Hands-On MLOps Fundamentals for ML Engineers

KodeKloud via Coursera Specialization

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Provider

Coursera Specialization
Pricing

Paid Course
Languages

English
Certificate

Certificate Available
Effort

4 weeks, 10 hours/week
Sessions

Self-Paced
Level

Beginner

Found in

Overview

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Transition seamlessly from DevOps to MLOps and master the complete machine learning lifecycle—from data ingestion to production deployment. This hands-on Specialization equips ML engineers with critical skills in data engineering, model deployment, monitoring, and governance to build reliable, scalable ML systems. Through real-world projects culminating in an automated insurance claim processing application, you'll gain job-ready expertise in MLOps tools and best practices aligned with 2026's fastest-growing technical skills. Join thousands of professionals mastering the critical intersection of machine learning and operations. Enrol in the Hands-On MLOps Fundamentals for ML Engineers Specialization today and position yourself at the forefront of one of tech's fastest-growing fields.

Syllabus

Course 1: Data Engineering Essentials
Course 2: ML Model Development and Tracking: Hands-on Guide
Course 3: Deploy ML Models to Production

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This course bridges the gap between raw data and production-ready AI systems. In 2026, the value of a machine learning model is defined by the reliability of the data pipelines that feed it. This program transforms you into an MLOps-ready engineer capable of building automated, scalable, and observable data architectures. You will start by mastering the MLOps lifecycle, learning why traditional DevOps isn't enough for the unique challenges of data and model drift. Moving into the technical core, you will learn to build resilient ETL pipelines using modern tools like Pandas and Polars for medium datasets, before scaling up to distributed processing with Apache Spark and Dask. The course features heavy emphasis on real-time streaming with Apache Kafka and the implementation of Feature Stores to solve the dreaded "training-serving skew." Finally, you will tie everything together through workflow orchestration using Airflow and Prefect, ensuring your data flows are not just functional, but production-grade, automated, and fully monitored. Course Highlights - Industry-Standard Stack: Hands-on experience with Kafka, Spark, Airflow, and Feature Stores. - Production-First Mindset: Focus on CI/CD/CT (Continuous Training) and data governance. - Hands-on Labs: Every module concludes with a practical lab to build your professional portfolio. - Scalability Focused: Transition from local Python scripts to distributed cloud-scale architectures.
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This comprehensive course is designed for aspiring MLOps engineers and data scientists looking to bridge the gap between experimental notebooks and robust production environments. You will begin by establishing a strong foundation in model development, exploring the hardware essentials of CPUs and GPUs, and mastering hyperparameter tuning. The curriculum moves rapidly into industrial-grade experimentation using MLflow, where you will learn to track parameters, manage model artifacts, and control versioning through hands-on labs. The second half of the course focuses on real-world application through a specialized project: building a deployment pipeline for an Insurance Claim application. You will gain practical experience generating synthetic data, setting up dedicated MLflow servers, and utilizing BentoML for high-performance model serving. By upgrading a standard Flask application to interact with a professional serving infrastructure, you will master the art of online model delivery. This course ensures you leave with the technical confidence to register, deploy, and manage machine learning models in a live operational setting.
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In this course, you will bridge the gap between experimental coding and production-ready machine learning by mastering the "Middle Loop" of the MLOps lifecycle. You will start by refining your model development process, learning to distinguish between standard training and hyperparameter tuning to maximize model performance. To ensure operational efficiency, you will evaluate compute strategies by matching your workloads to the specific strengths of CPUs and GPUs. The core of your experience involves building a robust "Source of Truth" using MLflow to automatically log parameters, track metrics, and manage model versions with professional precision. You will move beyond manual tracking by implementing a centralized dashboard that allows for seamless comparison of hundreds of experimental runs. To maintain organizational integrity, you will master the MLflow Model Registry to handle artifact versioning and transitions from staging to production. The course culminates in a hands-on capstone where you will launch a live MLflow server and generate synthetic datasets to simulate a real-world insurance claim review system. By the end, you will have established a fully reproducible training environment, ensuring your AI solutions are organized, searchable, and ready for high-scale deployment.