8-Chems

How to Use MLflow for Machine Learning Experiments MLflow is an open-source platform designed to manage the entire machine learning lifecycle . It helps researchers and engineers track experiments, package models, manage versions, and deploy models efficiently. If you train multiple models, perform hyperparameter tuning, or compare architectures, MLflow becomes extremely valuable because it allows you to log and visualize experiments automatically . 1. What MLflow Is Used For MLflow contains four major components: Component Purpose Tracking Log experiments, metrics, parameters, and artifacts Projects Package ML code for reproducible execution Models Standard format to store and share ML models Model Registry Manage model versions and deployment stages Most users start with MLflow Tracking because it is the easiest way to monitor experiments. 2. Installing MLflow pip install mlflow To start the MLflow user interface: mlflow ui...

📚 Lesson 01 — Databases & Data Foundations From Relational Algebra to SQL Mastery Understand the theory first. Then every SQL query will make perfect sense. 📋 What we cover Part 1 — Relational Algebra: the theory behind SQL Selection (σ), Projection (π), Rename (ρ) Set operations: Union, Intersection, Difference, Cartesian Product Join (⋈) and the RA → SQL mapping Part 2 — SQL, Step by Step Step 1 — SELECT & FROM: choosing columns Step 2 — WHERE: filtering rows Step 3 — ORDER BY & LIMIT: sorting and paging Step 4 — JOINs: combining tables Step 5 — Aggregation functions: COUNT, SUM, AVG, MIN, MAX Step 6 — GROUP BY & HAVING Step 7 — Nested Queries (Subqueries & CTEs) Part 3 — Database Design & Normalization 🧪 Practice Exercises on...