Deploying Machine Learning Models with Python, Flask, and MLOps

Building a machine learning model is only the first step; the real value comes when your model is deployed and ready to serve real-world applications. This text-based course guides you through the essential workflows of model deployment and MLOps. You will transition from writing local Python scripts to building robust, accessible web services. You will understand how to package models, create secure REST APIs, deploy to serverless environments, and integrate generative AI capabilities into modern applications. What you'll learn: - Understand the core concepts of model serialization, saving, and environment migration. - Build functional REST APIs for Scikit-learn, TensorFlow, and PyTorch models using Flask and FastAPI. - Deploy scalable, serverless machine learning APIs using cloud functions and serverless architectures. - Track model training runs, parameters, and deployments using MLflow for structured MLOps workflows. - Integrate generative AI models, prompt engineering, and LLM APIs into your software solutions. - Leverage AI-assisted development tools to accelerate your model building and deployment pipeline. The course starts with foundational definitions of model serving and API design before advancing to hands-on deployment scenarios. Through clear written explanations and code-focused exercises, you will progress from local API development to cloud-ready production setups. This course is designed for aspiring ML engineers, data scientists, and developers who have a basic grasp of Python and want to learn how to deploy models without needing advanced systems engineering experience. Start your journey into MLOps today and learn how to make your machine learning models accessible to the world.