Essential Guide to LLMOps: Implementing effective LLMOps strategies and tools from data to deployment

Essential Guide to LLMOps
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Preface
   Who this book is for
   What this book covers
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   Conventions used
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Part 1: Foundations of LLMOps
1
Introduction to LLMs and LLMOps
   The evolution of NLP and LLMs
      The rise of machine learning in NLP
      Deep learning revolution
      The birth of LLMs
      Current state and future directions
   Traditional MLOps versus LLMOps
      Stages in the MLOps life cycle
      Specific challenges and methodologies in LLMOps
   Trends in LLM integration
      Integration of LLMs across industries
      Current trends and examples of LLM applications
   Core concepts of LLMOps
      Key LLMOps-specific terminology
      Model architecture
   LLMOps workflow overview
      Step-by-step overview
      Real-world example
   Summary
2
Reviewing LLMOps Components
   Data collection and preparation
      Data collection
      Processing raw text
      Tokenization
      Storing token ID mappings
      Dataset storage and database management systems (DBMSs)
   Model pre-training and fine-tuning
      Pre-training
      Fine-tuning
      Sliding windows
      Implementation of the sliding window technique
      Sliding window nuances
   Governance and review
      Avoiding training data leakage
      Access control
      Review
      Regulatory compliance
   Inference, serving, and scalability
      Online and batch inference
      CPU versus GPU serving
      Containerized deployments
   Monitoring
   Continuous improvement
   Summary
Part 2: Tools and Strategies in LLMOps
3
Processing Data in LLMOps Tools
   Collecting data
      Collecting structured data
      Collecting semi-structured data
      Collecting unstructured data
   Transforming data
      Defining core data attributes
      Transforming data
   Preparing data
      Cleaning text data
      Handling insufficient context
      Transforming data for LLM consumption
      Example Workflow in PySpark
      Automating Spark Jobs
   Summary
4
Developing Models via LLMOps
   Creating features
      Tokenizing annotations
      Uniquely identifying tokens with attention masks
   Storing features
   Retrieving features
   Selecting the foundation model
      Choosing the LLM for your specific use case
      Testing foundation LLMs
      Addressing additional model concerns
   Fine-tuning the foundation LLM
   Tuning hyperparameters
   Automating model development
   Summary
5
LLMOps Review and Compliance
   Evaluating LLM performance metrics offline
      Evaluating binary, multi-class, and multi-label metrics
      Evaluating perplexity, BLUE, and ROUGE
      Evaluating reliability and robustness
      Evaluating conversational flow
   Securing and governing models with LLMOps
      Managing OWASP risks in LLMs
      Governance for LLMs
   Ensuring legal and regulatory compliance
   Operationalizing compliance and performance
      Operationalizing performance
      Security and governance
      Legal and regulatory compliance
      Validation of data and model licensing
      Human review points
   Summary
Part 3: Advanced LLMOps Applications and Future Outlook
6
LLMOps Strategies for Inference, Serving, and Scalability
   Operationalizing inference strategies in LLMOps
      Decoding inference types – real-time, batch, and interactive
      Model pruning
      Model quantization
      Synergistic effects and considerations
      Efficient hardware utilization
      Trade-offs between inference speed and output quality
   Optimizing model serving for performance
      Comparing serverless, containerized, and microservices architectures
      Leveraging the microservices architecture
      Performance tuning
      Serving up-to-date models
      Rolling back failed deployments
   Increasing model reliability
   Summary
7
LLMOps Monitoring and Continuous Improvement
   Monitoring LLMs fundamentals
      Maintaining consistent performance
      Compliance and security
      Resource optimization
      Fostering trust and reliability
      Monitoring metrics and parameters for LLMs
   Monitoring tools and technologies
      Cloud-based platforms
      Custom solutions
   Monitoring for metrics
      Key metrics to monitor
      Monitoring tools
      Actions in response to metrics
   Learning from human feedback
      Collecting and integrating feedback
      Challenges in integrating feedback
      Solutions for effective feedback integration
      Impacts of human feedback
   Incorporating continuous improvement
      Key principles of continuous improvement in LLMOps
      Integration of automation tools for seamless improvement cycles
   Implementing a continuously improving system
      Metrics used and performance improvements observed
   Summary
8
The Future of LLMOps and Emerging Technologies
   Identifying trends in LLM development
      Advancements in model architectures
      Scaling models
      Integration of multimodal capabilities
      Efficiency improvements
   Emerging technologies in LLMOps
      Automated Machine Learning (AutoML)
      Integration of AutoGPT and Distilabel in AutoML
      Benefits of AutoML in operational settings
      Challenges and limitations
      Federated learning
      Edge computing
      AI and IoT convergence
   Considering responsible AI
      Privacy and data security
      Regulatory compliance
      Preparing for next-generation LLMs
      Infrastructure and resource planning
      Scalability and flexibility
      Redundancy and disaster recovery
      Future-proofing infrastructure investments
   Developing talent and skill
      Collaboration and partnership
   Planning and risk management
   Summary
Index
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