MLOps Tutorial for Beginner | Introduction to MLOps | MLOps Overview [Updated 2024] - igmGuru

What is MLOps?
MLOps is basically a set of practices for collaboration and communication between data scientists and operations professionals. By applying these practices increases the quality, simplifies the management process, and automates the deployment of Machine Learning and Deep Learning models in large-scale production environments. It’s easier to align models with business needs, as well as regulatory requirements.
MLOps is slowly evolving into an independent approach to ML lifecycle management. It applies to the entire lifecycle – data gathering, model creation (software development lifecycle, continuous integration/continuous delivery), orchestration, deployment, health, diagnostics, governance, and business metrics.

What are the Key Phases of MLOps?
The key phases of MLOps are:
1. Data gathering
2. Data analysis
3. Data transformation/preparation
4. Model training & development
5. Model validation
6. Model serving
7. Model monitoring
8. Model re-training.

Why MLOps is in demand?
MLOps is fundamental. Machine learning helps individuals and businesses deploy solutions that unlock previously untapped sources of revenue, save time, and reduce cost by creating more efficient workflows, leveraging data analytics for decision-making, and improving customer experience.
These goals are hard to accomplish without a solid framework to follow. Automating model development and deployment with MLOps means faster go-to-market times and lower operational costs. It helps managers and developers be more agile and strategic in their decisions.

MLOps serves as the map to guide individuals, small teams, and even businesses to achieve their goals no matter their constraints, be it sensitive data, fewer resources, small budgets, and so on.