Google Cloud Skills Boost Generative AI Fundamentals and More

Google Cloud Skills has made it easier to learn and master generative AI fundamentals through its comprehensive training programs. These programs cover the basics of AI and machine learning, including how to use popular libraries like TensorFlow and Keras.

With Google Cloud Skills, you can gain hands-on experience with real-world projects and case studies, helping you to apply theoretical knowledge to practical problems. This is especially useful for those new to AI and machine learning.

Google Cloud Skills also offers specialized training on generative AI, covering topics like text-to-image synthesis and music generation. This is a great way to dive deeper into the field and explore its many applications.

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You can start learning the basics of Generative AI without spending a dime. Our no-cost Introduction to Generative AI course is a great place to begin, taking just about 45 minutes to finish.

This course is a fantastic starting point, and if you're eager to learn more, you can continue with the Generative AI Fundamentals skill badge. It includes the Introduction to Generative AI course, plus two other courses: 'Introduction to Large Language Models' and 'Introduction to Responsible AI', which will take about two hours to complete.

The Generative AI Fundamentals skill badge is a great way to validate your foundational understanding of Google Cloud generative AI technology, and you can share it on your resume and social channels to show off your new skills.

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To get started with generative AI, you can take the no-cost introductory course, "Introduction to Generative AI", which takes about 45 minutes to complete.

This course is a great place to start, and it's a prerequisite for the more advanced learning paths. You can also earn a digital skill badge by completing the Generative AI Fundamentals skill badge, which includes the introductory course, "Introduction to Large Language Models", and "Introduction to Responsible AI."

The Generative AI Fundamentals skill badge is a great way to validate your foundational understanding of Google Cloud generative AI technology, and it takes about two hours to complete.

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Generative AI is built on top of machine learning (ML) foundations, but what exactly is ML? It's the process of training algorithms to make predictions or decisions based on data. You can learn the basics of ML in Google Cloud's course, Introduction to AI and Machine Learning on Google Cloud, which covers the technologies, products, and tools used to build an ML model, pipeline, and project.

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This course is designed mainly for technical practitioners, like AI developers, data scientists, and ML engineers. It's a comprehensive learning experience that takes about six hours to complete and requires 12 credits. The course is made up of four primary modules, including labs and quizzes, that cover the AI development platform and how generative AI is embedded in it.

To get started with ML, you can begin with Google Cloud's labs, which offer hands-on experience with generative AI. For example, the Get Started with Generative AI Studio lab is an introductory, 60-minute lab that requires one credit. You can also explore the Vertex AI PaLM API: Qwik Start lab, which is another introductory, 60-minute lab that requires one credit.

Here are some key ML concepts to keep in mind:

These concepts are all covered in Google Cloud's Introduction to AI and Machine Learning on Google Cloud course, which is a great place to start your ML journey.

Text embeddings are a key component of generative AI, and they're being used in exciting new ways.

Andrew Ng, a globally recognized leader on the topic, has created content with DeepLearning.AI that teaches you how to use text embeddings with Vertex AI.

You can enroll for free in "Understanding and Applying Text Embeddings with Vertex AI" to learn tasks like classification, outlier detection, text clustering, and semantic search.

This course also covers how to integrate text embeddings into LLM applications.

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Foundational Models are large AI models that are pre-trained on a vast quantity of data. This allows them to be adapted or fine-tuned to a wide range of tasks. They're like a strong foundation that can be built upon.

These models are designed to be versatile and can be used for various applications, including text, code, images, audio, and video generation. According to example 3, Gen AI can take many data types and build a foundational model, making it a powerful tool for a wide range of tasks.

Foundational Models are often used as a starting point for more specialized models, which can be fine-tuned for specific tasks. However, this can be a time-consuming and costly process, as mentioned in example 5. That's why innovative approaches like parameter-efficient tuning are being developed to make it easier and more efficient to fine-tune these models.

Google Cloud Tools offer a range of features to help you get started with Generative AI. You can explore the model garden in Vertex AI, which includes foundational models.

