What you'll learn

This course is designed to help you become a Certified Generative AI Engineer. The course does not assume any prior knowledge in Machine Learning and Generative AI and starts teaching from the beginner level.

On completing this course, you will be able to design and implement LLM-enabled solutions. You will be able to to break down complex requirements into manageable tasks as well as choose appropriate models, tools, and approaches from the current generative AI landscape for developing comprehensive solutions.

You will also learn Vector Search, Model Serving for deploying models and solutions, managing solution lifecycle. You can also build and deploy scalable and performant RAG applications and LLM chains that take full advantage of state-of-the-art toolset and technologies.

  • Learn to create Generative AI and NLP applications using Open Source and state-of-the-art LLM models

  • Develop RAG pipelines and integrating Vector databases for Retrieval Augmented Generation

  • Designing and developing Scalable Compound AI Systems using modern frameworks

  • Develop, deploy and manage scalable Generative AI projects on Databricks platform

Course Prerequisite

What do you need to know before you start this course

  • You must already know Python programming and should have good hands-on in Python Language

  • You must be already familiar with Apache Spark and Databricks Cloud Platform

Course Content

Video lectures - Source code - Dataset

    1. What will you learn in this chapter

    2. Introduction to Machine Learning

    3. Types of Machine Learning

    4. Machine Learning Algorithms and Approaches

    5. Introduction to Deep Learning

    6. Introduction to Generative AI

    7. LLMs and AI System Design

    8. Chapter Notes

    1. What will you learn in this chapter

    2. What is My Learning Path

    3. How to use Transformer Models

    4. Working on your local machine using IDE

    5. Hands-on with Models and Tasks

    6. Advanced NLP Tasks

    7. Chapter Notes

    8. Exercise and Source Code Files

    1. What will you learn in this chapter

    2. Introduction to Transformer Architecture

    3. Looking inside the transformer pipelines

    4. How to use Transformer Auto Model Classes

    5. How to use Transformer Model Classes

    6. Inspecting and Dissecting Pipelines

    7. How to do model selection

    8. How to implement selected model

    9. Course Review

    10. Chapter Notes

    11. Exercise and Source Code Files

    1. What will you learn in this chapter

    2. Introduction to prompts

    3. Examples of prompts

    4. Elements of prompts

    5. Transformer Models and Prompts

    6. Few Shot Learning

    7. Chain of Thought and Meta Prompting

    8. How to access Gated Models

    9. Generation Configs

    10. Generation Strategies

    11. Course Review

    12. Chapter Notes

    13. Exercise and Source Code

    1. What will you learn in this chapter

    2. The Power of Context and Factual Recall

    3. RAG Solution Architecture

    4. Data Preparation for RAG

    5. Implementing RAG Pipeline

    6. RAG Tool References

    7. Course Review

    8. Chapter Notes

    9. Source Code and Data

    1. What will you learn in this chapter

    2. Introduction to LangChain

    3. How to use very large models

    4. Setup your Open AI Account

    5. Instruct Vs Dialog Tuned Models

    6. LangChain Basics - Instruct Model

    7. LangChain Basics - Chat Model

    8. Document Loaders and Splitters

    9. LangChain vector databases

    10. Simple RAG with LangChain

    11. Simple RAG with LangChain – Another Example

    12. Introduction to query rewrite

    13. Implementing query rewrite with RAG

    14. Introduction to multi-query for RAG

    15. Implementing multi-query for RAG

    16. Introduction to query routing for RAG

    17. Implementing query routing for RAG

    18. Chapter Notes

    19. Source Code and Data

Course Features

  • 91 lessons
  • 20 hours of video content
  • Source Code & Data
  • Total Support

Features & Support

  • Total Support

    We provide support throughout your learning and answer every question. You may also avail one-to-one and online technical support calls for blocker issues.

  • Completion Certificate

    Students who complete ScholarNest Academy courses earn free, verifiable course completion certificates to share with their friends, co-workers, and potential employers.

  • Future Updates

    Any future updates, upgrades, revisions, or topics included in the same course during your course access period will be available at no additional cost.

Course FAQ

  • Do you have a refund policy?

    Yes. You can ask for a refund within 7 days of your purchase or before completing 15% of the course material, whichever is earlier. We provide a refund after deducting 6% of payment processing charges.

  • How long can I access the course material?

    We provide standard 3-year access to the course material from the date of purchase. However, our promotional offers may reduce the access duration for a discounted price. Please check access validity terms and conditions for the promotional offers.

  • How do you provide support?

    We have a Q&A forum where you can ask questions, and our team will answer your queries.

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