This course introduces the AI and machine learning (ML) offerings on Google Cloud that build both predictive and generative AI projects.
It explores the technologies, products, and tools available throughout the data-to-AI life cycle, encompassing AI foundations, development, and solutions. It aims to help data scientists, AI developers, and ML engineers enhance their skills and knowledge through engaging learning experiences and practical hands-on exercises.
This course is designed for business users, business leaders, and, more generally, decision makers within organizations who are seeking to gain a comprehensive understanding of generative AI and its potential impact on their businesses.
The overall goal of the course is to educate decision makers on the potential impact generative AI can have in their organizations, educate them about generative AI services on Google Cloud and to inspire decision makers to start thinking about what is possible with generative AI technologies.
As generative AI becomes more common, the ability to interact with large language models is shifting from niche knowledge to a necessary skill across many different industries and roles.
In this course, you will learn the fundamentals of prompting large language models and exploring further techniques for improving the output from large language models. You will also explore similar concepts when working with multimodal models such as Gemini Vision Pro.
In this course, you explore generative AI (Gen AI) use cases across specific industries using Google Cloud.
After discussing generative AI in general, you will do a deep dive into two generative AI use cases and then explore your own use case within the same framework. This course targets professionals across different roles, including business analysts, data scientists, software developers, business users, and decision-makers, who are specifically interested in leveraging generative AI for business solutions.
In this course, you explore tools and APIs available on Google Cloud for integrating large language models (LLMs) into your application.
After exploring generative AI options on Google Cloud, you explore LLMs and prompt design in Vertex AI Studio. Then you learn about LangChain, an open-source framework for developing applications powered by language models.
After a discussion around more advanced prompt engineering techniques, you put it all together to build a multi-turn chat application by using LangChain and the Vertex AI Gemini API.
Vertex AI Agent Builder lets developers, even those with limited machine learning skills, tap into the power of Google’s foundation models, search expertise, and conversational AI technologies to create enterprise-grade generative AI applications.
In this course, you learn how to use Vertex AI Agent Builder to create search engines and chat applications. You will then explore how to integrate these search engineers and chat applications into your own applications.
Finally, you learn how to manage the tools built in Vertex AI Agent Builder in production.
Generative AI is now at the center of transforming how software is designed, built, run, and managed. For developers, generative AI is a powerful tool for making coding more efficient and using APIs, such as the Gemini and PaLM APIs, within their applications.
In this course, you are introduced to how generative AI can be used to make developers more efficient at writing code and implementing new features into applications. You will also explore available models in Vertex AI Model Garden.
In this course, you learn about the internals of BigQuery and best practices for designing, optimizing, and administering your data warehouse. Through a combination of lectures, demos, and labs, you learn about BigQuery architecture and how to design optimal storage and schemas for data ingestion and changes.
Next, you learn techniques to improve read performance, optimize queries, manage workloads, and use logging and monitoring tools. You also learn about the different pricing models.
Finally, you learn various methods to secure data, automate workloads, and build machine learning models with BigQuery ML.
This course introduces you to fundamentals, practices, capabilities and tools applicable to modern cloud-native application development using Google Cloud Run.
Through a combination of lectures, hands-on labs, and supplemental materials, you will learn how to design, implement, deploy, secure, manage, and scale applications on Google Cloud using Cloud Run.
In this course, you will take a journey from a broad overview of gen AI to understanding how to leverage gen AI and Google Cloud for organizational transformation.
Upskill in Generative AI with 30% off – limited-time price: RM3360
G Suite just got better — introducing Google Workspace. Content delivered by our partners may still include some references to G Suite as updates are being made but you will still receive relevant training. If you’re a Google Workspace admin who needs to manage and establish Google Workspace best practices for your organization, the lectures and hands-on labs in this course will show you how to use the features in the admin console so that you can manage users, control access to services, configure security settings, monitor Google Workspace operations, and more
In this course, you learn about data engineering on Google Cloud, the roles and responsibilities of data engineers, and how those map to offerings provided by Google Cloud. You also learn about ways to address data engineering challenges.
Upskill in introduction to data engineering with 30% off – limited-time price: RM1680
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