Models and Platforms for Generative AI from edX

This course is designed for enthusiasts and practitioners who share an interest in the rapidly advancing field of generative AI.

This course centers around the core concepts and generative AI models that form the building blocks of generative AI. You will delve into the concepts of deep learning and large language models (LLMs). You will learn about GANs, VAEs, transformers, and diffusion models – the fundamental components of generative AI.

You will learn about the concept of foundation models. You will gain insights into the capabilities of pre-trained models and platforms for AI application development. The course will also cover how foundation models utilize these platforms to generate text, images, and code. Additionally, participants will explore various generative AI platforms such as IBM watsonX and Hugging Face.

The course includes practical hands-on labs, offering participants the chance to delve into the applications of generative AI using the IBM Generative AI Classroom and platforms like IBM watsonX. Throughout the course, you'll have the opportunity to explore various models, including IBM Granite, OpenAI GPT, Google Flan, and Meta Llama. Additionally, expert practitioners will share insights into the capabilities, applications, and tools of generative AI.

What's inside

Learning objectives

Describe the fundamental concepts of generative ai.
Explore the building blocks of generative ai, including gans, vaes, transformers, and diffusion models.
Explain the concept of foundation models in generative ai.

Explore the ability of foundation models to generate text, images, and code using pre-trained models.
Describe the features, capabilities, and applications of different generative ai platforms, including ibm watsonx and hugging face.

Describe the fundamental concepts of generative ai.
Explore the building blocks of generative ai, including gans, vaes, transformers, and diffusion models.
Explain the concept of foundation models in generative ai.
Explore the ability of foundation models to generate text, images, and code using pre-trained models.
Describe the features, capabilities, and applications of different generative ai platforms, including ibm watsonx and hugging face.

Syllabus

Module 1: Models for Generative AI

Video: Course Introduction

Reading: Course Overview

Reading: Program Overview

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Taught by RAV Ahuja, an expert in the field of generative AI and deep learning

Suitable for those with a background in deep learning and machine learning

Covers advanced topics in generative AI, including foundation models and pre-trained models

Provides hands-on labs and access to platforms like IBM WatsonX and Hugging Face for practical experience

Shares insights from expert practitioners in the field

Students can explore various generative AI models, including IBM Granite, OpenAI GPT, Google Flan, and Meta Llama

Reviews summary

Generative ai models & platforms overview

According to learners, this course offers a comprehensive and practical introduction to generative AI. Students particularly value its structured approach to foundational models like LLMs, GANs, VAEs, and diffusion models. The inclusion of hands-on labs with IBM watsonX and the Generative AI Classroom is frequently highlighted as a major strength, providing valuable practical experience in developing AI applications for text, image, and code generation. While some might find the emphasis on IBM platforms and models like IBM Granite notable, it provides a solid industry-relevant perspective. Overall, it's seen as a highly valuable stepping stone for both enthusiasts and practitioners entering or advancing in the field.

Offers good exposure to industry-standard platforms, with an IBM emphasis.

"The insights into IBM watsonx.ai and Hugging Face are directly applicable to real-world scenarios and modern AI development."

"I found the focus on IBM Granite models very useful for understanding enterprise-level AI solutions."

"While it leans towards IBM tools, it provides a deep dive into how large companies are deploying generative AI."

Covers highly current topics in a rapidly evolving technological field.

"The course materials feel very up-to-date with the latest trends and advancements in generative AI."

"It's impressive how quickly and thoroughly they covered such a dynamic and fast-moving area of artificial intelligence."

"Gave me a clear picture of the current state of generative AI and where it's headed in the near future."

Well-structured for new learners in AI, requiring dedicated engagement.

"As an enthusiast new to generative AI, I found the course surprisingly accessible without sacrificing necessary depth."

"It's a great starting point for understanding complex AI concepts, explained in a clear and logical manner."

"While the pacing is generally good, you need to commit time to truly grasp the material and complete all the labs effectively."

Offers valuable practical experience using leading generative AI platforms.

"The labs on IBM watsonx and Generative AI Classroom were excellent for applying what I learned theoretically."

"I gained hands-on experience generating text, images, and code, which is invaluable for my career development."

"The opportunity to work with models like IBM Granite, OpenAI GPT, and Llama was a big plus for practical skills."

Provides comprehensive coverage of fundamental generative AI concepts.

"I really appreciated the clear explanations of deep learning, LLMs, and the different generative models like GANs and diffusion models."

"The course laid a solid groundwork on foundation models, making complex topics digestible and easy to understand."

"It helped me understand the building blocks of generative AI, which is crucial for anyone starting in this field."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Models and Platforms for Generative AI with these activities:

Review Deep Learning, Natural Language Processing, and Machine Learning

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Revisiting knowledge of these topics may help prepare you for the core learning objectives.