To build both predictive and generative AI projects, Google Cloud provides a variety of tools and technologies. These include AI foundations, development, and solutions that encompass the data-to-AI life cycle.

Some key tools to know about include PaLM API, which lets you test and experiment with Google’s Large Language Models and GenAI tools. Maker Suite is also available, providing model training tools, a model deployment tool, and a model monitoring tool.

Here are some of the key features of these tools:

We've got two new generative AI learning paths to help you level up your skills. You can choose from a mix of courses, labs, and skill badges that are designed to give you a comprehensive learning experience.

Our introductory path is perfect for non-technical roles like sales, HR, marketing, and operations. It includes four video courses and a skill badge that cover the basics of generative AI.

Here's a breakdown of the content in our introductory path:

If you're a technical developer who works with generative AI, our second path is designed specifically for you. It includes a combination of technical hands-on labs and courses that require Google Cloud credits to complete. This path also includes the introductory training as a prerequisite.

Our generative AI for developers path includes eight courses, two labs, and a skill badge that cover topics like image generation, attention mechanisms, and transformer models.

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Google Cloud Tools offer a range of features and products that make it easy to build and deploy AI and machine learning projects. You can start by exploring the Vertex AI model garden, which includes foundational models.

Vertex AI is also part of Google Cloud's AI and machine learning offerings, which are covered in an introductory course that introduces the technologies, products, and tools available throughout the data-to-AI life cycle.

PaLM API lets you test and experiment with Google's Large Language Models and GenAI tools, and can be integrated with Maker Suite for a more graphical user interface.

Maker Suite includes tools like model training, deployment, and monitoring, making it a one-stop-shop for developers working with AI and machine learning.

Here are some key features of Google Cloud Tools:

Generative AI Studio supports language, vision, and speech, and provides a range of features and tools for designing and building AI applications.

With Generative AI Studio, you can design prompts for tasks, start conversations, and tune models to better equip them for your use case.

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Google Cloud's generative AI capabilities have opened up new possibilities for image and document generation. Diffusion models, a family of machine learning models, have recently shown promise in the image generation space.

These models draw inspiration from physics, specifically thermodynamics, and have become popular in both research and practical applications within the last few years.

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Machine Learning and MLOps is a crucial aspect of Generative AI. Google Cloud offers various courses and tools to help you master MLOps for Generative AI.

Vertex AI empowers AI teams to streamline MLOps by equipping them with the knowledge and tools needed to deploy and manage Generative AI models. This includes uncovering the unique challenges faced by MLOps teams and exploring how to overcome them.

Model evaluation is a critical discipline for ensuring that ML systems deliver reliable, accurate, and high-performing results. You can learn essential tools, techniques, and best practices for evaluating both generative and predictive AI models with Vertex AI: Model Evaluation course.

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Machine learning models come in different types, each designed to tackle specific tasks. One type is text-to-text models, which take natural language input and produce a text output. This is useful for applications like chatbots and language translation.

Text-to-text models are great for tasks that require generating text based on input. For example, a text-to-text model can take a prompt and generate a response. This is especially useful for applications that require generating human-like text, such as customer service chatbots.

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Another type of model is text-to-image, which takes a large set of captioned images and produces a video representation from the text input. This is useful for applications like video generation and animation.

Here are some examples of model types:

These model types are just a few examples of the many different types of machine learning models that exist. Each type has its own strengths and weaknesses, and is suited to specific tasks and applications.

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Model evaluation is a critical discipline for ensuring that ML systems deliver reliable, accurate, and high-performing results.

This is where Vertex AI comes in, equipping machine learning practitioners with the essential tools, techniques, and best practices for evaluating both generative and predictive AI models.

Vertex AI empowers AI teams to streamline MLOps, making it easier to manage and deploy Generative AI models.

Model evaluation is a critical step in the MLOps process, and with Vertex AI, you can ensure that your ML systems are delivering the best possible results.

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