From 1276bfefcb81cd2a5899ce55a64f28783b226597 Mon Sep 17 00:00:00 2001 From: pazevedo-hyland Date: Sat, 21 Dec 2024 17:24:33 +0000 Subject: [PATCH] Add syllabus for LLMOps course with comprehensive modules and learning outcomes --- docs/8. LLMOps/8.0 What is LLMOps.md | 218 +++++++++++++++++++++++++++ 1 file changed, 218 insertions(+) create mode 100644 docs/8. LLMOps/8.0 What is LLMOps.md diff --git a/docs/8. LLMOps/8.0 What is LLMOps.md b/docs/8. LLMOps/8.0 What is LLMOps.md new file mode 100644 index 00000000..748f07ae --- /dev/null +++ b/docs/8. LLMOps/8.0 What is LLMOps.md @@ -0,0 +1,218 @@ +**Syllabus for LLMOps Course** + +%% Note: Leaving this here as a sketch to help me layout what needs to be adressed. + +--- + +### **Course Overview** +This course is designed to provide learners with a comprehensive understanding of Large Language Model Operations (LLMOps). It covers the fundamental principles, design patterns, and best practices for deploying and maintaining large language models (LLMs) and generative AI systems in production environments. The course is platform-agnostic and focuses on universal concepts and strategies. + +--- + +### **Module 1: Introduction to LLMOps** +- **What is LLMOps?** + - Definition and scope + - Importance in the AI lifecycle +- **Applications of LLMs** + - Real-world use cases + - Ethical considerations and biases +- **Differences Between Traditional MLOps and LLMOps** + - Unique challenges with LLMs + +--- + +### **Module 2: Foundations of Generative AI** +- **Understanding Generative AI** + - How LLMs work (overview of transformers, attention, and pretraining) + - Differences between generative models (GPT, BERT, etc.) +- **Prompt Engineering Basics** + - Techniques for effective prompts + - Zero-shot, one-shot, and few-shot learning + - Prompt tuning and evaluation + +--- + +### **Module 3: Retrieval-Augmented Generation (RAG) Systems** +- **Introduction to RAG** + - What are RAG systems? + - Use cases and benefits +- **Building RAG Pipelines** + - Document retrieval strategies + - Integration with LLMs +- **Evaluation of RAG Systems** + - Metrics for RAG effectiveness + - Handling failures and fallback mechanisms + +--- + +### **Module 4: Model Registry and Lifecycle Management** +- **Using a Model Registry** + - Benefits of model registries on LLMs + - Managing model versions and metadata +- **Integration with LLM Workflows** + - Deploying models from a registry + - Traceability and auditing +- **Data Ingestion and Management** + - Building robust data ingestion pipelines + - Streamlining data collection and transformation + - Ensuring data quality and consistency + - Compliance with regulations and secure data storage + +--- + +### **Module 5: Deployment Strategies** +- **Packaging and Versioning** + - Containerization (Docker basics) + - Version control for models and datasets +- **Deployment Patterns** + - Edge vs. cloud deployments + - Online vs. batch inference +- **Monitoring and Maintenance** + - Monitoring performance and usage + - Automating updates and retraining + +--- + +### **Module 6: Observability and Feedback Loops** +- **Real-Time Monitoring** + - Logging and tracing for LLM systems + - Handling anomalies and drift detection +- **User Feedback Integration** + - Collecting user inputs for model improvement + - Implementing active learning loops + +--- + +### **Module 7: Guardrails for LLM Systems** +- **Ensuring System Reliability** + - Strategies to prevent model misuse + - Implementing safeguards for outputs +- **Mitigating Risks** + - Addressing prompt injection attacks + - Building fallback and fail-safe mechanisms +- **Ethical and Fair Usage** + - Fairness and bias reduction + - Transparency in decision-making + +--- + +### **Module 8: Governance and AI Gateways** +- **Governance Principles** + - Establishing policies and protocols + - Ensuring compliance with regulations +- **AI Gateways** + - Centralized control for AI operations + - Implementing approval and review workflows + +--- + +### **Bonus Module: Training and Fine-Tuning** +- **Training Fundamentals** + - Dataset curation and preprocessing + - Training pipeline design +- **Fine-Tuning LLMs** + - When and why to fine-tune + - Techniques and tools for fine-tuning +- **Evaluation and Metrics** + - Evaluating model performance + - Metrics specific to generative models (e.g., BLEU, perplexity) + +--- + +### **Learning Outcomes** +By the end of this course, learners will: +- Understand the operational challenges of deploying LLMs at scale. +- Be able to design scalable and secure architectures for LLM systems. +- Apply best practices for monitoring, governance, and lifecycle management. +- Develop ethical and efficient generative AI solutions. + + + +# LLMOps vs. MLOps: Similarities, Differences, and Workflows + + +In the evolving world of machine learning (ML), large language models (LLMs) have emerged as a transformative force. These models, which excel at tasks such as answering open-ended questions, summarizing content, and executing complex instructions, are far more advanced than their traditional counterparts. However, when it comes to developing and deploying LLMs, the MLOps workflow requires significant adjustments. Let’s take a closer look at what’s common and what’s different when it comes to MLOps for LLMs. + +--- + +### **Common Practices vs. Unique Challenges in MLOps for LLMs** + +| **Aspect** | **Common Practices (MLOps)** | **Unique Challenges for LLMs** | +|-------------------------------|--------------------------------------------|---------------------------------------------------------------------| +| **Model Development** | Train from scratch / fine tune, often leveraging open source models | LLMs are often fine-tuned from existing models and require specialized prompt engineering for each model family. This imposes different data curation pipelines when fine tunning a model. | +| **Packaging ML Artifacts** | ML artifacts often include models, datasets, and scripts | Focus shifts from models to pipelines, which include prompts, agents, or chains | +| **Evaluation** | Use of traditional ML metrics (e.g., accuracy, precision) | LLMs lack a single "right" answer for many use cases, requiring human feedback for evaluation (or LLM as judge) | +| **Training and Serving Infrastructure** | Model