easylearn.ing
Easy Learning with Deployment of Machine Learning Models
Development > Data Science
10 h
£39.99 £12.99
4.8 ★★★★★
10834 students

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Language: Arabic

Mastering Machine Learning Model Deployment: From Development to Production

What you will learn:

  • Deploy ML models to edge devices (Raspberry Pi, Android) and mobile apps.
  • Optimize models for efficient deployment using compression techniques (pruning, quantization, distillation).
  • Implement browser-based deployments using TensorFlow.js (TFJS).
  • Master server-side deployment using various frameworks (Flask, Django, TensorFlow Serving).
  • Leverage cloud-based platforms (TFHub, AWS EC2) for scalable deployments.
  • Utilize Docker containers for efficient and reproducible deployments.
  • Employ ONNX for cross-framework model compatibility.
  • Implement Model Monitoring and MLOps.
  • Build robust, scalable systems with considerations for model serving qualities.
  • Design efficient model architectures for various deployment scenarios.

Description

Are you an AI/ML engineer, researcher, or software developer ready to bridge the gap between model development and real-world applications? This comprehensive course empowers you to deploy your machine learning models effectively across various platforms. Whether it's embedding AI into a mobile app, optimizing performance on resource-constrained edge devices like Raspberry Pi, deploying to browsers using TFJS, or building robust, scalable server systems for millions of users – we've got you covered.

We delve into crucial computer vision (CV) deployment techniques, addressing model compression strategies such as pruning, distillation, and quantization. Learn to leverage optimized convolutional operations (Depthwise Separable, Group Convolutions, etc.) and explore the architecture of efficient models like MobileNet, EfficientNet, and SqueezeNet for optimized deployment. We'll guide you through practical implementation on diverse platforms, including Android, embedded systems, and web browsers, while also delving into the theory behind the methods used. From setting up cloud-based deployments with TFHub to building custom solutions using Flask and Django on AWS EC2 and leveraging Docker containers for efficient model serving, you'll master the entire process.

The course covers critical aspects of model serving, including optimizing for speed, latency, and scalability. Discover robust strategies for serving your model using Flask and Django frameworks, deploying with Docker containers, and leveraging the power of TensorFlow Serving. Finally, we explore the essential concepts of ONNX for interoperability and MLOps for managing the entire machine learning lifecycle. This course is your definitive guide to deploying your AI vision from concept to production.

Curriculum

Introduction

This introductory section lays the foundation for successful model deployment. The "Deployment course intro" lecture sets the stage, followed by "Course contents and overview," providing a roadmap. "Deployment pipeline" walks you through the process, while "Deployment constraints" identifies challenges, and "Deployment scenarios" explores various application contexts. Together, these lectures provide a holistic starting point for the journey ahead.

Device Deployment

This section dives deep into deploying models on diverse edge devices and mobile platforms. Starting with "Model compression overview," you'll learn crucial techniques like pruning, distillation, and quantization for efficient model deployment. Lectures on various optimized convolution types (Depthwise Separable, Group Convolutions, etc.) and the architectures of optimized models such as MobileNet, EfficientNet, and SqueezeNet prepare you to deploy to different resource-constrained environments. This is followed by practical guides on TFLite usage, hands-on sessions involving Raspberry Pi, Android app development (static and camera-based image classification and object detection), and web browser deployment using TFJS. This section culminates in "Device deployment wrap up", reinforcing key concepts and best practices.

Server Deployment

This section focuses on deploying models on servers for scalable and high-performance applications. You'll start by exploring the general principles of "Client server overview and model options." Next, you will learn how to leverage cloud based deployment and TFHub. Then we dive into practical deployment scenarios such as object detection, including customizing models with TF-API and building from scratch. We will cover several options for model serving (Flask, Django, TensorFlow Serving, and Docker). The lectures on "Serving qualities" and "Serving landscape" provide guidance on selecting the right tools for diverse application needs. We will conclude this section with detailed practical examples using Flask, Django on AWS EC2, TFServing Native Deployment, and Docker.

Wrap-up

In the concluding section, we bring together the key concepts from the previous modules and discuss advanced topics. The "ONNX" lecture introduces a crucial format for model interoperability, while "MLOps" focuses on the management of the machine learning lifecycle. The course concludes with a summary in the "Conclusion" lecture, reinforcing the comprehensive skills gained.

Material

This section provides access to supplementary materials to support your learning, including course slides.