Databricks to Local LLMs
Master Databricks for data engineering and explore local LLMs like Mixtral. Perfect for ML practitioners.
Master Databricks for data engineering and explore local LLMs like Mixtral. Perfect for ML practitioners.
This course cannot be purchased separately - to access the complete learning experience, graded assignments, and earn certificates, you'll need to enroll in the full Large Language Model Operations (LLMOps) Specialization program. You can audit this specific course for free to explore the content, which includes access to course materials and lectures. This allows you to learn at your own pace without any financial commitment.
Instructors:
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What you'll learn
Master Databricks for data engineering and ML workloads
Create and manage data transformation pipelines
Implement Delta Lake and Unity Catalog solutions
Deploy local LLMs using Llamafile and Mixtral
Apply responsible AI practices in production
Develop practical MLOps skills
Skills you'll gain
This course includes:
3.8 Hours PreRecorded video
15 quizzes
Access on Mobile, Tablet, Desktop
FullTime access
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There are 4 modules in this course
This comprehensive course teaches learners to master Databricks for data engineering and data analytics workflows. Students learn the Databricks Lakehouse Platform, create ML pipelines, and work with local large language models. The curriculum covers data transformation, Delta Lake pipelines, responsible AI deployment, and practical implementation of local LLMs using tools like Mixtral, Hugging Face Candle, and Mozilla llamafile.
Databricks Lakehouse Platform Fundamentals
Module 1 · 7 Hours to complete
Data Transformation and Pipelines
Module 2 · 7 Hours to complete
Responsible Generative AI
Module 3 · 8 Hours to complete
Local LLMOps
Module 4 · 3 Hours to complete
Fee Structure
Instructors
Executive in Residence and Founder of Pragmatic AI Labs at Duke University
Noah Gift is the founder of Pragmatic AI Labs and serves as an Executive in Residence at Duke University, where he lectures in the Master of Interdisciplinary Data Science (MIDS) program. He specializes in designing and teaching graduate-level courses on machine learning, MLOps, artificial intelligence, and data science, while also consulting on machine learning and cloud architecture for students and faculty. A recognized expert in the field, Gift is a Python Software Foundation Fellow and an AWS Machine Learning Hero, holding multiple AWS certifications, including AWS Certified Solutions Architect and AWS Certified Machine Learning Specialist. He has authored several influential books, such as Practical MLOps, Python for DevOps, and Pragmatic AI, and has published over 100 technical articles across various platforms, including Forbes and O'Reilly. His extensive industry experience includes roles as CTO and Chief Data Scientist for notable companies like Disney Feature Animation, Sony Imageworks, and AT&T, contributing to major films like Avatar and Spider-Man 3. Gift's work has generated millions in revenue through product development on a global scale. He actively consults startups on machine learning and cloud architecture while leading initiatives to enhance data science education.
Adjunct Assistant Professor at Duke University
Dr. Alfredo Deza is an Adjunct Assistant Professor in the Pratt School of Engineering at Duke University, where he teaches courses on machine learning, programming, and data engineering. He has been involved in academia for several years, focusing on innovative teaching methods and practical applications of technology. Dr. Deza co-authored the book Practical MLOps and has published several other works related to Python and machine learning. His teaching includes courses such as Python Bootcamp and advanced data engineering topics, and he actively develops online courses available on platforms like Coursera. In addition to his academic role, Dr. Deza works in developer relations at Microsoft, leveraging his extensive experience in software engineering and cloud computing to enhance educational content and support for students and faculty. He collaborates with various universities worldwide, including Georgia Tech and Carnegie Mellon University, to promote knowledge sharing in the field of technology and data science.
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