Navigating LLM Tradeoffs Techniques for Speed & Accuracy

  • BeginnerLevel

  • 1500+Students Enrolled

  • 30 MinsDuration

  • 4.8Average Rating

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About this Course

  • Learn to optimize LLMs by balancing speed, cost, scale, and accuracy for efficient AI solutions.
  • Master techniques like LoRA, model quantization, and parameter-efficient fine-tuning to enhance performance.
  • Explore deployment strategies and evaluate LLMs using industry-standard benchmarks for real-world applications.

Learning Outcomes

Optimize LLM Tradeoffs

Master techniques to balance speed, cost, scale & accuracy.

Enhance Inferencing

Use KV caching & Flash attention for faster results.

Deploy Effectively

Choose APIs, Kubernetes & cloud solutions for scaling.

Who Should Enroll

  • ML Engineers & Data Scientists looking to optimize LLMs for efficient performance, cost, and scalable deployment.
  • AI Enthusiasts eager to learn practical techniques for balancing LLM speed, accuracy, and resource efficiency.
  • MLOps Professionals exploring deployment strategies, including cloud, containerized, and serverless LLM solutions.

Course Curriculum

Explore a comprehensive curriculum covering Python, machine learning models, deep learning techniques, and AI applications.

tools

  1. 1. Introduction

  2. 2. Resources

  3. 3. Technique to Increase Accuracy

  4. 4. Training Speed and Cost Optimization

  5. 5. Inference Speed and Cost Optimization

  6. 6. Scaling the LLM

Meet the instructor

Our instructor and mentors carry years of experience in data industry

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Kartik Nighania

MLOps Engineer, Typewise

Kartik Nighania, MLOps Engineer at Typewise, has 7+ years of AI experience in CV, NLP & DevOps. Former Head of Engineering at Pibit.ai, he led AI automation & scaling. He honed CI/CD expertise at HSBC & worked on ML-driven crop health detection

Get this Course Now

With this course you’ll get

  • 30 Mins

    Duration

  • Kartik Nighania

    Instructor

  • Beginner

    Level

Certificate of completion

Earn a professional certificate upon course completion

  • Globally recognized certificate
  • Verifiable online credential
  • Enhances professional credibility
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Frequently Asked Questions

Looking for answers to other questions?

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.

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

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

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

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