Generative AI Engineering and Fine-Tuning Transformers from Coursera

The demand for technical gen AI skills is exploding. Businesses are hunting hard for AI engineers who can work with large language models (LLMs). This Generative AI Engineering and Fine-Tuning Transformers course builds job-ready skills that will power your AI career forward.

During this course, you’ll explore transformers, model frameworks, and platforms such as Hugging Face and PyTorch. You’ll begin with a general framework for optimizing LLMs and quickly move on to fine-tuning generative AI models. Plus, you’ll learn about parameter-efficient fine-tuning (PEFT), low-rank adaptation (LoRA), quantized low-rank adaptation (QLoRA), and prompting.

Additionally, you’ll get valuable hands-on experience in online labs that you can talk about in interviews, including loading, pretraining, and fine-tuning models with Hugging Face and PyTorch.

If you’re keen to take your AI career to the next level and boost your resume with in-demand gen AI competencies that catch the eye of an employer, ENROLL today and have job-ready skills you can use straight away within a week!

What's inside

Syllabus

Transformers and Fine-Tuning

In this module, you will be introduced to Fine Tuning. You’ll get an overview of generative models and compare Hugging Face and PyTorch frameworks. You’ll also gain insights into model quantization and learn to use pre-trained transformers and then fine-tune them using Hugging Face and PyTorch.

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Explores transformers, model frameworks, and platforms like Hugging Face and PyTorch, which are essential tools for AI engineers working with large language models

Provides hands-on experience in online labs, allowing learners to load, pretrain, and fine-tune models with Hugging Face and PyTorch, which are valuable skills to showcase in interviews

Covers parameter-efficient fine-tuning (PEFT), low-rank adaptation (LoRA), and quantized low-rank adaptation (QLoRA), which are current techniques for optimizing large language models

Presented by IBM, a company recognized for its contributions to artificial intelligence and machine learning technologies, which may add credibility to the course content

Reviews summary

Practical fine-tuning for generative ai

According to learners, this course offers a highly relevant and practical introduction to fine-tuning large language models, focusing on key techniques like PEFT, LoRA, and QLoRA. Students particularly appreciate the hands-on labs using Hugging Face and PyTorch, which provide valuable coding experience. While many find the content valuable for gaining job-ready skills in the rapidly evolving field of generative AI, some note that the course assumes a significant level of prior knowledge in machine learning and Python, and the labs can be challenging without this background. Overall, it is seen as a solid foundation but may require supplemental learning for true mastery or navigating the fast-changing ecosystem.

Provides a good starting point, not deep dive

"It gives a very good overview of the key concepts in LLM fine-tuning."

"I feel like I have a solid foundation now, but I know I need to learn more to be truly proficient."

"The course introduces the topics well but doesn't go into exhaustive detail on every aspect."

Covers essential, in-demand generative AI skills

"This course covers exactly the techniques like LoRA and QLoRA that are critical right now in generative AI."

"The focus on fine-tuning and PEFT is very relevant for anyone working with LLMs."

"Learning about transformers and different fine-tuning methods is crucial for this field, and this course delivers."

Excellent hands-on practice with key tools

"The hands-on labs were incredibly useful; actually using Hugging Face and PyTorch made the concepts stick."

"I really liked the practical coding exercises. It wasn't just theory, we got to work with models."

"The labs give you real experience you can talk about in job interviews."

Content can be demanding, especially labs

"Some sections were quite challenging to grasp, particularly the detailed explanations of model architecture."

"I struggled with some of the lab setups and debugging without prior experience."

"The course is not for complete beginners; it requires significant effort to keep up."

Assumes solid ML/Python background

"Definitely need a strong background in Python and basic machine learning before taking this."

"If you're not already comfortable with PyTorch or similar frameworks, the labs can be a steep learning curve."

"The course moves quickly and doesn't spend much time on prerequisites, so come prepared."

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 Engineering and Fine-Tuning Transformers with these activities:

Review Deep Learning Fundamentals

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Solidify your understanding of deep learning concepts, which are foundational to understanding transformers and fine-tuning techniques.

Browse courses on Deep Learning

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Review key concepts like neural networks and backpropagation.
Work through online tutorials or practice problems.

Read 'Attention is All You Need' paper

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Understand the original Transformer paper to gain a deeper understanding of the architecture and its underlying principles.

View Putting Knowledge to Work on Amazon

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Download and read the 'Attention is All You Need' paper.
Take notes on key concepts and architecture details.
Research any unfamiliar terms or concepts.

