migration.  PSO will help customers think through the different migration options and how all of the considerations will play out.

PSO will work with the customer team to determine the best migration approach for moving servers from on-prem to Google Cloud. PSO will walk customers through different migration approaches, such as refactoring, lift-shift, or new installs. From there, the customer can determine the best fit for their migration. PSO will provide guidance on best practices and use cases from other customers with similar use cases. 

Google offers a variety of cloud native tools that can assist with asset discovery, the migration itself, and post-migration optimization. PSO, as one example, will help work with project managers to determine the best tooling that accommodates the customer’s requirements for migrating servers. PSO will also engage Google product team to ensure the customer fully understands the capabilities of each tool and the best fit for the use case. Google understands from a tooling perspective, one size does not fit all, thus PSO will work with the customer on determining the best migration approach and tooling for different requirements. 

Cutover Activities

Once all of the planning activities have been completed, PSO will assist in making sure the cutover is successful.

During and leading up to critical customer events, PSO can provide proactive event management services which deliver increased support and readiness for key workloads.  Beyond having a solid architecture and infrastructure on the platform, support for this infrastructure is essential and TAMs will help ensure that there are additional resources to support and unblock the customer where challenges arise.

As part of event management activities, PSO liaises with the Google Cloud Support Organization to ensure quick remediation and high resilience for situations where challenges arise.  A war room is usually created to facilitate quick communication about the critical activities and roadblocks that arise.  These war rooms can give customers a direct line to the support and engineering teams that will triage and resolve their issues.

Post-Migration Activities

Once cutover is complete, PSO will continue to provide support in areas such incident management, capacity planning, continuous operational support, and optimization to ensure the customer is successful from start to finish.

PSO will serve as the liaison between the customer and Google engineers. If support cases need to be escalated, PSO will ensure the appropriate parties are involved and work to get the case resolved in a timely manner. Through operational rigor, PSO will work with the customer in determining if certain Google Cloud services will be beneficial to the customer objectives. If services will add value to the customer, PSO will help enable the services so it aligns with the customer’s goal and current cloud architecture. In cases where there are missing gaps in services, PSO will proactively work with the customer and Google engineering teams to close the gaps by enabling additional functionality in the services.  

PSO will continue to work with the engineering teams to consistently review and provide recommendations on the customer’s cloud architecture in ensuring the most optimal and cost efficient design along with adhering to Google’s best practices guidelines. 

Aside from migrations, PSO is also responsible for providing continuous training of Google Cloud to customers. To ensure consistent development of Google Cloud, PSO will work with the customer to jointly develop a learning roadmap to ensure the customer has the necessary skills to succeed in delivering successful projects in Google Cloud.

Conclusion

Google PSO will be actively engaged throughout the customer’s cloud journey to ensure the necessary guidance, methodology, and tools are presented to the customer. PSO will engage in a series of activities from pre-migration planning to post migration in key areas such as capacity planning to ensure sufficient resources are allocated for future workloads to providing support on technical cases for troubleshooting. PSO will serve as a long-term trusted advisor who will be the voice of the  customer and provide the reliability and stability of the customer’s Google Cloud environment.

Click here if you’d like to engage with our PSO team on your migration. Or, you can also get started with a free discovery and assessment of your current IT landscape.