Editor’s note: This blog has been updated since it originally published to reflect the total number of generative AI courses that have become available since the publish date.

An AI/ML (artificial intelligence/machine learning) career path can be a great specialty area within the cloud—and one of the most accessible! Because this area is constantly developing, most recently with the rise of generative AI, I want to share some recommendations to help you chart a sustainable career as AI/ML continues to evolve.

Generative AI falls under the overall category of AI and offers a new and exciting way of interacting with information, brands, and other people. Let’s talk about how these disciplines work together and about materials available to help you upskill in these areas.

To this end, we are happy to announce a new set of generative AI training content available at no cost. So, whether you are just getting started or already have a more advanced role, read on to find ways to help reach your desired position. 

ML compared to AI

While the terms ML and AI are often used interchangeably, there are distinct differences. AI is an umbrella concept wherein machines are taught to perform tasks normally associated with human intelligence, such as decision-making and language interaction. ML is a subset of AI dedicated to taking data from the past and training algorithms to create models that can perform highly complex tasks without being explicitly programmed. It’s the basis for most forms of AI that people interact with, like virtual assistants, music recommendations, and chatbots.

Data engineers and ML engineers work with data scientists to get insights from data. They are needed to create software models and get clear results, and develop deployable applications. These skilled roles are needed in every industry!

Change the world for the better with generative AI

I recently wrote about four key pillars of technology trends expected in the next decade, including the role of AI/ML in the cloud environment as one of those pillars, and how you can bridge the skills gap and build your career.

Generative AI is a new type of ML that has made a lot of headlines recently. Research from CIO Dive finds that seven in 10 executives say their companies are investigating or exploring generative AI. Now is a great time to become an expert, while we are at the cusp of this technology becoming more widely adopted. 

To get to generative AI, we need to talk first about deep learning. Deep learning is a subset of ML that uses artificial neural networks to process more complex patterns than traditional ML. Generative AI sits still further down the funnel, as a subset of deep learning that typically involves the Transformer architecture. Essentially, it’s a type of AI that can map long-range dependencies and patterns in large training sets, then use what it learns to produce new content, including text, imagery, audio, and synthetic data.

Generative AI relies on large models, such as large language models (LLMs) that can classify and generate text, answer questions, and summarize documents. For more detail about how this works, check out the video below from Google I/O 2023, which features great information from Dr. Gwendolyn Stripling, Artificial Intelligence Technical Curriculum Developer for Google Cloud.

How Google is offering generative AI

Here at Google, we’ve been heavily invested for decades in research and innovation aimed at helping businesses, governments, and developers maximize the potential of AI. Our vision is to empower builders, innovators, developers, and doers to use AI in unique, responsible, productive ways. 

To deliver on this, we recently announced a variety of solutions to bring generative AI into our offerings, beginning with Generative AI support on Vertex AI and Gen App Builder. 

Vertex AI is Google Cloud’s ML platform for training and deploying ML models and AI applications. With Generative AI support on Vertex AI, data science teams and developers can access foundation models from both Google and other sources, helping them to quickly build, customize, and deploy models for their own use cases. 

As part of generative AI support on Vertex AI, Model Garden and Generative AI Studio are now in preview, including access to PaLM 2 for Text and Chat and Embeddings API for Text, while other features and services are available to select trusted testers. If you’d like early access to Google Cloud’s AI products, join the waitlist here.

We announced our latest PaLM model in production at I/O 2023: PaLM 2. Today, it powers more than 25 products! Additionally, we’ve fine-tuned PaLM 2 for specific use cases, including security and medical domains. You’ve probably also heard of Bard, our experiment for conversational AI, which is fully running on PaLM 2. 

Our Gen App Builder aims to make customer, partner, and employee interactions more effective and helpful. With it, developers — even those with limited data science expertise — can quickly create bots, chat apps, digital assistants, custom search engines, and more. Gen App Builder even makes it possible to create some generative AI apps without any coding skills. 

Now, let’s bring these products to life! Not sure exactly the use case or in need of a little inspiration? Check out this blog from Google Cloud CEO Thomas Kurian to see how customers and partners in the ecosystem are bringing ideas to life with generative AI. And watch this video to see generative AI in action!

Build your AI/ML skills and validate your knowledge to grow your career

There is a lot of excitement around generative AI—it’s truly a brand new path to consider for your AI/ML career! Check out my recommendations below for training options for AI/ML roles. These will help you gain critical skills as generative AI becomes more widely available.

