Model Deployment Courses and Certifications

From Pull To Predict - Accelerating AI Model Deployment on Kubernetes

Discover techniques to accelerate AI model deployment on Kubernetes using SOCI lazy loading, optimized storage, and Karpenter for faster startup times and better resource utilization.

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

EDGE AI FOUNDATION

Write Once, Deploy Everywhere - The Model-Agnostic AI Deployment

Discover OwLite, a model-agnostic AI deployment framework that enables single pipeline reuse across diverse edge devices, eliminating redundant engineering and accelerating rollout.

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

Machine Learning with Python and Scikit-Learn – Full Course

1 rating

Hands-on introduction to machine learning with Python and Scikit-Learn. Covers regression, classification, tree-based models, and best practices. Includes real-world project and cloud deployment using Flask.

• freeCodeCamp
• 18 hours 1 minute
• Self-Paced
• Free Video

Microsoft Azure AI Fundamentals: Explore visual tools for machine learning

1 rating

Microsoft Azure AI Fundamentals: Explore visual tools for machine learning

• Microsoft Learn
• 5 hours 18 minutes
• Self-Paced
• Free Online Course

Credential

AWS Machine Learning Engineer Nanodegree

41 ratings at Udacity

Equip developers with AWS SageMaker skills to build and deploy ML models in production. Learn data science, model design, and best practices for real-world applications.

• Udacity
• 4 months 2 weeks 5 days 11 hours 36 minutes
• Self-Paced
• Paid Course

MLOps Fundamentals: The Complete Guide to ML in Production

38 ratings at Udemy

MLOps fundamentals for real‑world: Lifecycle, pipelines, platforms, monitoring, retraining, governance & ROI explained

• Udemy
• 2 hours 21 minutes
• Self-Paced
• Paid Course

Ace The Machine Learning System Design Interview

11 ratings at Taro

Master a repeatable system for ML system design interviews, learning to structure your thoughts, communicate clearly, and demonstrate senior-level thinking to stand out from other candidates.

• Taro
• 59 minutes
• Self-Paced
• Paid Course

LLMOps: Building Real-World Applications With Large Language Models

3 ratings at Udacity

Master LLMOps principles through hands-on projects: fine-tune models, deploy production applications, implement AI safety measures, and build real-world solutions like chatbots.

• Udacity
• 11 hours
• Self-Paced
• Free Certificate

Introduction to Machine Learning

1 rating at Udacity

Explore machine learning concepts and workflows using AWS SageMaker, from data analysis to model deployment. Learn to apply ML techniques, use advanced algorithms, and optimize models for real-world scenarios.

• Udacity
• 18 hours
• Self-Paced
• Paid Course

Building Generative AI Solutions on Azure with OpenAI GPT Large Language Models

Master building custom AI copilots and applications using Azure AI Studio, Prompt Flow, and OpenAI models with full lifecycle management and GenAIOps deployment strategies.

• Udacity
• 10 hours
• Self-Paced
• Paid Course

Deploying a Machine Learning Model with FastAPI

Master FastAPI deployment techniques for machine learning models using real Census data, covering development, monitoring, and production launch strategies.

• Udacity
• 5 hours
• Self-Paced
• Paid Course

Amazon Web Services;  Amazon

Machine Learning Exam Basics

Explore AWS machine learning services, from building and deploying models to deep learning. Learn from Amazon's scientists about ML business challenges and data processing in the ML pipeline.

• AWS Skill Builder
• 2 hours
• Self-Paced
• Free Certificate

Amazon Web Services;  Amazon

Introduction to Amazon SageMaker

Learn to build, train, and deploy machine learning models using Amazon SageMaker's fully-managed platform, exploring its key components for notebooks, training, and hosting.

• AWS Skill Builder
• 2-3 hours
• Self-Paced
• Free Certificate

Building a Reproducible Model Workflow

Learn to build efficient, reproducible machine learning pipelines using MLflow, pytest, and Weights & Biases. Master data validation, experiment tracking, and model deployment for real-world ML projects.

• Udacity
• 21 hours
• Self-Paced
• Paid Course

TensorFlow on Google Cloud

This course covers designing and building a TensorFlow input data pipeline, building ML models with TensorFlow and Keras, improving the accuracy of ML models, writing ML models for scaled use, and writing specialized ML models.

• Google Skills
• 15 hours 30 minutes
• Self-Paced
• Paid Course