Machine Learning Pipelines Courses and Certifications

Amazon Web Services;  Amazon

Lab - Orchestrate a Machine Learning Workflow using Amazon SageMaker Pipelines and SageMaker Model Registry (Korean)

Amazon SageMaker Pipelines와 Model Registry로 데이터 로드부터 모델 평가·편향 탐지·배포까지 자동화된 ML 워크플로를 오케스트레이션하는 실습입니다.

• AWS Skill Builder
• 1 hour
• Self-Paced
• Paid Course

Amazon Web Services;  Amazon

Lab - Orchestrate a Machine Learning Workflow using Amazon SageMaker Pipelines and SageMaker Model Registry (Japanese)

Amazon SageMaker Pipelines とモデルレジストリで、顧客離れ予測モデルの機械学習ワークフローをオーケストレーションする実践ラボ。

• AWS Skill Builder
• 1 hour
• Self-Paced
• Paid Course

Project Peach - Hardware-Accelerated Multi-Cloud AI Workload Platform

Project Peach 通过端到端组件集成、一键部署和 GPU 管理，降低在 vSphere 上运行 AI/ML 工作负载的门槛。

• YouTube
• 41 minutes
• Self-Paced
• Free Video

Toronto Machine Learning Series (TMLS)

Serverless for Machine Learning Pipelines

Explore serverless architecture for ML/DL deployment, utilizing AWS services to organize scalable and affordable deep learning workflows for efficient model training and operationalization.

• YouTube
• 38 minutes
• Self-Paced
• Free Video

Toronto Machine Learning Series (TMLS)

Building Machine Learning Pipelines in Airflow with Jupyter Notebooks

Learn to develop scalable, end-to-end machine learning pipelines using Jupyter Notebooks and Airflow. Explore parameterization with Papermill for efficient, maintainable ML workflows in financial services.

• YouTube
• 38 minutes
• Self-Paced
• Free Video

Transform Your Machine Learning Pipelines with Apache Hudi

Optimize ML pipelines on data lakes with Apache Hudi. Learn to tackle challenges, leverage upserts, incremental processing, and near real-time access for efficient, up-to-date machine learning models and analytics.

• YouTube
• 25 minutes
• Self-Paced
• Free Video

Transforming Jupyter Notebooks to Reproducible Machine Learning Pipelines with Kale

Convert a customer churn prediction notebook into a reproducible Kubeflow pipeline with Kale, then deploy the pipeline.

• YouTube
• 53 minutes
• Self-Paced
• Free Video

Data Council

Testing Models and Data in CI/CD Pipelines for Machine Learning

Explore best practices for testing ML models in CI/CD pipelines. Learn to validate models and data using deepchecks, addressing common pitfalls and ensuring continuous relevance and trustworthiness.

• YouTube
• 48 minutes
• Self-Paced
• Free Video

NDC Conferences

Scaling Machine Learning Pipelines in Cloud

See how Kubeflow can make machine-learning pipelines portable and scale their infrastructure from a laptop to production clusters.

• YouTube
• 54 minutes
• Self-Paced
• Conference Talk

MLOps World: Machine Learning in Production

Creating Machine Learning Retraining Pipelines with Kubeflow and Kubernetes

Develop ML retraining pipelines using Kubernetes and Kubeflow. Master advanced techniques for efficient model updates and deployment in production environments.

• YouTube
• 2 hours 5 minutes
• Self-Paced
• Free Video

MLOps World: Machine Learning in Production

Hands-on Workshop: Create Machine Learning Retraining Pipelines

Develop ML retraining pipelines using Kubernetes and Kubeflow. Master advanced techniques for efficient model updates and deployment in production environments.

• YouTube
• 2 hours 22 minutes
• Self-Paced
• Free Video

Conf42

AI-Driven ETL Evolution - Building Intelligent Data Pipelines with Machine Learning

Explore AI-enhanced ETL pipelines with intelligent extraction, ML-powered transformation, and self-healing capabilities for modern data processing challenges.

• YouTube
• 12 minutes
• Self-Paced
• Free Video

JuliaHub

Deploying Trained Machine Learning Pipelines Behind a Webserver with Dashboards.jl

Discover how to deploy trained machine learning models using Dashboards.jl, enabling network accessibility and scaling for larger deployments with practical examples and implementation tips.

• YouTube
• 42 minutes
• Self-Paced
• Free Video

Full-Stack AI Engineer 2026–Machine Learning Foundations - I

53 ratings at Udemy

Build strong Machine Learning foundations with Python, real projects, and a Full-Stack AI Engineer mindset

• Udemy
• 8 hours
• Self-Paced
• Paid Course

Career Certificate

Machine Learning/AI Engineer

Build end-to-end machine learning applications by combining foundational and intermediate ML with software engineering, pipelines, and a portfolio project.

• Codecademy
• 50 hours
• Self-Paced
• Free Trial Available