deployment on GPUs or CPUs with scaling considerations | LLMs require large-scale GPUs, specialized hardware, and fast storage solutions which makes serving and hosting larger models expensive | + +--- + +### **Understanding LLMs and Their Impact on MLOps** + +LLMs represent a leap in the capabilities of machine learning, but managing these models in production requires specific attention to their unique characteristics. While the fundamental principles of MLOps remain the same, there are several aspects of LLMs that require tailored workflows and processes. + +#### 1. **LLM Ecosystem: A World of Diversity** + +LLMs come in many forms—proprietary models, open-source versions, and custom fine-tuned models. You may interact with models hosted via third-party APIs, integrate pre-trained open-source models, or build entirely custom models fine-tuned to your needs. + +- **Implication for MLOps**: Development often begins by adapting an existing model, and over time, it evolves into custom fine-tuned models. This iterative approach means managing pipelines that are flexible and capable of integrating different model sources. + +#### 2. **Inputs and Prompts: The Heart of LLM Interactions** + +Unlike traditional ML models that work primarily with structured data, LLMs thrive on natural language input. This often requires developing complex prompts and text templates that help guide the model to produce the desired output. + +- **Implication for MLOps**: In LLM workflows, creating and managing prompt templates becomes an essential part of the pipeline. This addition to the workflow is crucial for maximizing the model’s effectiveness. +So in some essense prompts and prompt templates work in a similar way to what features would look like in traditional ML. + +#### 3. **Packaging ML Artifacts: Pipelines Over Models** + +When working with LLMs, a lot of times we will be calling a model API so the ML artifact you deploy is often not the model itself, but the pipeline or workflow that drives the model's behavior. This includes not only the model but also the chains, agents, and prompt templates that help it generate meaningful responses. + +- **Implication for MLOps**: Instead of focusing solely on the model, you need to consider packaging entire pipelines that can be deployed and managed in production. These pipelines include prompts, data preprocessing, and potentially post-processing logic. + +#### 4. **Serving LLMs: Dynamic Infrastructure Needs** + +LLMs often perform better when augmented with additional context, such as examples, related documents, or real-time data. To provide this context, you might integrate vector databases or other tools that can dynamically supply the model with relevant information. + +- **Implication for MLOps**: Your infrastructure must support these dynamic requirements, with the ability to handle real-time queries and fast retrieval of relevant data. You will likely rely on high-performance GPUs and fast storage to meet these demands if you are hosting your own LLM or other serving technologies that can handle the Artifacts (chains, agents etc..) you deployed. + +#### 5. **API Governance: Managing Multiple LLM APIs** + +Many LLMs are accessed via third-party APIs, whether proprietary or open-source. This allows you to use powerful models without needing to host them yourself. However, managing these external dependencies requires careful oversight. And Involve different governance and cost tracking mechanisms. + +- **Implication for MLOps**: Centralized API governance becomes crucial, most organizations use models from different providers, having a centralized place of governance allows us to now only manage and switch between different API providers seamlessly, ensuring the right model is always available for your needs but also to track and manage expenses across different teams/ projects within a company. + +#### 6. **Size and Scale: The Massive Footprint of LLMs** + +LLMs are large, often reaching hundreds of gigabytes in size. This massive scale introduces challenges in terms of model storage, loading, and serving. + +- **Implication for MLOps**: Serving self hosted LLMs requires specialized hardware, such as GPUs, and efficient storage systems. Moreover, given the size of these models, optimizing for cost and performance becomes essential. However since we can often leverage paid APIs to do this hosting for us we can also focus on the serving the pipelines/business logic which requires lower compute. + +#### 7. **Evaluation: A Shift Toward Human Feedback** + +Unlike traditional ML models that can be evaluated using clear, quantifiable metrics, LLMs require a different approach. Because LLMs often generate outputs without a single "correct" answer, evaluation relies heavily on human feedback. For this reason it's really important that we monitor and track our LLMs responses when deploying them. + +- **Implication for MLOps**: Human feedback plays a pivotal role in assessing and fine-tuning LLMs. Integrating user feedback into your evaluation and testing processes is essential for continuous improvement and ensuring high-quality model outputs. + +--- + +### **The Core Principles of MLOps: What Stays the Same** + +Despite these new challenges, many of the foundational principles of MLOps remain unchanged. Whether you’re working with traditional ML models or LLMs, these best practices will still apply: + + +| Aspect | Description | +|----------------------------|-----------------------------------------------------------------------------| +| **Environments** | Development, staging, and production environments remain essential. | +| **Version Control** | Use Git to track changes in code, prompts, and configurations. | +| **Model Lifecycle** | Manage model development with tools like MLflow and lifecycle registries. | +| **Data Storage** | Store data using lakehouse architectures and Delta tables for scalability. | +| **CI/CD Pipelines** | Existing CI/CD infrastructure integrates with LLM workflows. | + +The modular structure of MLOps also persists: +- Pipelines for featurization, training, and inference remain integral. +- Monitoring, testing, and governance practices apply universally. + +--- + +#### Resources: +https://docs.databricks.com/en/machine-learning/mlops/llmops.html \ No newline at end of file