Implement Generative AI engineering with Azure Databricks DP-3028 Training Plan: Detailed Modules
Module 1: Get started with language models in Azure Databricks
- Understand Generative AI
- Understand Large Language Models (LLMs)
- Identify key components of LLM applications
- Use LLMs for Natural Language Processing (NLP) tasks
- Exercise – Explore language models
Module 2: Implement Retrieval Augmented Generation (RAG) with Azure Databricks
- Explore the main concepts of a RAG workflow
- Prepare your data for RAG
- Find relevant data with vector search
- Rerank your retrieved results
- Exercise – Set up RAG
Module 3: Implement multi-stage reasoning in Azure Databricks
- What are multi-stage reasoning systems?
- Explore LangChain
- Explore LlamaIndex
- Explore Haystack
- Explore the DSPy framework
- Exercise – Implement multi-stage reasoning with LangChain
Module 4: Fine-tune language models with Azure Databricks
- What is fine-tuning?
- Prepare your data for fine-tuning
- Fine-tune an Azure OpenAI model
- Exercise – Fine-tune an Azure OpenAI model
Module 5: Evaluate language models with Azure Databricks
- Explore LLM evaluation
- Evaluate LLMs and AI systems
- Evaluate LLMs with standard metrics
- Describe LLM-as-a-judge for evaluation
- Exercise – Evaluate an Azure OpenAI model
Module 6: Review responsible AI principles for language models in Azure Databricks
- What is responsible AI?
- Identify risks
- Mitigate issues
- Use key security tooling to protect your AI systems
- Exercise – Implement responsible AI
Module 7: Implement LLMOps in Azure Databricks
- Transition from traditional MLOps to LLMOps
- Understand model deployments
- Describe MLflow deployment capabilities
- Use Unity Catalog to manage models
- Exercise – Implement LLMOps
Recommended Prerequisite Knowledge
- Understanding of artificial intelligence and machine learning fundamentals.
- Familiarity with basic Spark and Azure Databricks concepts.
- Knowledge of language models and data processing principles.
Eccentrix Corner Articles: Generative AI Engineering with Azure Databricks DP-3028 Resources
Explore our technical articles on Generative AI Engineering with Azure Databricks DP-3028 published on Eccentrix Corner. These resources dive deep into key concepts, share best practices, and provide practical guides to maximize your learning and success. Our experts offer real-world insights to help you master the exploration, fine-tuning, evaluation, and integration of advanced language models on Azure Databricks.
Azure Databricks Generative AI Training
The Azure Databricks Generative AI training is designed to equip IT professionals with the knowledge and skills needed to implement robust generative AI solutions in Microsoft Azure. This course provides an in-depth understanding of Azure Databricks for generative AI, including the use of Spark, fine-tuning large language models (LLMs), retrieval-augmented generation (RAG), and integrating responsible AI practices.
By mastering Azure Databricks for generative AI, participants can efficiently manage the model lifecycle, optimize deployments, and ensure the reliability of their AI solutions.
Why Take This Training?
Effectively deploying and managing advanced language models is essential for ensuring the performance and relevance of generative AI solutions. Azure Databricks offers a comprehensive platform to explore, fine-tune, evaluate, and integrate LLMs. This training provides expertise for end-to-end generative AI implementation, with a focus on industry best practices and responsible model management.
Participants will learn to automate model evaluation, customize training pipelines, and leverage LLMOps to enhance the resilience and scalability of their AI infrastructure.
Skills Developed During the Training
Exploring and Integrating LLMs with Spark
Learn to use Spark on Azure Databricks to explore and integrate advanced language models.Fine-tuning and Evaluating Language Models
Master fine-tuning LLMs for specific use cases and evaluating their performance.Implementing Retrieval-Augmented Generation (RAG)
Discover how to implement RAG techniques to improve the relevance of generated responses.Multi-step Reasoning and Complex Scenarios
Design solutions that leverage multi-step reasoning for advanced tasks.Integrating Responsible AI Practices
Apply responsible AI principles for deploying reliable and ethical solutions.Managing Model Lifecycle with LLMOps
Gain expertise in managing, monitoring, and scaling models in production with LLMOps.
Interactive, Practice-Oriented Training
Led by certified Azure experts, this course combines theoretical knowledge with practical exercises. Participants will work on real-world scenarios to effectively apply Azure Databricks generative AI features, ensuring readiness for professional challenges.
Who Should Attend?
- Data scientists and AI engineers developing generative AI solutions
- IT professionals responsible for deploying advanced models on Azure
- Systems architects designing scalable and reliable AI pipelines
- Anyone interested in learning advanced generative AI techniques on Azure Databricks
Enhance Your AI Management with Azure Databricks Expertise
The Generative AI Engineering with Azure Databricks (DP-3028) training equips you with the skills needed to leverage Azure Databricks and optimize your AI operations. Enroll now to master advanced generative AI techniques and ensure the resilience and efficiency of your cloud solutions.
Frequently Asked Questions – Azure Databricks Generative AI Training (FAQ)
It covers exploration, fine-tuning, evaluation, and integration of LLMs, RAG techniques, responsible model management, and LLMOps.
Basic experience with Azure Databricks and Spark is recommended, but the course introduces essential concepts to help learners succeed even with limited prior exposure.
Azure Databricks provides a unified platform to explore, train, fine-tune, deploy, and monitor advanced language models. LLMOps tools streamline the lifecycle management of models in production, ensuring reliability and scalability.
Yes, Azure Databricks integrates seamlessly with other Azure services such as Azure Machine Learning, Azure Data Lake, Azure Storage, and Azure Monitor, enabling advanced data and model management.
Absolutely. The program emphasizes industry best practices for generative AI, including responsible AI, security, model optimization, and effective production deployment management.
