Model Deployment Courses and Certifications

KodeKloud Credential

Hands-On MLOps Fundamentals for ML Engineers

Master the complete machine learning lifecycle from data ingestion to production deployment through hands-on projects, gaining job-ready MLOps expertise for scalable ML systems.

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

Coursera Credential

GenAI Deployment & Governance

Master enterprise-scale GenAI deployment with hands-on training in ML pipelines, governance frameworks, performance optimization, and responsible AI practices.

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

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

Deploy, Manage, and Orchestrate Your Models

Master containerization and orchestration for ML models using Docker and Kubernetes. Build, deploy, and manage scalable machine learning systems with hands-on projects and real-world applications.

• Coursera
• 1 hour 25 minutes
• Self-Paced
• Paid Course

Coursera

GPU Clusters & Containers

Unlock distributed AI training and production deployment using cloud GPU clusters and containerization strategies for scalable machine learning systems.

• Coursera
• 2 hours 5 minutes
• Self-Paced
• Paid Course

Deploy and Scale AI Models with Cloud Run

Discover how to deploy and optimize AI inference services on Cloud Run with GPU support and data storage integration for efficient serverless ML applications.

• Coursera
• 1 hour 24 minutes
• Self-Paced
• Paid Course

H2O.ai

Data Science and Machine Learning H2O.ai Platforms

Master H2O.ai's comprehensive data science toolkit for automated machine learning, model deployment, and generative AI applications, featuring Driverless AI, Wave App, and Enterprise GPTe.

• Coursera
• 1 hour 12 minutes
• Self-Paced
• Paid Course

Managing Machine Learning Models

Master model lifecycle management from creation to deployment, including governance, testing, monitoring, and automated scoring across SAS, Python, and R environments.

• Coursera
• 8 hours 34 minutes
• Self-Paced
• Paid Course

Whizlabs

Azure ML: Explore & Configure the Machine Learning Workspace

Explore Azure ML workspace configuration, compute management, and data handling to build scalable machine learning solutions and prepare for DP-100 certification.

• Coursera
• 7 hours 1 minute
• Self-Paced
• Paid Course

Coursera

Optimizing and Deploying Computer Vision Models

Explore techniques to optimize and deploy computer vision models, covering dataset analysis, augmentation, model evaluation, training stability, and reproducible AI workflows for real-world applications.

• Coursera
• 7 hours 18 minutes
• Self-Paced
• Paid Course

Packt

Databricks ML in Action

Master practical machine learning on Databricks platform with hands-on projects covering MLflow, Vector Search, AutoML, and complete ML lifecycle from data ingestion to deployment.

• Coursera
• 13 hours 23 minutes
• Self-Paced
• Paid Course

Azure Machine Learning: Deploy and consume models

This course teaches how to deploy and use machine learning models with Azure Machine Learning. You'll learn the essentials of getting your trained models ready for real-world applications and how to make predictions using them within the Azure cloud plat…

• edX
• 1 week, 1-2 hours/week
• Self-Paced
• Free Online Course (Audit)

Packt

From Recipe to Chef - Become an LLM Engineer

Master LLM engineering from fundamentals to deployment, covering data processing, model training, fine-tuning, prompt engineering, and building production-ready AI applications.

• Coursera
• 8 hours 30 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

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