MLOps & LLM Engineering Resources
Papers, repos, courses, and communities for ML platform teams, LLMOps practitioners, and engineers running models in production.
Papers
- Scaling Laws for Neural Language ModelsKaplan et al., 2020 #scaling
- Efficient Memory Management for Large Language Model Serving with PagedAttentionKwon et al., 2023 #inference
- MLflow: A Platform for Managing the Machine Learning LifecycleZaharia et al., 2020 #mlops
- Responsible AI Practices for ML EngineersGoogle AI #governance
- Patterns for Building LLM-based Systems & ProductsEugene Yan, 2023 #architecture
- LLM Powered Autonomous AgentsLilian Weng, 2023 #agents
Tools & Repos
- MLflow
Open-source platform for managing the ML lifecycle including experiment tracking, model registry, and deployment.
#experiment-tracking - Weights & Biases
ML experiment tracking, model versioning, and collaboration platform.
#experiment-tracking - Evidently AI
Open-source ML monitoring library for data drift, model performance, and data quality.
#monitoring
Talks & Videos
- Stanford CS329S: Machine Learning Systems Design
Full course on building and deploying scalable ML systems.
#course - MLOps Zoomcamp (DataTalks.Club)
Free practical MLOps course covering monitoring, CI/CD, and deployment.
#course - LLM Bootcamp (Full Stack Deep Learning)
End-to-end LLM application development and deployment.
#llmops
Courses
- Machine Learning Engineering for Production (MLOps) SpecializationAndrew Ng, DeepLearning.AI #certification
- Designing Machine Learning SystemsChip Huyen (O'Reilly) #book
- Introducing MLOpsTreveil et al. (O'Reilly) #book
Communities
- Weights & Biases Discord
Active community around experiment tracking and ML best practices.
#discord