*NEW* training materials, specific to generative AI technologies
Generative AI Learning Path – no cost training 

• [Course] Introduction to Generative AI (1 day)
• [Course] Introduction to Large Language Models (1 day)
• [Course] Attention Mechanism (1 day)
• [Course] Transformer Models and BERT Model (1 day)
• [Course] Introduction to Image Generation (1 day)
• [Course] Create Image Captioning Models (1 day)
• [Course] Encoder-Decoder Architecture (1 day)

Additional AI/ML training – varying learning credits required to complete on Google Cloud Skills Boost 
Introductory level

• [Course with completion badge] How Google Does Machine Learning (1 day)
• [Course with completion badge] MLOps: Getting Started (1 day)
• [Skill badge] Get started with TensorFlow on Google Cloud (8 hours)  
• [Skill badge] Perform foundational ML, AI and data tasks in Google Cloud (7 hours)
• [Course with completion badge] Language, Speech, Text, and Translation with Google Cloud APIs (5 hours)

Intermediate/Multi-level

• [Learning Path] Machine Learning Engineer (Collection of 15 video courses and labs) 
• [Skill badge] Build and Deploy Machine Learning Solutions on Vertex AI (1 day)

Advanced

• [Training + certification exam] Google Cloud Professional ML Engineer Certification (varied time)
• [Skill badge] Machine Learning with TensorFlow in Vertex AI (90 minutes)
• [Course with completion badge] Natural Language Processing in Google Cloud (1 day)

You can also catch up on hands-on demos in our most recent Cloud OnBoard: From Data to AI with BigQuery and Vertex AI. In it, we guide you through all steps of the data-to-AI solution.

Coming up on June 29, we are hosting Getting Started with Vertex Generative AI as part of Innovators Live. Register now!

I also encourage you to consider an Innovators Plus subscription. You get access to over 700 labs, skill badges and courses (including many that are specific to AI/ML), a certification exam voucher, and up to $1,000 in Google Cloud credits. You’ll also get access to live learning events and technical briefings with Google Cloud experts, plus 1:1 consultations (talk with us about your upcoming generative AI project!). It’s a $1,500 package for only $299. Learn more!

Generative AI is a rapidly expanding technology with a wide range of potential applications. Google Cloud Learning is thrilled to offer a new, no-cost Generative AI Fundamentals skill badge. This skill badge is designed for anyone eager to learn about the power of generative AI. No technical skills or prior knowledge required!

For those new to digital credentials, Google Cloud skill badges are digital credentials issued by Google Cloud in recognition of your knowledge of Google Cloud products and services. Individuals can earn skill badges on Google Cloud Skills Boost, and can share their skill badge to their social media profile and resume.

Watch the short videos in the generative AI courses and complete the final quiz to earn the Generative AI Fundamentals skill badge pictured below. In as little as 120 minutes, you will learn the basics of how generative AI works, how Google Cloud AI technology can be used by businesses and individuals, and how the principles of responsible AI lead to ethical decisions about the use of generative AI. 

By earning the skill badge, you will demonstrate your understanding of foundational concepts in generative AI.

The topics covered in the courses include:

1. Introduction to Generative AI 

• Explain how generative AI works
• Describe generative AI model types
• Describe generative AI applications

2. Introduction to Large Language Models

• Define large language models (LLMs)
• Describe LLM use cases
• Explain prompt tuning
• Describe Google’s generative AI development tools

3. Introduction to Responsible AI

• Identify the need for a responsible AI practice within an organization
• Recognize that decisions made at all stages of a project make an impact in Responsible AI
• Recognize that organizations can design an AI infrastructure to fit their own business needs and values

Earn the skill badge and show off your generative AI knowledge today! And for more content to help you stay up to date with generative AI, check out “The Prompt” and our generative AI primer for executives on Transform with Google Cloud.

The latest round of MLPerf benchmark results have been released, and Google’s TPU v4 supercomputers demonstrated record-breaking performance at scale. This is a timely milestone since large-scale machine learning training has enabled many of the recent breakthroughs in AI, with the latest models encompassing billions or even trillions of parameters (T5, Meena, GShard, Switch Transformer, and GPT-3). 

Google’s TPU v4 Pod was designed, in part, to meet these expansive training needs, and TPU v4 Pods set performance records in four of the six MLPerf benchmarks Google entered using TensorFlow and JAX. These scores are a significant improvement over our winning submission from last year and demonstrate that Google once again has the world’s fastest machine learning supercomputers. These TPU v4 Pods are already widely deployed throughout Google data centers for our internal machine learning workloads and will be available via Google Cloud later this year.

