• Intermediate

Model Deployment

Model Deployment: From Notebook to Production

This hands-on course guides you through deploying machine learning models into real-world applications. You’ll learn to take models built in Jupyter Notebooks and push them to scalable environments using tools like Flask, FastAPI, Streamlit, Docker, Hugging Face, and cloud platforms. Ideal for learners who want to bridge the gap between model training and production deployment.

💻 Developer-friendly | 🛠️ Project-based learning | 🚀 Real-world deployment strategies
  • 4 weeks content
  • 4 weeks content

What You'll Learn

  • The fundamentals and real-world relevance of model deployment in AI/ML
  • How to serialize and save machine learning models for production use
  • Designing and developing APIs using Flask or FastAPI to serve models
  • Creating endpoints and managing input/output flows for deployed models
  • Best practices for handling dependencies and version control
  • Techniques to securely host and expose machine learning models
  • Understanding the end-to-end architecture of model deployment pipelines
  • Importance of containerization in modern deployment practices
  • Building Dockerfiles and container images to package ML applications
  • Exploring virtualization, Docker, and containerization platforms
  • Deploying RESTful APIs in containerized environments
  • Using tools like Streamlit or Gradio for interactive UI-based deployment
  • Introduction to cloud and server-less deployment options (e.g., Hugging Face, AWS)

Course Curriculum

  • Why model deployment matters
  • Types of deployment: batch vs real-time
  • Overview of deployment architecture

Materials Included

  • Live and recorded instructor-led sessions
  • Project templates with Flask, FastAPI, and Streamlit
  • Docker files for containerized deployment
  • Code examples with Hugging Face and Gradio
  • Deployment walkthroughs on Hugging Face Spaces and AWS/GCP (optional)
  • End-to-end mini-projects (model to API/GUI)
  • GitHub repository access
  • Procelevate Academy Certificate of Completion

Requirements

  • Basic knowledge of Python and machine learning
  • Familiarity with model building using libraries like scikit-learn, TensorFlow, or PyTorch
  • Experience with Jupyter Notebook
  • Internet-connected laptop or desktop
  • (Optional but helpful) Basic command line and Git knowledge

Who This Course Is For

  • AI/ML learners ready to move beyond notebooks
  • Data scientists looking to deploy real-time or batch inference systems
  • Software engineers integrating ML into applications
  • Students working on capstone projects or internships
  • Professionals aiming to build product-ready AI solutions

Your Instructor

Mahesh B. Govindaraj

Mahesh B. Govindaraj

Founder & CEO, Procelevate Consulting

With over ~20 years of experience in global business services, process excellence, and intelligent automation, Mahesh B. Govindaraj brings deep expertise in driving enterprise transformation. As the Founder of Procelevate Consulting, he partners with companies across industries to elevate their operational performance through AI/ML, Lean Six Sigma, and automation-led solutions.

Connect on LinkedIn

Certificate of Completion

₹ 10,000

Exc. GST

₹ 20,000/-

50% Independence Day Discount
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  • Certificate of Completion
Earn a verified certificate upon successful completion of all modules and assessments.
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📊 End-to-End Skill Building | 🏆 Certification Included
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