Machine Learning Pipelines Courses and Certifications

Automating ML Workflows with SageMaker Pipelines

Master automated ML workflows by building robust SageMaker Pipelines that connect data processing, training, evaluation, and deployment into repeatable, monitored systems.

• CodeSignal
• 3 hours
• Self-Paced
• Free Certificate

Credential

Enterprise Machine Learning with Amazon SageMaker

Master the complete ML lifecycle using Amazon SageMaker to build, train, deploy, and automate scalable machine learning solutions in AWS cloud environments.

• CodeSignal
• 12 hours
• Self-Paced
• Free Certificate
• 4 courses

Amazon Web Services;  Amazon

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

Automate ML workflows with SageMaker Pipelines, covering data processing, model training, evaluation, and deployment. Learn to use the model registry for versioning and managing trained models.

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

Deployment of Machine Learning Models

5888 ratings at Udemy

Learn how to integrate robust and reliable Machine Learning Pipelines in Production

• Udemy
• 10 hours 27 minutes
• Self-Paced
• Paid Course

A Complete Guide on TensorFlow 2.0 using Keras API

2004 ratings at Udemy

Build Amazing Applications of Deep Learning and Artificial Intelligence in TensorFlow 2.0

• Udemy
• 13 hours 6 minutes
• Self-Paced
• Paid Course

Machine Learning with Apache Spark

114 ratings at Coursera

Unlock Apache Spark's potential for machine learning, covering fundamentals, supervised and unsupervised techniques, and Generative AI. Gain hands-on experience with Spark streaming, ML pipelines, and model evaluation.

• Coursera
• 15 hours 54 minutes
• Self-Paced
• Paid Course

Credential

AI for Scientific Research

102 ratings at Coursera

Explore AI applications in scientific research, from data analysis to advanced techniques like neural networks and random forests, culminating in a COVID-19 drug discovery project.

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

H2O.ai

H2O Label Genie Starter Track

Master H2O Label Genie to accelerate data labeling workflows with AI-powered annotation tools for text, image, and audio data, reducing manual effort by over 50%.

• Coursera
• 1 hour 9 minutes
• Self-Paced
• Paid Course

Coursera Credential

Master Agentic AI: Core Principles & Real-World PC

Master autonomous AI agent systems from architecture design to production deployment with hands-on labs, security governance, and portfolio-ready projects.

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

Coursera

Advanced Data Science Techniques (with AWS Integration)

Master sophisticated ML models and AWS deployment through hands-on projects covering feature engineering, time series analysis, and cloud-based machine learning pipelines.

• Coursera
• 1 day 5 hours 29 minutes
• Self-Paced
• Paid Course

Orchestrating a TFX Pipeline with Airflow

This is a self-paced lab that takes place in the Google Cloud console. In this lab, you'll learn to create your own machine learning pipelines using TensorFlow Extended (TFX) and Apache Airflow as the orchestrator.

• Coursera
• 1 hour 30 minutes
• Self-Paced
• Paid Course

Pragmatic AI Labs

MLOps Platforms: Amazon SageMaker and Azure ML

Elevate Your MLOps Game: Master AWS SageMaker and Azure ML for Production-Ready AI Solutions.

• edX
• 4 weeks, 3-6 hours/week
• Self-Paced
• Free Online Course (Audit)

Coursera

Building, Optimizing, and Validating Machine Learning Models

Master building, optimizing, and validating ML models using hyperparameter tuning, cross-validation, SHAP explanations, and automated pipelines for efficient, reproducible workflows.

• Coursera
• 12 hours 9 minutes
• Self-Paced
• Paid Course

Coursera Credential

ML Model Training & Validation

Master end-to-end ML model training, validation, and optimization for production environments through hands-on labs covering data preparation, algorithm comparison, and deployment.

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

Coursera Credential

Machine Learning Made Easy for Software Engineers

Master the full ML lifecycle—from training and optimizing models to deploying production-ready microservices—with hands-on projects covering pipelines, monitoring, and debugging.

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