MLOps Courses and Certifications

Machine Learning Operations (MLOps): Getting Started - Português Brasileiro

Aprenda MLOps no Google Cloud: implantação, avaliação e monitoramento de sistemas de ML em produção. Conheça ferramentas e práticas para automatizar e melhorar continuamente modelos implantados.

• Coursera
• 3 hours 56 minutes
• Self-Paced
• Paid Course

Machine Learning Operations (MLOps): Getting Started - Français

Découvrez les outils et pratiques MLOps pour déployer, évaluer et surveiller efficacement les systèmes de ML en production sur Google Cloud, en utilisant Vertex AI pour automatiser et optimiser le cycle de vie du ML.

• Coursera
• 3 hours 22 minutes
• Self-Paced
• Paid Course

Machine Learning Operations (MLOps): Getting Started - Español

Introducción a MLOps en Google Cloud: implementación, evaluación y supervisión de sistemas de AA en producción usando Vertex AI y prácticas recomendadas.

• Coursera
• 4 hours 22 minutes
• Self-Paced
• Paid Course

Machine Learning Operations (MLOps): Getting Started - 한국어

이 과정에서는 Google Cloud에서 프로덕션 ML 시스템 배포, 평가, 모니터링, 운영을 위한 MLOps 도구와 권장사항을 소개합니다. MLOps는 프로덕션에서 ML 시스템을 배포, 테스트, 모니터링, 자동화하는 방법론입니다. 머신러닝 엔지니어링 전문가들은 배포된 모델의 지속적인 개선과 평가를 위해 도구를 사용합니다. 이들이 협력하거나 때론 그 역할을 하는 데이터 과학자는 고성능 모델을 빠르고 정밀하게 배포할 수 있도록 모델을 개발합니다.

• Coursera
• 3 hours 51 minutes
• Self-Paced
• Paid Course

Machine Learning Operations (MLOps): Getting Started - 日本語版

このコースでは、Google Cloud 上で本番環境の ML システムをデプロイ、評価、モニタリング、運用するための MLOps ツールとベスト プラクティスについて説明します。MLOps は、本番環境 ML システムのデプロイ、テスト、モニタリング、自動化に重点を置いた規範です。機械学習エンジニアリングの担当者は、ツールを活用して、デプロイしたモデルの継続的な改善と評価を行います。また、データ サイエンティストと協力して、あるいは自らがデータ サイエンティストとして、最も効果的なモデルを迅速かつ正確…

• Coursera
• 3 hours 56 minutes
• Self-Paced
• Paid Course

AI Engineer Production Track: Deploy LLMs & Agents at Scale

2525 ratings at Udemy

Deploy AI to AWS, GCP, Azure, Vercel with MLOps, Bedrock, SageMaker, RAG, Agents, MCP: scalable, secure and observable.

• Udemy
• 18 hours 38 minutes
• Self-Paced
• Paid Course

Coursera

Production-Ready Multimodal ML Engineering

1 rating

Master the full ML engineering stack — design multimodal feature stores, automate ETL pipelines, optimize inference with TensorRT, and deploy GPU-accelerated services to Kubernetes for production-ready AI systems.

• Coursera
• 11 hours 56 minutes
• Self-Paced
• Paid Course

Coursera

Engineer Features and Evaluate Models for Production

Discover how to build production-grade ML systems with robust feature engineering pipelines and advanced model evaluation techniques using scikit-learn and TensorBoard.

• Coursera
• 2 hours 46 minutes
• Self-Paced
• Paid Course

Pragmatic AI Labs

Building AI with Bedrock Agent

Develop enterprise-grade AI applications using AWS Bedrock and Python. Master MLOps, IaC, DataOps, and UI creation for generative AI solutions in this hands-on program.

• edX
• 4 weeks, 4-6 hours/week
• Self-Paced
• Free Online Course (Audit)

Coursera Credential

ML Production Systems

Master containerization, deployment, and MLOps practices to transform ML models into scalable production systems using Docker, Kubernetes, and automated pipelines.

• Coursera
• 4 weeks, 10 hours/week
• Self-Paced
• Paid Course
• 4 courses

Coursera Credential

Blueprint to Bytecode: Architecting Scalable AI Systems

Master the full AI systems lifecycle—from data pipelines and GPU clusters to cloud deployment—using Kubernetes, Docker, SageMaker, and MLOps best practices to build scalable, production-ready enterprise AI solutions.

• Coursera
• 4 weeks, 10 hours/week
• Self-Paced
• Paid Course
• 10 courses

Coursera

Deploy, Evaluate and Create AI Systems

Master production-ready AI deployment with containerization, performance optimization, and zero-downtime strategies to prevent the 70% failure rate plaguing AI models.

• Coursera
• 2 hours 7 minutes
• Self-Paced
• Paid Course

Coursera

Automate, Evaluate and Deploy ML Models Confidently

Master production-grade MLOps by building automated CI/CD pipelines with GitHub Actions, analyzing Optuna trials, and implementing validation gates for confident model deployment.

• Coursera
• 2 hours 49 minutes
• Self-Paced
• Paid Course

Coursera

Orchestrate, Analyze, and Evaluate AI Deployments

Master MLOps deployment lifecycle with CI/CD, Kubernetes, and GitLab. Analyze telemetry data, troubleshoot performance issues, and evaluate AI model success against business KPIs.

• Coursera
• 2 hours 13 minutes
• Self-Paced
• Paid Course

KodeKloud

Deploy ML Models to Production

Master MLOps by deploying ML models from development to production using MLflow, BentoML, and AWS SageMaker with hands-on insurance claim application project.

• Coursera
• 3 hours 53 minutes
• Self-Paced
• Paid Course