Follow Hugging Face Tutorials

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Gain practical experience with Hugging Face by working through their official tutorials on loading, pretraining, and fine-tuning models.

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Visit the Hugging Face website and navigate to their tutorials section.
Select tutorials related to model loading, pretraining, and fine-tuning.
Follow the tutorials step-by-step, running the code examples.

Four other activities

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Read 'Natural Language Processing with Transformers'

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Expand your knowledge of Transformers and their applications in NLP by reading this comprehensive book.

View Natural Language Processing with Transformers,... on Amazon

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Obtain a copy of 'Natural Language Processing with Transformers'.
Read the book, focusing on chapters relevant to fine-tuning and generative AI.
Experiment with the code examples provided in the book.

Experiment with LoRA on a small dataset

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Practice implementing LoRA (Low-Rank Adaptation) on a small dataset to understand its impact on model performance and efficiency.

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Choose a small dataset suitable for fine-tuning.
Implement LoRA using a framework like Hugging Face or PyTorch.
Train the model with LoRA and compare the results to training without LoRA.

Fine-tune a Transformer for Text Generation

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Apply your knowledge by fine-tuning a transformer model for a specific text generation task, such as generating creative writing or summarizing articles.

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Select a transformer model and a text generation task.
Prepare the dataset and fine-tune the model using appropriate techniques.
Evaluate the model's performance and iterate on the fine-tuning process.

Write a Blog Post on PEFT Techniques

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Solidify your understanding of Parameter Efficient Fine-Tuning (PEFT) by writing a blog post explaining the different techniques and their benefits.

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Research different PEFT techniques like LoRA and QLoRA.
Write a blog post explaining the concepts and providing examples.
Publish the blog post on a platform like Medium or your personal website.

Career center

Learners who complete Generative AI Engineering and Fine-Tuning Transformers will develop knowledge and skills that may be useful to these careers:

Machine Learning Engineer

A Machine Learning Engineer builds and deploys machine learning models, and this course is directly relevant to this role. This course provides hands-on experience with transformers, Hugging Face, and PyTorch, which are essential tools and frameworks used in developing modern machine learning applications. The content helps build a foundation in fine-tuning large language models, which is an increasingly important skill for machine learning engineers. This course provides practical skills and experience in model optimization, parameter-efficient fine-tuning, and prompting, which are all pertinent to the work of a machine learning engineer. If you are looking to become a machine learning engineer, this course will be immediately beneficial to your goals.

See salaries and explore the career path for Machine Learning Engineer

Artificial Intelligence Engineer

An Artificial Intelligence Engineer designs and implements AI solutions and this course will be highly beneficial to them. The course focuses on using technologies such as transformers, Hugging Face, and PyTorch, which are fundamental for creating AI applications. It provides training in fine-tuning generative AI models, including parameter-efficient methods like LoRA and QLoRA. This course provides the training to load, pretrain, and fine-tune models, which is core to an artificial intelligence engineer role. Any person seeking to enter the field of artificial intelligence engineering would gain practical and relevant experience from this course.

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Natural Language Processing Engineer

A Natural Language Processing Engineer creates systems that allow computers to understand and process human language and they would find this course to be very relevant. Working with transformers, Hugging Face, and PyTorch, are at the heart of this kind of work and these are covered by the course. The focus on fine-tuning large language models will provide direct applicable skills. Additionally, parameter efficient fine-tuning techniques such as LoRA and QLoRA will offer useful techniques. A natural language processing engineer would find the practical hands-on experience with loading, pre-training, and fine-tuning models to be essential.

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

Deep Learning Engineer

A Deep Learning Engineer develops and implements deep learning models, and this course will prepare them. The course focuses on transformers, model frameworks, and platforms such as Hugging Face and PyTorch, which are central to deep learning. This course provides useful knowledge on fine-tuning generative AI models and offers an introduction to parameter efficient methods such as LoRA and QLoRA. Deep learning engineers will find that the hands-on experience in online labs, which include loading, pretraining, and fine-tuning models, is extremely useful. Any aspiring deep learning engineer should consider this course.

See salaries and explore the career path for Deep Learning Engineer

Generative AI Specialist

A Generative AI Specialist focuses on developing and implementing generative AI models, and this course is directly applicable to their role. The course provides essential knowledge about transformers and model frameworks like Hugging Face and PyTorch. This course helps a generative AI specialist develop skills in fine-tuning models, which is a core task. The material on parameter efficient fine-tuning (PEFT) is particularly pertinent. Hands-on experience with model loading, pretraining, and fine-tuning is extremely valuable to anyone looking to transition into a role as a generative AI specialist.

