By the end of this course, a learner will master Databricks to perform data engineering and data analytics tasks for data science workflows. Additionally, a student will learn to master running local large language models like Mixtral via Hugging Face Candle and Mozilla llamafile.
Databricks to Local LLMs
This course is part of Large Language Model Operations (LLMOps) Specialization
Instructors: Noah Gift
Sponsored by Coursera Learning Team
1,868 already enrolled
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What you'll learn
Use Databricks for data engineering and ML workloads
Create and design ML pipelines
Use Llamafile and other local LLMs like Mixtral
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There are 4 modules in this course
In this module, you will learn how to describe the Databricks architecture, create clusters, use notebooks for analysis, and share notebooks by completing hands-on labs and knowledge checks on these topics.
What's included
25 videos13 readings4 assignments1 discussion prompt2 ungraded labs
In this module, you will learn how to read and transform data, create Delta Lake pipelines, and work with complex data types by implementing ETL solutions and passing code samples reviews.
What's included
23 videos9 readings4 assignments3 ungraded labs
In this module, you will learn foundations of generative AI and responsible deployment strategies to benefit from the latest advancements while maintaining safety, accuracy, and oversight.By directly applying concepts through hands-on labs and peer discussions, you will gain practical experience putting AI into production.
What's included
13 videos14 readings4 assignments3 ungraded labs
In this module, you will learn mitigation strategies, evaluate task performance, and operationalize workflows by identifying risks in notebooks and deploying an LLM application.
What's included
13 videos10 readings3 assignments
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