Introduction to Cloud Platforms for MLOps
Overview of AWS, GCP, and Azure ML services. (MLOPS: ABSOLUTE BEGINNERS TO PRO IN 100 DAYS - DAY 17)
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Serving Models with MLflow Models Serve
Move from experimentation to production by spinning up a REST API for your trained model with a single MLflow command. Learn mlflow models serve, model signatures, input schemas, and how to send real-time predictions. (MLOPS: ABSOLUTE BEGINNERS TO PRO IN 100 DAYS - DAY 16)
Model Serving Fundamentals & REST APIs
A trained model sitting in a .pkl file helps nobody. Today your iris classifier gets a front door: a REST API that any application, in any language, can call over HTTP. (MLOPS: ABSOLUTE BEGINNERS TO PRO IN 100 DAYS - DAY 15)
DVC Pipelines & Experiment Tracking
Stop running scripts manually. Learn how to define reproducible, dependency-aware ML pipelines using DVC's declarative dvc.yaml, track every run automatically, and share exact pipeline states with your team — no Makefile required. (MLOPS: ABSOLUTE BEGINNERS TO PRO IN 100 DAYS - DAY 6)
Data Versioning & DVC Introduction
Why your ML experiments are irreproducible without proper data versioning and how DVC pairs with Git to give every dataset, model, and artifact a permanent, auditable address. (MLOPS: ABSOLUTE BEGINNERS TO PRO IN 100 DAYS - DAY 5)
Version Control with Git for ML Projects
Stop losing ML experiments. Learn Git workflows, branching strategies, and .gitignore best practices to make your ML projects reproducible, organized, and production-ready. (MLOPS: ABSOLUTE BEGINNERS TO PRO IN 100 DAYS - DAY 4)