LLMOps & ML Deployment: Bring LLMs and GenAI to Production | Udemy [Update 04/2024]
English | Size: 2.73 GB
Genre: eLearning[/center]

Llama 3, GPT, ML-Ops, Ray, MLFlow, LoRa, AWQ, GPTQ, LLMOps, Deployment, Generative AI, LLMs, Flash Paged Attention, Cost

What you'll learn
Learn to set-up, configure and deploy large language models with precision, ensuring smooth operation in production environments.
Gain practical skills in ML-Ops with MLflow for effective model management and deployment.
Conduct cost-benefit analyses and apply strategic planning for economical AI project management.
Implement the latest LLM optimization and scaling techniques to enhance model performance.

Welcome to the course where you'll learn how to effectively deploy and scale Large Language Models in production environments using LLMOps and cutting edge techniques.

This course is designed to equip you with the knowledge and skills required for using large, machine learning models into the real world.

Key Topics Covered:

Pre-Deployment Essentials:

Model Evaluation: Techniques for ensuring model correctness.

Performance Tuning: Useful Strategies for optimizing model performance (both accuracy and speed) before deployment.

Advanced Model Management with ML-Ops:

MLflow Mastery: Hands-on guidance setting up and using MLflow our own mlflow server

Operational practice: Hands-on exercises and insights into ML-Ops practices for model tracking, serving, and deployment.

End to end integration: How to securely integrate these concepts into existing pipelines.

State-of-the-Art Deployment Techniques:

Efficiency Strategies: Learn and implement advanced batching, dynamic batches, and quantization.

Latest Advancements in LLM optimisation: We'll cover cutting edge concepts such as Flash Attention, Paged Attention, GPTQ, AWQ, LoRa and much more!

Innovative Scaling: Dive into advanced scaling techniques such as ZeRo and Deepspeed.

Economics of Machine Learning Inference:

Cost-Benefit Analysis: Balancing the economics of deployment with technical feasibility.

Strategic Planning: Understanding the business impact of deployment decisions.

Cluster Management for Scalability:

Distributed Deployments: Techniques for managing LLMs across clusters.

Distributed Dataflow: Learn how to move large scale, big data across a cluster of servers with RabbitMQ.

Distributed Compute: Implement AI workload scaling frameworks and use them to speed up LLM inference over multiple machines.

Real-World Applications: Practical, hands-on guidance for deploying at scale.

What You Will Learn:

Deploy with Confidence: From environment setup to advanced LLM deployment, gain hands-on experience that translates directly to real-world scenarios.

Strategic Deployment Insights: Master the balance between speed and accuracy, and learn to navigate the complex economics of machine learning projects.

Cost Efficiency & Business Perspective: Understand cost-cutting in AI projects without sacrificing quality. Learn from successful AI integrations vs. failures, focusing on practical, business-driven outcomes.

Success in AI Deployment: Identify best practices and common pitfalls in ML-Ops and scalability. Equip yourself with insights to make informed decisions, ensuring your AI projects add value and drive business success.

Cutting-Edge Techniques: Stay ahead of the curve with the latest optimizations for enhancing model performance and efficiency.

From Theory to Practice: Leverage real-world case studies and expert insights to understand successful strategies and common challenges.

Who This Course Is For:

AI Enthusiasts & Professionals: Whether you're deepening your expertise or just beginning, this course offers valuable knowledge for anyone involved in AI and machine learning projects.

Practical Learners: Ideal for those seeking a mix of theoretical knowledge and hands-on experience in deploying large language models.

Enrollment Benefits:

Comprehensive Learning: A structured, step-by-step guide through the complexities of LLM deployment.

Expert Guidance: Learn from industry experts with real-world experience.

Practical Experience: Engage with hands-on exercises and case studies for applicable skills.

Are you ready to become a master in deploying large language models?

Enroll today and start your journey to mastery!

Who this course is for:
This course is tailored for AI practitioners, data scientists, software engineers, and business professionals aiming to integrate AI into their operations, offering deep insights into deploying large language models with an emphasis on scalability, cost efficiency, and ML-Ops, making it valuable for any company looking to leverage AI for a strategic advantage.

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