MLOps/LLMOps Course
Foundations of AI Engineering and LLMs
LLMOps Part 1: An overview of AI engineering and LLMOps, and the core dimensions that define modern AI systems.
MLOps
A collection of 22 posts
MLOps/LLMOps Course
CI/CD Workflows
MLOps Part 18: A hands-on guide to CI/CD in MLOps with DVC, Docker, GitHub Actions, and GitOps-based Kubernetes delivery on Amazon EKS.
MLOps/LLMOps Course
Monitoring and Observability: Practical Tooling with Evidently, Prometheus, and Grafana
MLOps Part 17: ML monitoring in practice with Evidently, Prometheus and Grafana, stitched into a FastAPI inference service with drift reports, metrics scraping, and dashboards.
MLOps/LLMOps Course
Monitoring and Observability: Core Fundamentals
MLOps Part 16: A comprehensive overview of drift detection using statistical techniques, and how logging and observability keep ML systems healthy.
MLOps/LLMOps Course
Model Deployment: EKS Lifecycle and Model Serving
MLOps Part 15: Understanding the EKS lifecycle, getting hands-on with AWS setup, and deploying a simple ML inference service on Amazon EKS.
MLOps/LLMOps Course
Model Deployment: Introduction to AWS
MLOps Part 14: Understanding AWS cloud platform, and zooming into EKS.
MLOps/LLMOps Course
Model Deployment: Cloud Fundamentals
MLOps Part 13: An overview of cloud concepts that matter, from virtualization and storage choices to VPC, load balancing, identity, and observability.
MLOps/LLMOps Course
Model Deployment: Kubernetes
MLOps Part 12: An introduction to Kubernetes, plus a practical walkthrough of deploying a simple FastAPI inference service using Kubernetes.
MLOps/LLMOps Course
Model Deployment: Serialization, Containerization and API for Inference
MLOps Part 11: A practical guide to taking models beyond notebooks, exploring serialization formats, containerization, and serving predictions using REST and gRPC.
MLOps/LLMOps Course
Model Development and Optimization: Compression and Portability
MLOps Part 10: A comprehensive guide to model compression covering knowledge distillation, low-rank factorization, and quantization, followed by ONNX and ONNX Runtime as the bridge from training frameworks to fast, portable production inference.
MLOps/LLMOps Course
Model Development and Optimization: Fine-Tuning, Pruning, and Efficiency
MLOps Part 9: A deep dive into model fine-tuning and compression, specifically pruning and related improvements.
MLOps/LLMOps Course
Model Development and Optimization: Fundamentals of Development and Hyperparameter Tuning
MLOps Part 8: A systems-first guide to model development and optimizing performance with disciplined hyperparameter tuning.
MLOps/LLMOps Course
Data and Pipeline Engineering: Distributed Processing and Workflow Orchestration
MLOps Part 7: An applied look at distributed data processing with Spark and workflow orchestration and scheduling with Prefect.
MLOps/LLMOps Course
Data and Pipeline Engineering: Sampling, Data Leakage, and Feature Stores
MLOps Part 6: A deep dive into sampling, class imbalance, and data leakage; plus a hands-on Feast feature store demo.
MLOps/LLMOps Course
Data and Pipeline Engineering: Data Sources, Formats, and ETL Foundations
MLOps Part 5: A detailed walkthrough of data engineering for MLOps, covering data sources, format performance trade-offs, and ETL/ELT pipelines.
MLOps/LLMOps Course
Reproducibility and Versioning in ML Systems: Weights and Biases for Reproducible ML
MLOps Part 4: A practical walkthrough of W&B-powered reproducibility.
MLOps/LLMOps Course
Reproducibility and Versioning in ML Systems: Fundamentals of Repeatable and Traceable Setups
MLOps Part 3: A practical exploration of reproducibility and versioning, covering deterministic training, data and model versioning, and experiment tracking.
MLOps/LLMOps Course
The Machine Learning System Lifecycle
MLOps Part 2: A deeper look at the ML lifecycle, plus a minimal train-to-API and containerization demo using FastAPI and Docker.
MLOps/LLMOps Course
Background and Foundations for ML in Production
MLOps Part 1: An introduction to machine learning in production, covering pitfalls, system-level concerns, and an overview of the full ML lifecycle.
Classical ML and Deep Learning
Federated Learning: A Critical Step Towards Privacy-Preserving ML
Learn real-world ML model development with a primary focus on data privacy – A practical guide.
Engineering Best Practices
Optimize Your ML Development and Operations with MLflow
The guide that every data scientist must read to manage ML experiments like a pro.
Engineering Best Practices
You Cannot Build Large Data Projects Until You Learn Data Version Control!
The underappreciated, yet critical, skill that most data scientists overlook.