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AI Research Scientist

An AI Research Scientist explores and develops new AI models and algorithms, and this course may prove valuable to their work. This course explores the practical aspects of working with generative AI models using transformers, Hugging Face, and PyTorch frameworks. This course will help build a foundation in fine-tuning LLMs and introduce parameter efficient methods and prompt engineering. While research often requires an advanced degree, this course may assist an AI research scientist by providing hands-on skills with the technologies and techniques they would need to work with.

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Data Scientist

A Data Scientist analyzes data and develops models to extract insights and this course may be helpful. The knowledge of transformers, Hugging Face, and PyTorch gained in this course will aid in the development of machine-learning based models. This course also helps build a foundation in fine-tuning large language models, a skill that may prove to be useful in numerous data science roles. Additionally, the focus on parameter-efficient fine-tuning will impart valuable techniques for using these models effectively. Data scientists would benefit from the experience of working with models in online labs, where loading, pretraining, and fine-tuning takes place.

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Computational Linguist

A Computational Linguist develops computational models of human language and this course may be a good fit. This course introduces foundational tools such as transformers, Hugging Face, and PyTorch, which are used in the field. The course provides the opportunity to gain experience in fine-tuning large language models. The course's focus on parameter-efficient fine-tuning techniques and prompting may prove to be useful. The hands-on experience with loading, pretraining, and fine-tuning models can be very beneficial to a computational linguist.

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Software Developer

A Software Developer writes code to build applications, and this course may be helpful to their practice. The course provides an introduction to transformer models, Hugging Face, and PyTorch frameworks. This course helps build a foundation in optimizing and fine-tuning large language models. In addition, the modules on parameter-efficient fine-tuning with LoRA and QLoRA may prove useful. The hands-on experience in labs with model loading, pretraining, and fine-tuning may also be valuable experience to a software developer.

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Solutions Architect

A Solutions Architect designs technical solutions to meet business needs, and this course may be helpful in certain kinds of work. This course introduces relevant technologies in the field of AI, such as transformers, Hugging Face, and PyTorch. The course provides an introduction to model fine-tuning, and parameter-efficient fine-tuning techniques. A solutions architect would find that these learnings would be useful in designing solutions that incorporate generative AI. Hands-on experience, such as model loading, pretraining, and fine-tuning may also be useful.

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Research Engineer

A Research Engineer applies engineering principles to research problems, and this course may be relevant for certain types of research. This course introduces hands-on experience with transformers and frameworks such as Hugging Face and PyTorch. Coursework introduces the concepts of fine-tuning and parameter-efficient fine-tuning. The material in this course may prove useful in conducting applied research in artificial intelligence. A research engineer would find the online labs particularly beneficial for practicing with loading, pretraining, and fine-tuning models.

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Data Analyst

A Data Analyst examines data to identify trends and insights, and this course may be helpful for specific projects. While the core of the course is not directly related to a data analyst's daily tasks, the introduction to transformer models, Hugging Face, and PyTorch might be valuable. The concepts of fine-tuning models may also be useful in certain contexts. A data analyst may find the hands-on experience working with machine learning models useful in certain kinds of analysis. These skills, while not essential, may nevertheless prove to be useful.

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Technical Consultant

A Technical Consultant advises clients on technology solutions, and this course may prove helpful in some specific projects. The course introduces the technology of machine learning, through a study of transformers, Hugging Face, and PyTorch. In addition, the course touches on optimizing large language models. This course may be helpful to a technical consultant that works with clients that make use of AI applications. Having a basic understanding of model fine-tuning can also be beneficial.

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Business Intelligence Analyst

A Business Intelligence Analyst analyzes data to help make business decisions, and this course may be tangentially relevant. The course introduces concepts and technologies such as transformers, Hugging Face, and PyTorch, which are typically not used in business intelligence analysis. The course's learning objectives are very different than those of a business intelligence analyst. A business intelligence analyst would not find this course to be immediately useful, although in specific circumstances some of the topics may be applicable.

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Project Manager

A Project Manager organizes and manages projects, and this course is unlikely to be helpful to their career. This course introduces concepts and technologies such as transformers, Hugging Face, and PyTorch. The content and skills taught in this course are not directly relevant nor needed to manage a project. This course is not recommended for a project manager unless they are seeking a career change and not advancement in their existing one. A project manager would likely find this course to be irrelevant.

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Generative AI Engineering and Fine-Tuning Transformers

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Reviews summary

Practical fine-tuning for generative ai

Activities

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