Generative AI and Model Selection from Coursera

Dive into the world of generative AI and learn how to select the right model for your needs in this practical course. You'll gain a solid understanding of how generative AI models work and compare deployment options like web APIs, hosted solutions, and local installations.

By the end of this course, you will be able to:

• Describe the basic architecture of generative AI models

• Compare different AI model deployment options

• Evaluate AI models using benchmarks and custom assessments

• Troubleshoot and improve model performance

• Determine when to use in-context learning vs. retrieval augmented generation

Through hands-on exercises, you'll learn to evaluate models using industry benchmarks and create custom assessments for your specific use cases. You'll also master techniques to troubleshoot and enhance model performance.

What sets this course apart is its focus on real-world application - you'll leave equipped to make informed decisions about AI model selection and optimization for your projects. Whether you're new to AI or looking to deepen your knowledge, this course will empower you to leverage generative AI effectively.

What's inside

Syllabus

Lesson 1 Course and Instructor Introduction

Meet Professor Jesse Spencer-Smith, an experienced practitioner in the field of artificial intelligence. Learn about the course structure and its significance in today's AI-driven world. This lesson sets the foundation for understanding the critical role of model selection in AI implementation and introduces the key concepts you'll master throughout the course.

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Explores deployment options like web APIs, hosted solutions, and local installations, which are essential for practical application

Teaches how to evaluate models using industry benchmarks and custom assessments, which is crucial for real-world projects

Covers prompt engineering, in-context learning, and data augmentation techniques, which are useful for optimizing model performance

Examines Retrieval Augmented Generation (RAG) and its advantages and limitations compared to long-context models, which is relevant for advanced AI applications

Taught by Professor Spencer-Smith, an experienced practitioner in the field of artificial intelligence, which may add credibility to the course

Requires learners to understand the trade-offs in terms of security, cost, and customizability, which may require additional research

Reviews summary

Practical generative ai model selection guide

According to learners, this course offers a practical and highly useful foundation for selecting and evaluating generative AI models. Many found the instructor, Professor Spencer-Smith, to be clear, knowledgeable, and engaging, making complex topics accessible. Students particularly valued the course's focus on real-world applications and the sections covering benchmarking, custom assessments, and different deployment options. While providing a solid introduction, a few students felt certain areas, like troubleshooting or advanced techniques, could use more depth and would require supplemental study for those seeking expert-level knowledge. Overall, it's considered a strong course for professionals looking to gain confidence in AI model selection.

Useful insights on deployment & RAG.

"The content on RAG vs long-context models was insightful."

"I especially appreciated the comparison of different deployment strategies."

"The lecture content is informative, especially on deployment options and RAG."

"I learned about comparing deployment options like web APIs, hosted solutions, and local installations."

Strong focus on model evaluation skills.

"The section on benchmarking and creating custom assessments was particularly useful..."

"The hands-on exercises on evaluation were good..."

"The discussion on benchmarking limitations was very helpful."

"I learned how to evaluate models using industry benchmarks and create custom assessments for my specific use cases."

Clear, knowledgeable, and engaging instructor.

"Professor Spencer-Smith explains complex topics clearly and provides practical examples."

"Professor Spencer-Smith is knowledgeable and presents the information in an engaging way."

"Professor Spencer-Smith is an engaging speaker with deep knowledge."

"The instructor is clear."

Directly applicable for real-world use.

"The section on benchmarking and creating custom assessments was particularly useful for my work."

"This course provided me with the practical tools to evaluate and select models confidently."

"The course content is practical and directly applicable."

"I learned how to make informed decisions about AI model selection and optimization for my projects."

Hands-on activities were mixed.

"The labs were hands-on and reinforced the concepts effectively."

"The hands-on exercises on evaluation were good, but could have benefited from more detailed instructions for beginners."

"More hands-on coding exercises beyond just evaluation would have been beneficial."

"The labs sometimes felt a bit buggy or the instructions weren't perfectly aligned."

Good start, but needs supplementation.

"I felt it was a bit too theoretical in parts."

"Disappointed with the lack of depth... doesn't go deep enough..."

"Felt like a high-level overview rather than a practical guide."

"It introduces model selection but doesn't give you everything you need to become an expert."

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 Generative AI and Model Selection with these activities:

Review Deep Learning Fundamentals

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Refresh your understanding of deep learning concepts to better grasp the architecture of generative AI models.

Browse courses on Deep Learning

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Review online resources on neural networks.
Practice building simple neural networks with a framework like TensorFlow or PyTorch.

Read 'Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow' by Aurélien Géron

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Review machine learning fundamentals to provide a stronger foundation for generative AI.

