更智能地部署 AI - LLM 可扩展性、ML-Ops 和成本效率
上次更新时间：2024-10-27
课程售价: 2.9 元
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课程内容
1. Introduction
2. Getting Started
3. Pre-Deployment Strategies
4. Advanced Model Management with ML-Ops
- 1. Fundamentals of ML Model Management and ML-Ops
- 2. Overview of Effective ML-Ops Frameworks
- 3. Setting up ML-Ops Framework Introduction to MLflow (Practical)
- 4. Getting Started with MLflow A Practical Approach (Practical)
- 5. Training Models with MLflow A Hands-On Guide (Practical)
- 6. MLflow for Model Inference Techniques and Practices (Practical)
- 7. Advanced Techniques in MLflow Extending Functionality (Practical)
5. Advanced Model Deployment Techniques
- 1. Efficiency through Batching and Dynamic Batches
- 2. Hands-on Application of Batching Techniques (Practical)
- 3. The Role of Sorting in Model Deployment (Practical)
- 4. Leveraging Quantization for Model Efficiency (Practical)
- 5. Inference Strategies Parallelism, Flash Attention, GPTQ & AVQ,
- 6. Next-Gen Scaling LoRa, Paged Attention, ZeRO
6. The Economics of Machine Learning Inference
7. Effective Cluster Management for Large Scale ML Deployments
- 1. Basic Inference - First Levels of Deployment (Practical)
- 2. Entering Optimisations - Advanced Levels of Deployment (Practical)
- 3. Setting Up Data Access in Distributed Environments (Practical)
- 4. Distributing Data Across a Cluster with RabbitMQ (Practical)
- 5. Foundations of Distributed Computing with Ray (Practical)
- 6. Scaling Large Language Models on a Cluster (Practical)
更智能地部署 AI - LLM 可扩展性、ML-Ops 和成本效率
上次更新时间：2024-10-27 19:56
课程内容
7个章节 , 29个讲座
1. Introduction
2. Getting Started
3. Pre-Deployment Strategies
4. Advanced Model Management with ML-Ops
- 1. Fundamentals of ML Model Management and ML-Ops
- 2. Overview of Effective ML-Ops Frameworks
- 3. Setting up ML-Ops Framework Introduction to MLflow (Practical)
- 4. Getting Started with MLflow A Practical Approach (Practical)
- 5. Training Models with MLflow A Hands-On Guide (Practical)
- 6. MLflow for Model Inference Techniques and Practices (Practical)
- 7. Advanced Techniques in MLflow Extending Functionality (Practical)
5. Advanced Model Deployment Techniques
- 1. Efficiency through Batching and Dynamic Batches
- 2. Hands-on Application of Batching Techniques (Practical)
- 3. The Role of Sorting in Model Deployment (Practical)
- 4. Leveraging Quantization for Model Efficiency (Practical)
- 5. Inference Strategies Parallelism, Flash Attention, GPTQ & AVQ,
- 6. Next-Gen Scaling LoRa, Paged Attention, ZeRO
6. The Economics of Machine Learning Inference
7. Effective Cluster Management for Large Scale ML Deployments
- 1. Basic Inference - First Levels of Deployment (Practical)
- 2. Entering Optimisations - Advanced Levels of Deployment (Practical)
- 3. Setting Up Data Access in Distributed Environments (Practical)
- 4. Distributing Data Across a Cluster with RabbitMQ (Practical)
- 5. Foundations of Distributed Computing with Ray (Practical)
- 6. Scaling Large Language Models on a Cluster (Practical)