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| File Name: | Deploy AI Smarter: LLM Scalability, ML-Ops & Cost Efficiency |
| Content Source: | https://www.udemy.com/course/deploy-ai-smarter-llm-scalability-ml-ops-cost-efficiency/ |
| Genre / Category: | Programming |
| File Size : | 2.9 GB |
| Publisher: | udemy |
| Updated and Published: | April 02, 2024 |
Welcome to “Deploy AI Smarter: LLM Scalability, ML-Ops & Cost Efficiency”!
This comprehensive guide is designed to equip you with the knowledge and skills required use and deploying 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.
DOWNLOAD LINK: Deploy AI Smarter: LLM Scalability, ML-Ops & Cost Efficiency
Deploy_AI_Smarter_LLM_Scalability__ML-Ops__amp__Cost_Efficiency.part1.rar – 995.0 MB
Deploy_AI_Smarter_LLM_Scalability__ML-Ops__amp__Cost_Efficiency.part2.rar – 995.0 MB
Deploy_AI_Smarter_LLM_Scalability__ML-Ops__amp__Cost_Efficiency.part3.rar – 923.8 MB
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