The Ultimate Guide to MLOps: An End-to-End Overview

In today’s data-driven world, Machine Learning Operations (MLOps) has become a cornerstone for businesses aiming to harness the power of data. But what does an end-to-end MLOps process look like? Let’s break it down step by step.

📦 Data Handling

Before any model can be trained, the data needs to be in the right shape.

• 📩 Ingest: The first step is to collect raw data from a myriad of sources, be it databases, APIs, or even manual entries.
• ✅ Validate: Not all data is good data. It’s essential to ensure the data’s quality, consistency, and relevance for the task at hand.
• 🧽 Clean: Like cleaning up a messy room, this step involves tidying up the data by addressing inconsistencies, outliers, and missing values.
• 🔄 Standardize: To ensure smooth processing, data needs to be in a consistent format, be it in terms of units, scales, or encoding.
• 🗂 Curate: Think of this as setting the stage. Organize and structure the data to facilitate feature engineering and model development.

🔍 Feature Work

Features are the building blocks for any machine learning model.

• 💡 Extract: Dive deep into the data to unearth patterns…