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.
Deployment
A collection of 24 posts
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.
Engineering Best Practices
5 Must-Know Ways to Test ML Models in Production (Implementation Included)
A beginner-friendly guide to model testing.
Engineering Best Practices
Quantization: Optimize ML Models to Run Them on Tiny Hardware
A must-know skill for ML engineers to reduce model footprint and inference time.
Engineering Best Practices
Deploy, Version Control, and Manage ML Models Right From Your Jupyter Notebook with Modelbit
Deployment has possibly never been so simple.
Engineering Best Practices
PyTorch Models Are Not Deployment-Friendly! Supercharge Them With TorchScript.
Eliminating the dependence of PyTorch models on Python.
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.
Engineering Best Practices
Sklearn Models are Not Deployment Friendly! Supercharge Them With Tensor Computations.
Speed up sklearn model inference up to 50x with GPU support.
