Hyperparameter Tuning & Experiment Comparison
Systematic search strategies, MLflow-tracked runs, and the art of comparing hundreds of experiments to extract your best-performing model. (MLOPS: ABSOLUTE BEGINNERS TO PRO IN 100 DAYS - DAY 10)
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MLflow Projects & Model Registry
Stop running ML code in isolation. MLflow Projects make your experiments reproducible on any machine — while the Model Registry gives your models a professional lifecycle: from experiment to staging to production. (MLOPS: ABSOLUTE BEGINNERS TO PRO IN 100 DAYS - DAY 9)
Experiment Tracking with MLflow
Stop losing track of your best model. Learn how MLflow turns chaotic trial-and-error into a reproducible, queryable history of every experiment you've ever run. (MLOPS: ABSOLUTE BEGINNERS TO PRO IN 100 DAYS - DAY 8)
Review Week 1 & Build a Simple ML Project
Consolidate everything from Days 1–6 by building a fully versioned, end-to-end Iris classification project with Git, DVC, and a reproducible pipeline. (MLOPS: ABSOLUTE BEGINNERS TO PRO IN 100 DAYS - DAY 7)
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)