Model Deployment Courses and Certifications

Coursera

Operationalizing ML Models: MLOps for Scalable AI

Master MLOps practices to transform ML prototypes into scalable, production-ready systems using Docker, MLflow, and Kubernetes. Automate deployments, monitor performance, and optimize AI infrastructure for robust applications.

• Coursera
• 3 hours 40 minutes
• Self-Paced
• Paid Course

Packt

AI Agents and MLOps for Production-Ready AI

Master AI agents and MLOps to build production-ready systems using AutoGen, CrewAI, Docker, and Kubernetes for scalable deployment and automation workflows.

• Coursera
• 5 hours 28 minutes
• Self-Paced
• Paid Course

Predict Baby Weight with TensorFlow on AI Platform

In this lab you train, evaluate, and deploy a machine learning model to predict a baby’s weight. You then send requests to the model to make online predictions. This lab is part of a series of labs on processing scientific data.

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

Build and Deploy an Agent with Reasoning Engine in Vertex AI

Develop and implement an AI agent with reasoning capabilities using Vertex AI SDK for Python in Google Cloud.

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

Pragmatic AI Labs Career Certificate

Next-Gen AI Development with Hugging Face

Master building, deploying, and scaling AI applications using Hugging Face's ecosystem through hands-on projects that run locally without cloud costs.

• Coursera
• 12 weeks, 5 hours/week
• Self-Paced
• Paid Course
• 4 courses

Coursera

Databricks Machine Learning Fundamentals

Master production-grade machine learning on Databricks with hands-on experience in data preparation, MLflow tracking, and model deployment for real-world applications.

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

Learn MLOps for Machine Learning

Discover how to streamline ML workflows with DVC, MLFlow, and AWS tools for tracking experiments, versioning data, and automating model deployment processes.

• Coursera
• 6 hours 39 minutes
• Self-Paced
• Paid Course

Pragmatic AI Labs

Hugging Face Hub and Ecosystem Fundamentals

Discover how to navigate the Hugging Face ecosystem to find, evaluate, and deploy pre-trained ML models for text, image, and audio tasks across 500,000+ options without training from scratch.

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

KodeKloud Career Certificate

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

Edureka

Fine-Tuning & Optimizing Large Language Models

Master parameter-efficient fine-tuning, context engineering, and deployment strategies for large language models using modern tools like Hugging Face and LoRA adapters.

• Coursera
• 13 hours 45 minutes
• Self-Paced
• Paid Course

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 Career Certificate

Gradient to Production: MLOps & Model Serving

Master the full MLOps stack—from data pipelines to model deployment—using FastAPI, Docker, Kubernetes, and Airflow, and learn to ship, monitor, and maintain ML systems in production.

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

Whizlabs

Azure ML: Deploying, Managing, and Experimenting with Models

Master Azure ML model deployment, management, and experimentation with MLOps best practices, AutoML, and Responsible AI implementation for scalable machine learning workflows.

• Coursera
• 7 hours 44 minutes
• Self-Paced
• Paid Course

Coursera Career Certificate

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

Whizlabs

Azure AI & ML: Optimize Language Models for AI Applications

Master Azure AI & ML model deployment, optimization, and MLOps workflows. Build production-ready solutions using Azure AI Foundry, AutoML, and advanced monitoring techniques.

• Coursera
• 8 hours 19 minutes
• Self-Paced
• Paid Course