Generative Artificial Intelligence (AI) engineering with Azure Databricks uses the platform's capabilities to explore, fine-tune, evaluate, and integrate advanced language models. By using Apache Spark's scalability and Azure Databricks' collaborative environment, you can design complex AI systems.
After completing this course, students will be able to:
Describe Large Language Models (LLMs)
Use LLMs for Natural Language Processing (NLP) tasks
Set up a RAG workflow
Improve model accuracy by reranking your search results
Identify the need for multi-stage reasoning systems
Implement multi-stage reasoning with libraries like LangChain, LlamaIndex, Haystack, and the DSPy framework
Understand when to use fine-tuning
Compare LLM and traditional ML evaluations
Describe the responsible AI principles for implementation of language models
Implement key security tooling for language models
As part of Lumify Group, Lumify Work has skilled more people in Microsoft technologies than any other organisation in Australia and New Zealand. We have a campus in the Philippines, too. We offer the broadest range of instructor-led training courses, from end user to architect level. We are proud to be the winner of the Microsoft MCT Superstars Award for FY24, which formally recognises us as having the highest quality Microsoft Certified Trainers in ANZ.
Data Scientists
You should be familiar with fundamental AI concepts and Azure Databricks.
The supply of this course by Lumify Work is governed by the booking terms and conditions. Please read the terms and conditions carefully before enrolling in this course, as enrolment in the course is conditional on acceptance of these terms and conditions.
