This isn’t just another “intro to AI” course. It’s a deep dive into the real-world skills that set AI Engineers apart.

You’ll learn how to fine-tune open-source large language models models on custom data, teaching you the skills needed to do the same with a business' proprietary or private data.

Plus you'll conduct fine-tuning using QLoRA - a game-changing technique that drastically cuts resource usage.

But you won’t stop there. Businesses need more from their LLMs. They demand more.

You’ll deploy your own custom LLMs using AWS tools like SageMaker, Lambda, and API Gateway, as well as Streamlit for the creating an easy-to-use user interface for the business' employees or customers.

Along the way, you’ll master concepts like bfloat16 training, dataset chunking, attention masks, HuggingFace’s Estimator API, and much more.

From theory to hands-on coding, this course gives you the full experience of building truly production-ready AI.

What Careers Does This Course Prepare Me For?

AI and machine learning are so hot right now. If you want to catch and ride the AI wave, customizing LLMs for business use cases is a great place to start. It's a skill that's used in a ton of in-demand careers that are at the forefront of Artificial Intelligence including:

AI Engineer & Machine Learning Engineer: Focuses on designing, developing, and customizing machine learning models and deploying them to production environments. Requires skills in model training, optimization, and deployment.

AI Specialist: Specializes in building applications using artificial intelligence technologies and machine learning models.

Data Scientist: Involves analyzing and interpreting complex data to help companies make informed decisions. Requires expertise in data preparation, exploratory data analysis, and model building.

AI Research Scientist: Conducts research to advance the field of artificial intelligence and machine learning. Requires deep understanding of advanced machine learning concepts, including attention mechanisms and large language models.

Cloud Engineer: Focuses on designing, planning, managing, maintaining, and supporting cloud computing applications. Requires knowledge of AWS services and best practices for cloud deployment.

DevOps Engineer: Bridges the gap between development and operations by automating the process of software delivery and infrastructure changes. Needs skills in deploying and monitoring machine learning models using tools like AWS CloudWatch.

Software Engineer: Involves developing software applications, including those with integrated machine learning components. Requires understanding of integrating machine learning models into applications and ensuring their scalability and performance.

Data Engineer: Focuses on building and maintaining data pipelines, ensuring data is clean, reliable, and ready for analysis. Requires knowledge of data storage solutions like AWS S3 and data preparation techniques.

Technical Product Manager: Manages the development and deployment of technology products, including those involving machine learning. Requires an understanding of the technical aspects of machine learning deployment and monitoring.

What Else Should I Know?

By becoming a ZTM member you'll not only get access to all our bootcamp courses, bytes, and projects.

But you’ll also get to join our exclusive live online community classroom to learn alongside thousands of students, alumni, mentors, TAs and Instructors.

Most importantly, you'll be learning from an industry professional (Patrik) that has actual real-world experience as an AI & Machine Learning Engineer. He teaches you the exact strategies and techniques he uses in his role.

Finally, as with all ZTM courses, this course is a living thing. It will be constantly updated as the landscape changes so you can use it as your go-to guide for using Amazon SageMaker now and throughout your career.

Join 1,000s of Zero To Mastery graduates that have gotten hired and are now working at companies like Google, Tesla, Amazon, Apple, IBM, JP Morgan, Facebook, Shopify + other top tech companies.

They come from all different backgrounds, ages, and experiences. Many even started as complete beginners.

So there's no reason it can't be you too.

And you have nothing to lose. Because you can start learning right now and if this course isn't everything you expected, we'll refund you 100% within 30 days. No hassles and no questions asked.