Browse courses on Deep Learning

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Review and summarize key concepts from deep learning
Familiarize yourself with natural language processing (NLP) techniques
Brush up on machine learning algorithms

Explore Generative AI Toolkits and Platforms

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Expand your knowledge by exploring different generative AI toolkits and platforms.

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Research and identify different generative AI toolkits and platforms
Explore the documentation and tutorials of these platforms

Join Study Group or Peer Discussion

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Connect with other students and challenge your understanding of course concepts through discussions.

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Identify or create a study group or discussion forum
Participate actively in group discussions and knowledge sharing

Three other activities

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Complete Hands-on Labs and Practice Exercises

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Reinforce your understanding of the concepts by working on the course's hands-on exercises and labs.

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Work through the hands-on labs in the course
Complete the practice exercises provided in the course material

Compile Resources on Generative AI

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Organize and share resources to enhance your understanding of generative AI.

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Gather high-quality articles, tutorials, and code samples on generative AI
Organize these resources into a coherent collection
Share your compilation with other students or the wider community

Build a Generative AI Model

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Apply your knowledge by building a generative AI model of your own.

Browse courses on Generative AI Models

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Choose a generative AI model type (e.g., GAN, VAE, transformer, diffusion model)
Gather and prepare your data
Implement the model architecture
Train and fine-tune your model
Evaluate the performance of your model
Document your work and findings in a report

Career center

Learners who complete Models and Platforms for Generative AI will develop knowledge and skills that may be useful to these careers:

AI Researcher

AI Researchers explore new and emerging AI technologies and applications. This course provides AI Researchers with the knowledge and skills necessary to understand and apply generative AI models to real-world problems, contributing to the advancement of AI research and development.

See salaries and explore the career path for AI Researcher

Natural Language Processing Engineer

Natural Language Processing Engineers develop and deploy AI models that can understand and generate human language. This course provides the necessary knowledge and skills to apply generative AI models to NLP tasks, such as text generation, language translation, and sentiment analysis.

See salaries and explore the career path for Natural Language Processing Engineer

Computer Vision Engineer

Computer Vision Engineers develop and deploy AI models that can analyze and interpret images and videos. This course provides a strong foundation in generative AI models that can be used to create computer vision applications for object detection, image segmentation, and facial recognition.

See salaries and explore the career path for Computer Vision Engineer

Machine Learning Engineer

Machine Learning Engineers design and implement machine learning models to solve real-world problems. This course can help Machine Learning Engineers build a foundation in generative AI models and gain experience in using them to develop innovative AI applications.

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Data Scientist

Data Scientists are responsible for collecting, analyzing, and interpreting large and complex data sets. This course would be useful for Data Scientists as it provides the foundation and hands-on experience necessary to understand and apply generative AI models to data analysis and decision-making.

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Software Engineer

Software Engineers design, develop, and maintain software applications. This course provides Software Engineers with the foundation and practical experience necessary to incorporate generative AI models into their software applications, enhancing their functionality and user experience.

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Data Analyst

Data Analysts collect, analyze, and interpret data to provide insights and recommendations. This course may be useful for Data Analysts who want to stay updated on the latest trends in generative AI and learn how to apply these models to improve their data analysis and decision-making.

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Business Analyst

Business Analysts analyze business processes and recommend solutions to improve efficiency and effectiveness. This course may be helpful for Business Analysts who want to gain a foundational understanding of generative AI and how it can be used to improve business outcomes.

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Product Manager

Product Managers define and manage the development and launch of new products. This course may be useful for Product Managers who want to learn about the potential applications of generative AI and how it can be used to enhance product development and innovation.

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Marketing Manager

Marketing Managers develop and execute marketing campaigns to promote products and services. This course may be useful for Marketing Managers who want to learn about the use of generative AI in marketing to create personalized content, automate marketing tasks, and optimize marketing campaigns.

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UX Designer

UX Designers design and enhance the user experience of products and services. This course may be useful for UX Designers who want to explore the potential of generative AI to improve the user experience through personalized interactions, content generation, and user interface design.

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Content Writer

Content Writers create and edit written content for various purposes. This course may be useful for Content Writers who want to learn about the use of generative AI in content creation to generate ideas, assist with writing, and improve content quality and engagement.

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Technical Writer

Technical Writers create and edit technical documentation for software, hardware, and other technical products. This course may be useful for Technical Writers who want to learn about the use of generative AI to automate documentation generation, improve documentation quality, and enhance the user experience.

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Instructional Designer

Instructional Designers design and develop educational materials and learning experiences. This course may be useful for Instructional Designers who want to explore the use of generative AI to create personalized learning experiences, develop interactive simulations, and enhance the overall effectiveness of training and education.

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Customer Success Manager

Customer Success Managers build and maintain relationships with customers to ensure their satisfaction and success. This course may be useful for Customer Success Managers who want to learn about the use of generative AI to improve customer engagement, provide personalized support, and enhance the customer experience.

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