View Hands-On Machine Learning with Scikit-Learn,... on Amazon

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Obtain a copy of 'Hands-On Machine Learning'.
Focus on the chapters covering neural networks and deep learning.
Work through the code examples to gain practical experience.

Read 'Generative Deep Learning' by David Foster

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Gain a deeper understanding of generative models by studying a dedicated book on the topic.

View Generative Deep Learning 2nd Edition... on Amazon

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Obtain a copy of 'Generative Deep Learning'.
Read the chapters relevant to the models discussed in the course.
Experiment with the code examples provided in the book.

Four other activities

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Show all seven activities

Benchmark Different Models on a Custom Dataset

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Improve your model evaluation skills by benchmarking different models on a dataset you create.

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Create a custom dataset relevant to your interests.
Select several generative AI models to benchmark.
Implement the benchmarking process and record the results.
Analyze the results and draw conclusions about model performance.

Build a Simple Generative Model

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Solidify your understanding of generative AI by building a simple model from scratch.

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Choose a generative model architecture (e.g., GAN, VAE).
Gather a dataset for training your model.
Implement the model using a deep learning framework.
Train and evaluate the model's performance.

Write a Blog Post on Model Selection

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Reinforce your learning by explaining the process of model selection in a blog post.

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Research different model selection techniques.
Outline the key steps in the model selection process.
Write a clear and concise blog post explaining the concepts.
Publish the blog post on a platform like Medium or your personal website.

Follow Tutorials on Retrieval Augmented Generation (RAG)

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Deepen your understanding of RAG by following online tutorials and implementing it yourself.

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Search for tutorials on RAG using platforms like YouTube or blog posts.
Choose a tutorial that aligns with your skill level and interests.
Follow the tutorial step-by-step and implement RAG in a project.

Career center

Learners who complete Generative AI and Model Selection will develop knowledge and skills that may be useful to these careers:

Machine Learning Engineer

A Machine Learning Engineer builds and deploys AI models. This course directly addresses the crucial aspect of selecting the right generative AI model for a given task. The course's focus on comparing different deployment options, like web APIs versus local installations, also helps a machine learning engineer make informed decisions. The ability to evaluate models using benchmarks and enhance their performance, as taught in this course, are essential in the day-to-day tasks of a machine learning engineer. The course specifically teaches troubleshooting and improving model performance, which are vital for a machine learning engineer.

See salaries and explore the career path for Machine Learning Engineer

AI Product Manager

An AI Product Manager guides the development and launch of AI-powered products. This course helps an AI product manager make informed decisions about which generative AI models to incorporate into their products. Understanding model architecture and deployment options is valuable for anyone making high-level decisions about AI. The course's content on evaluating models using industry benchmarks and creating custom assessments enables an AI product manager to evaluate the suitability of a model for a specific product. The course also covers the use cases of in-context learning versus retrieval-augmented generation, which are key considerations for an AI product manager.

See salaries and explore the career path for AI Product Manager

AI Consultant

An AI Consultant advises organizations on how to best leverage AI technologies. This course provides a solid foundation required for an AI consultant to understand the nuances of selecting appropriate generative AI models and their deployment. The course's content on comparing model performance and understanding different deployment options informs the advice provided by an AI consultant. This course would help an AI consultant make recommendations that are pragmatic and aligned with the needs of a client. The skills to troubleshoot as well as enhance model performance, which are part of this course, further enhance the consultant's capabilities.

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Data Scientist

A Data Scientist uses data to solve complex problems, and AI models are key tools in that process. This course provides a perspective on the world of generative AI, and helps a data scientist choose the right model for a particular task. The focus on evaluating AI models, troubleshooting, and improving model performance all tie directly into the work of a data scientist. The content on benchmarks and custom assessments would be useful for data scientists as they evaluate their deployed solutions. The course's material on retrieval-augmented generation (RAG) and in-context learning also deepens a data scientist's understanding of AI.

See salaries and explore the career path for Data Scientist

AI Solutions Architect

An AI Solutions Architect designs and implements AI systems. This course helps an AI solutions architect by focusing on the selection and deployment of AI models. Understanding the trade-offs between different deployment options such as web APIs and local installations is especially useful to an AI solutions architect. The course's coverage of model evaluation using benchmarks is directly applicable to an AI solutions architect when making design decisions. In addition, the course includes the important skill of optimizing AI model performance so that the architect can make informed choices when designing practical AI solutions.

See salaries and explore the career path for AI Solutions Architect

Computational Linguist

A Computational Linguist develops and uses computational models to analyze and understand human language, a skill enhanced by artificial intelligence. This course enhances the work of a computational linguist by providing a technical understanding of the use of AI models. The course's discussion of model architecture and its variations, as well as the different deployment options, provides a good foundation. The ability to evaluate models and optimize performance enhances a computational linguist's capabilities and their understanding of how generative AI can apply to their work. This is also a good course for a computational linguist because it explores in-context learning.

