The 10 best MLOps courses in 2026

This course prepares you to leverage your advanced skills in model development, optimization, and AI ethics to pursue senior career opportunities. You will learn how to strategically position yourself for roles such as Senior Machine Learning Engineer, MLOps Engineer, and AI Ethics Specialist by building a portfolio that showcases your end-to-end project experience and preparing for advanced technical interviews focused on system design and responsible AI.

This course is an introduction to Vertex AI Notebooks, which are Jupyter notebook-based environments that provide a unified platform for the entire machine learning workflow, from data preparation to model deployment and monitoring. The course covers the following topics: (1) The different types of Vertex AI Notebooks and their features and (2) How to create and manage Vertex AI Notebooks.

Technical expertise alone won't land your next role—you need to communicate your skills clearly and confidently to hiring teams. This course helps you translate your hybrid cloud and AI infrastructure knowledge into a compelling professional narrative for senior-level positions. You'll learn to articulate your experience with cloud architecture, MLOps, container orchestration, and edge infrastructure in the language hiring managers and technical recruiters look for. You'll optimize your resume and LinkedIn profile with industry-relevant keywords, and practice interview scenarios specific to e

Learn to deploy ML models to production using the Sovereign Rust Stack—a pure Rust implementation with zero Python runtime dependencies. This hands-on course teaches you to work with three critical model formats (GGUF, SafeTensors, APR), implement MLOps pipelines with CI/CD and observability, and deploy models across GPU, CPU, WebAssembly, and edge targets. Through real-world projects including a Python-to-Rust transpiler (Depyler), browser-based speech recognition (Whisper.apr), and LLM inference benchmarking (Qwen), you'll master format conversion, cryptographic model signing, and performan

This course equips you with the essential skills to take generative AI models from development to production. You will learn to implement robust MLOps practices on Azure, including automated CI/CD pipelines, version control, and full lifecycle management for your models. Simultaneously, you will dive into the critical principles of Responsible AI, using Microsoft’s framework to build fair, transparent, and ethical models that you can deploy with confidence.

In this comprehensive course, you will explore the intricate world of Large Language Models (LLMs) and gain the skills to design, train, and deploy them using cutting-edge MLOps practices. LLMs are revolutionizing the AI landscape, and understanding how to develop and manage them is essential for AI professionals. This course is designed to help you not only grasp the core concepts behind LLMs but also give you hands-on experience to build production-grade LLM systems. You'll learn how to create scalable, efficient LLM systems from scratch, focusing on real-world applications that will make y

This intermediate course provides a practical, hands-on exploration of Databricks Governance, focusing on the essential tools and workflows for managing and securing your data lakehouse. You will learn to navigate and control access to your data assets using Unity Catalog, the foundation of Databricks governance. The course covers the core hierarchy of metastores, catalogs, schemas, and tables, and teaches you how to manage them programmatically using the Databricks Python SDK, CLI, and VS Code extension. Beyond foundational access control, you will master the skills to implement modern CI/CD

This course provides an intermediate-level exploration of MLOps, focusing on how machine learning systems are scaled, productionized, and managed across feature engineering, training, orchestration, serving, and deployment. You will examine how modern ML solutions use feature stores, Kubernetes, Kubeflow, distributed training, advanced serving frameworks, progressive release strategies, and inference optimization techniques. Through hands-on demonstrations and practical exercises, you will gain experience building reliable and scalable workflows with industry-standard technologies such as Fe

AWS and DeepLearning.AI course on LLM architecture, fine-tuning, and deployment.

Design production ML systems: data pipelines, deployment, monitoring, and lifecycle management.