Effectively deploying machine learning models requires competencies more commonly found in technical fields such as software engineering and DevOps. Machine learning engineering for production combines the foundational concepts of machine learning with the functional expertise of modern software development and engineering roles.

The Machine Learning Engineering for Production (MLOps) Specialization covers how to conceptualize, build, and maintain integrated systems that continuously operate in production. In striking contrast with standard machine learning modeling, production systems need to handle relentless evolving data. Moreover, the production system must run non-stop at the minimum cost while producing the maximum performance. In this Specialization, you will learn how to use well-established tools and methodologies for doing all of this effectively and efficiently.

In this Specialization, you will become familiar with the capabilities, challenges, and consequences of machine learning engineering in production. By the end, you will be ready to employ your new production-ready skills to participate in the development of leading-edge AI technology to solve real-world problems.

This is the first course of the Machine Learning Engineering for Production MLOps.

Week 1: Overview of the ML Lifecycle and Deployment

Week 2: Selecting and Training a Model

Week 3: Data Definition and Baseline

This is the second course of the Machine Learning Engineering for Production MLOps.

Week 1: Collecting, Labeling, and Validating data

Week 2: Feature Engineering, Transformation, and Selection

Week 3: Data Journey and Data Storage

Week 3: Advanced Data Labeling Methods, Data Augmentation, and Preprocessing Different Data Types

This is the third course of the Machine Learning Engineering for Production MLOps.

Week 1: Neural Architecture Search

Week 2: Model Resource Management Techniques

Week 3: High-Performance Modeling

Week 4: Model Analysis

Week 5: Interpretability

This is the fourth course of the Machine Learning Engineering for Production MLOps.

Week 1: Model Serving Introduction

Week 2: Model Serving Patterns and Infrastructures

Week 3: Model Management and Delivery

Week 4: Model Monitoring and Logging