Deployment of Machine Learning Models

Course Name : Deployment of Machine Learning Models
Students : 8,870
Duration : 10 hrs
Avg Rating : 4.8 (107 ratings)
Original Price : $119.99
Discount Price : 100%OFF
Instructor / Provider : Soledad Galli, Udemy
Course Type : Self Paced Online Course. Lifetime Access
Coupon : Click on ENROLL NOW to apply discount code

You’ll Learn How to

Deployment Scenarios and System Architecture

• Understand various ML deployment scenarios, including Edge and Server deployment.
• Learn the constraints and challenges of each deployment approach.
• Gain the skills to select the appropriate deployment scenario and design the optimal system architecture for your use case.

Edge, Mobile, and Browser Model Deployment

• Deploy ML models on Edge devices and Mobile platforms using TensorFlow Lite (TF Lite) tools.
• Deploy ML models in web browsers with TensorFlow.js (TFJS) for interactive applications.

Production-Grade Model Serving

• Define and understand key model serving qualities, such as latency, scalability, and reliability.
• Explore the landscape of model serving options and identify the best approach for production-level systems.
• Serve models using:
  • Flask or Django for lightweight APIs.
  • TensorFlow Serving (TFServing) for high-performance model serving.
  • Custom infrastructure or Cloud platforms like AWS EC2 with Docker containers.

Model Conversion and Framework Interoperability

• Convert models across frameworks into a common runtime format using ONNX for compatibility and deployment flexibility.

Cloud Integration and Customization

• Build and deploy server-side models using Cloud APIs like TensorFlow Hub, TorchHub, and TF-API.
• Customize pre-trained models on custom datasets or build models from scratch for specialized applications.

MLOps and Monitoring

• Understand the full ML development lifecycle, from data preparation to deployment.
• Learn about MLOps best practices, including model monitoring and addressing model drift to maintain system performance.

Frequently Bought Together

Skills You’ll Gain by the End of This Course

• Confidence in deploying ML models for Edge, Mobile, Browser, and Cloud environments.
• Proficiency in selecting and configuring model serving solutions for production systems.
• Expertise in converting, customizing, and serving models with tools like ONNX, Flask, and Docker.
• Practical knowledge of MLOps workflows and strategies for maintaining and scaling ML systems.

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