See salaries and explore the career path for Computational Linguist

Research Scientist

A Research Scientist explores new frontiers in AI, often requiring them to select and use appropriate models. The knowledge from this course about model architecture and deployment options can be directly applied to the work of a research scientist. The course teaches how to evaluate AI models using benchmarks and to create custom assessments, which a research scientist would find useful. The hands-on exercises and emphasis on real-world application enhance a research scientist's ability to use state-of-the-art models. The strategies for improving model performance that are in this course are important to a research scientist, particularly for someone specializing in generative AI.

See salaries and explore the career path for Research Scientist

Software Engineer

A Software Engineer often integrates AI models into various applications, so understanding AI models is important. This course is useful for a software engineer, because it covers how to compare AI models. The course provides a good overview of the architecture, variations, and deployment options, which would be useful for a software engineer. The ability to evaluate models using benchmarks and to improve model performance are useful to a software engineer who wishes to build applications that leverage the power of generative AI. This course in particular would be helpful because it covers in-context learning and retrieval-augmented generation.

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Business Intelligence Analyst

A Business Intelligence Analyst analyzes data and derives insights to guide business decisions, and some of their work could be improved with AI. This course may be helpful to a business intelligence analyst. It provides an introduction to different AI models and their deployment options. The course touches on evaluating model performance and improving it, which are all relevant to using AI. This course would be useful as a starting point, and would enable the analyst to grasp the power of AI in the context of business data analysis. The course also provides the understanding of in-context learning and retrieval-augmented generation, which would be beneficial to the role.

See salaries and explore the career path for Business Intelligence Analyst

Technical Writer

A Technical Writer creates documentation for technical products, and AI tools are increasingly being used to support such writing. This course may be useful for a technical writer who wants to understand AI and the technical specifications of AI models. This will help a writer create better documentation. The course provides a good overview of model selection and deployment which would help a technical writer who is working on a document that includes information on AI. The course covers topics such as benchmarking, troubleshooting, and improving performance, which increase a technical writer's technical understanding.

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Project Manager

A Project Manager plans and manages projects, and an understanding of AI projects is becoming increasingly important. This course may be useful for a project manager who seeks to work on projects that involve the development and deployment of generative AI. The course covers model selection and deployment, and it provides insight into the challenges and opportunities associated with AI. The course introduces model evaluation and also optimization, which are useful topics for a project manager to be at least familiar with. This course would help a project manager understand their team, and to speak with them about generative AI.

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Startup Founder

A Startup Founder launches a new business, and increasingly those are based on leveraging AI. This course may be helpful to a startup founder, especially one who aims to build a company that leverages generative artificial intelligence. The course provides insight into AI model selection and evaluation, which are key to developing a technology startup. The course includes important skills for optimization that a startup founder needs to be aware of when planning their business. The course will help a startup founder better understand the technology choices that need to be made in their business plan.

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Academic Researcher

An Academic Researcher studies academic topics, and artificial intelligence is a growing area of study. This course may be useful for an academic researcher who seeks to study AI and generative models in particular. The course provides an introduction to model architecture and options for deployment. The course teaches model evaluation, which is helpful knowledge for an academic researcher to know. Furthermore, the course covers methods for improving model performance, a topic highly relevant to anyone working with generative AI in a research setting. This course can help build an academic researcher's knowledge of artificial intelligence.

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Policy Analyst

A Policy Analyst researches and recommends policies, and increasingly this involves technology, including AI. This course may be useful for a policy analyst to learn about AI model selection and deployment, especially if their work is focused on technology policy. The course provides an overview of model evaluation and optimization, which is helpful for a policy analyst who wants to understand the technical aspects of AI policies. The course will help a policy analyst better understand the technical dimensions of artificial intelligence, which is needed in order to create good policy. The course may be particularly useful for its insights into in-context learning and retrieval-augmented generation.

See salaries and explore the career path for Policy Analyst

Educator

An Educator teaches and trains others, and may be interested in adding generative AI to their curriculum. This course may be useful for an educator who seeks to gain a better understanding of AI, particularly generative models. The course focuses on model selection and deployment options. An educator may be interested in the model evaluation and optimization in the course. This course would provide a helpful introduction to generative artificial intelligence, which can inform how an educator might teach this subject and topic to others.

See salaries and explore the career path for Educator

Generative AI and Model Selection

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Reviews summary

Practical generative ai model selection guide

Activities

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