8-Chems

DevOps · Containers Docker — A Complete Command Reference Core concepts, essential commands, choosing the right base image, and a full 3-service Python walkthrough from scratch. 🐳 Docker lets you package any application and its dependencies into a lightweight, portable container that runs identically across every environment — your laptop, a CI server, or a cloud VM. This guide covers everything from the vocabulary to a production-ready multi-service stack. Core Concepts 📦 Image A read-only template built from a Dockerfile. The blueprint for a container. 🚢 Container A running instance of an image. Isolated, ephemeral, and fast to start. 📋 Dockerfile A text file of instructions used to build a custom image layer by layer. 🏪 Registry A storage hub for images. Docker Hub is the default public registry. 🔗 Volume Persistent storage that survives container restarts and removals. 🌐 Ne...

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...