Figure 1: Speedup of Google’s best MLPerf Training v1.0 TPU v4 submission over the fastest non-Google submission in any availability category – in this case, all baseline submissions came from NVIDIA. Comparisons are normalized by overall training time regardless of system size. Taller bars are better.¹

Let’s take a closer look at some of the innovations that delivered these ground-breaking results and what this means for large model training at Google and beyond. 

Google’s continued performance leadership

Google’s submissions for the most recent MLPerf demonstrated leading top-line performance (fastest time to reach target quality), setting new performance records in four benchmarks. We achieved this by scaling up to 3,456 of our next-gen TPU v4 ASICs with hundreds of CPU hosts for the multiple benchmarks. We achieved an average of 1.7x improvement in our top-line submissions compared to last year’s results. This means we can now train some of the most common machine learning models in a matter of seconds.

Figure 2: Speedup of Google’s MLPerf Training v1.0 TPU v4 submission over Google’s MLPerf Training v0.7 TPU v3 submission (exception: DLRM results in MLPerf v0.7 were obtained using TPU v4). Comparisons are normalized by overall training time regardless of system size. Taller bars are better. Unet3D not shown since it is a new benchmark for MLPerf v1.0.²

We achieved these performance improvements through continued investment in both our hardware and software stacks. Part of the speedup comes from using Google’s fourth-generation TPU ASIC, which offers a significant boost in raw processing power over the previous generation, TPU v3. 4,096 of these TPU v4 chips are networked together to create a TPU v4 Pod, with each pod delivering 1.1 exaflop/s of peak performance.

Figure 3: A visual representation of 1 exaflop/s of computing power. If 10 million laptops were running simultaneously, then all that computing power would almost match the computing power of 1 exaflop/s.

In parallel, we introduced a number of new features into the XLA compiler to improve the performance of any ML model running on TPU v4. One of these features provides the ability to operate two (or potentially more) TPU cores as a single logical device using a shared uniform memory access system. This memory space unification allows the cores to easily share input and output data – allowing for a more performant allocation of work across cores. A second feature improves performance through a fine-grained overlap of compute and communication. Finally, we introduced a technique to automatically transform convolution operations such that space dimensions are converted into additional batch dimensions. This technique improves performance at the low batch sizes that are common at very large scales.

Enabling large model research using carbon-free energy

Though the margin of difference in topline MLPerf benchmarks can be measured in mere seconds, this can translate to many days worth of training time on the state-of-the-art models that comprise billions or trillions of parameters. To give an example, today we can train a 4 trillion parameter dense Transformer with GSPMD on 2048 TPU cores. For context, this is over 20 times larger than the GPT-3 model published by OpenAI last year. We are already using TPU v4 Pods extensively within Google to develop research breakthroughs such as MUM and LaMDA, and improve our core products such as Search, Assistant and Translate. The faster training times from TPUs result in efficiency savings and improved research and development velocity. Many of these TPU v4 Pods will be operating at or near 90% carbon free energy. Furthermore, cloud datacenters can be ~1.4-2X more energy efficient than typical datacenters, and the ML-oriented accelerators – like TPUs – running inside them can be ~2-5X more effective than off-the-shelf systems. 

We are also excited to soon offer TPU v4 Pods on Google Cloud, making the world’s fastest machine learning training supercomputers available to customers around the world. Cloud TPUs support leading frameworks such as TensorFlow, PyTorch, and Jax, and we recently released an all-new Cloud TPU system architecture that provides direct access to TPU host machines, greatly improving the user experience. 

Want to learn more? 

Please contact your Google Cloud sales representative to request early access to Cloud TPU v4 Pods. We are excited to see how you will expand the machine learning frontier with access to exaflops of TPU computing power!

^{1. All results retrieved from www.mlperf.org on June 30, 2021. MLPerf name and logo are trademarks. See www.mlperf.org for more information. Chart uses results 1.0-1067, 1.0-1070, 1.0-1071, 1.0-1072, 1.0-1073, 1.0-1074, 1.0-1075, 1.0-1076, 1.0-1077, 1.0-1088, 1.0-1089, 1.0-1090, 1.0-1091, 1.0-1092.
2. All results retrieved from www.mlperf.org on June 30, 2021. MLPerf name and logo are trademarks. See www.mlperf.org for more information. Chart uses results 0.7-65, 0.7-66, 0.7-67, 1.0-1088, 1.0-1090, 1.0-1091, 1.0-1092.}