• 1 Hour

  • 4.8/5

  • Beginner

Course Description

This course provides a concise guide to optimizing Large Language Models (LLMs) by navigating tradeoffs in speed, cost, scale, and accuracy. Learn practical techniques like LoRA, model quantization, and parameter-efficient fine-tuning to improve performance while reducing costs. You'll explore various deployment strategies and understand how to evaluate LLMs using industry-standard benchmarks, making this course ideal for anyone seeking efficient, scalable AI solutions.

Course curriculum

  • 1
    Navigating LLM Tradeoffs
    • Introduction
    • Resources
    • Technique to Increase Accuracy
    • Training Speed and Cost Optimization
    • Inference Speed and Cost Optimization
    • Scale

Certificate of Completion

Unlock a lifetime-valid certificate from Analytics Vidhya upon completing the course—your achievement is forever recognized!
Certificate of Completion

Who should Enroll?

  • ML Engineers and Data Scientists seeking to optimize LLMs for efficient deployment.

  • AI Enthusiasts interested in learning practical techniques to balance LLM performance, cost, and scalability.

  • Professionals in MLOps aiming to understand different deployment strategies for LLMs, including cloud, containerized, and serverless options.

Instructor

Kartik Nighania - MLOps Engineer at Typewise|Certified AWS Cloud and Kubernetes Engineer

Kartik Nighania, an MLOps Engineer at Typewise, brings over seven years of AI experience across computer vision, NLP, and DevOps. Formerly Head of Engineering at Pibit.ai, he led AI-driven automation and infrastructure scaling. His expertise in CI/CD pipelines was honed at HSBC Technology, and his academic work includes AI publications and projects like ML-driven crop health detection.
LinkedIn
Instructor

Frequently Asked Questions (FAQs)

  • What will I learn from this course?

    You'll learn how to navigate tradeoffs in LLMs, including techniques for improving speed, reducing costs, scaling models, and optimizing accuracy, as well as deployment and fine-tuning methods.

  • Do I need prior experience with LLMs to take this course?

    Basic knowledge of machine learning concepts and coding is helpful, but the course covers LLM fundamentals, making it accessible to those new to LLMs.

  • How is fine-tuning covered in the course?

    The course focuses on efficient fine-tuning methods like LoRA, which enable you to adapt large models cost-effectively with minimal hardware requirements.

  • What practical skills will I gain?

    You'll gain hands-on skills in LLM evaluation, model quantization, prompt engineering, and deploying models using various cloud and on-premise solutions.

  • How can this course help me reduce LLM deployment costs?

    You'll learn techniques like quantization, spot instance utilization, and serverless deployments to significantly cut down on costs without sacrificing model performance.

Key Takeaways

  • Optimize LLM Tradeoffs: Master techniques to balance speed, cost, scale, and accuracy for LLMs.

  • Efficient Fine-Tuning: Use LoRA to train large models with less compute while maintaining performance.

  • Model Quantization: Reduce memory use and boost inference speed with 8-bit/4-bit quantization.

  • LLM Evaluation Metrics: Assess models with ROUGE, BLEU, and benchmark tools like HuggingFace Leaderboard.

  • Inference Optimization: Multi-LoRA, Improve efficiency with KV caching and Flash attention for faster results.

  • Flexible Deployments: Choose from APIs, Kubernetes, and serverless options for scalable LLM deployment.

  • Cost-Effective scaling and testing: Leveraging different cloud solutions based on use-case and how to load test applications for real-world scenario.