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Deploy Serverless Machine Learning Models to AWS Lambda

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Deploy Serverless Machine Learning Models to AWS Lambda, available at $109.99, has an average rating of 3.95, with 62 lectures, 1 quizzes, based on 291 reviews, and has 2590 subscribers.

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You will learn about Deploy regression, NLP and computer vision machine learning models to scalable AWS Lambda environment How to effectively prepare scikit-learn, spaCy and Keras / Tensorflow frameworks for deployment How to use basics of AWS and Serverless Framework How to monitor usage and secure access to deployed ML models and their APIs This course is ideal for individuals who are Beginner Machine Learning and DevOps Engineers, Data Scientists or Solution Architects or All Data Scientists and ML practitioners who need to deploy their trained ML models to production, quickly and at scale, without much bothering with infrastructure It is particularly useful for Beginner Machine Learning and DevOps Engineers, Data Scientists or Solution Architects or All Data Scientists and ML practitioners who need to deploy their trained ML models to production, quickly and at scale, without much bothering with infrastructure.

Enroll now: Deploy Serverless Machine Learning Models to AWS Lambda

Summary

Title: Deploy Serverless Machine Learning Models to AWS Lambda

Price: $109.99

Average Rating: 3.95

Number of Lectures: 62

Number of Quizzes: 1

Number of Published Lectures: 62

Number of Published Quizzes: 1

Number of Curriculum Items: 66

Number of Published Curriculum Objects: 66

Original Price: €24.99

Quality Status: approved

Status: Live

What You Will Learn

  • Deploy regression, NLP and computer vision machine learning models to scalable AWS Lambda environment
  • How to effectively prepare scikit-learn, spaCy and Keras / Tensorflow frameworks for deployment
  • How to use basics of AWS and Serverless Framework
  • How to monitor usage and secure access to deployed ML models and their APIs

Who Should Attend

  • Beginner Machine Learning and DevOps Engineers, Data Scientists or Solution Architects
  • All Data Scientists and ML practitioners who need to deploy their trained ML models to production, quickly and at scale, without much bothering with infrastructure

Target Audiences

  • Beginner Machine Learning and DevOps Engineers, Data Scientists or Solution Architects
  • All Data Scientists and ML practitioners who need to deploy their trained ML models to production, quickly and at scale, without much bothering with infrastructure

In this course you will discover a very scalable, cost-effective and quick way of deploying various machine learning models to production by using principles of serverless computing. Once when you deploy your trained ML model to the cloud, the service provider (AWS in this course) will take care of managing server infrastructure, automated scaling, monitoring, security updating and logging.

You will use free AWS resources which are enough for going through the entire course. If you spend them, which is very unlikely, you will pay only for what you use.

By following course lectures, you will learn about Amazon Web Services, especially Lambda, API Gateway, S3, CloudWatch and others. You will be introduced with various real-life use cases which deploy different kinds of machine learning models, such as NLP, deep learning computer vision or regression models. We will use different ML frameworks – scikit-learn, spaCy, Keras / Tensorflow – and show how to prepare them for AWS Lambda. You will also be introduced with easy-to-use and effective Serverless Framework which makes Lambda creation and deployment very easy.

Although this course doesn’t focus much on techniques for training and fine-tuning machine learning models, there will be some examples of training the model in Jupyter Notebook and usage of pre-trained models.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction: what you will build during the course

Lecture 2: What is Serverless Computing ?

Lecture 3: What is AWS Lambda ?

Lecture 4: What is Serverless Framework ?

Lecture 5: Exposing ML Models through AWS Lambda

Chapter 2: Setting up your system

Lecture 1: Why Linux?

Lecture 2: Pre-configured Virtual Machine Download

Lecture 3: For Mac Users: Setup Instructions

Lecture 4: Installing VirtualBox

Lecture 5: Creating Ubuntu Virtual Machine

Lecture 6: Initial Ubuntu Setup

Lecture 7: Installing Miniconda

Lecture 8: Installing Visual Studio Code

Lecture 9: Installing pip3

Lecture 10: What is Docker ?

Lecture 11: Installing Docker

Lecture 12: Installing Serverless Framework

Lecture 13: Configuring Serverless

Chapter 3: Program Code and Solutions Availability

Lecture 1: Program Code and Solutions Availability

Chapter 4: Hello World from Lambda

Lecture 1: Serverless Create

Lecture 2: Editing serverless.yml File

Lecture 3: First Deployment

Lecture 4: Supporting Services Overview

Chapter 5: Deploying scikit-learn Regression Model

Lecture 1: Intro to Dataset and Frontend Code

Lecture 2: Creating Virtual Environment with Conda

Lecture 3: Simple Dataset Exploration

Lecture 4: Training the Model

Lecture 5: Saving the Model

Lecture 6: Creating Project and Handler Prototype

Lecture 7: Developing Prediction Function

Lecture 8: Testing Lambda Function Locally

Lecture 9: Editing serverless.yml File

Lecture 10: Creating requirements.txt and Deploying Model

Chapter 6: Post Deployment Activities

Lecture 1: Analyzing CloudWatch Reports

Lecture 2: Dealing With Cold Starts

Lecture 3: Important Notice About Scaling

Lecture 4: Basics of Usage Plans and API Keys

Lecture 5: Check S3 storage and Costs

Chapter 7: Deploying spaCy NLP Model

Lecture 1: Intro to spaCy NLP framework

Lecture 2: Creating Virtual Environment with Conda

Lecture 3: spaCy Usage Example in Jupyter Notebook

Lecture 4: Creating Project with Serverless

Lecture 5: Coding Lambda Function

Lecture 6: Unzipping Requirements in handler.py

Lecture 7: Updating handler.py

Lecture 8: Editing serverless.yml File

Lecture 9: Adding requirements.txt and Local Testing

Lecture 10: Deployment and Global Testing

Chapter 8: Deploying Keras ResNet50 Model

Lecture 1: Solution Architecture Overview

Lecture 2: Creating Virtual Environment with Conda

Lecture 3: ResNet50 Usage Example in Jupyter Notebook

Lecture 4: Creating S3 Buckets

Lecture 5: Updated Usage Example

Lecture 6: Creating Project and Editing Handler File

Lecture 7: Finishing Handler File

Lecture 8: Updating Handler and Editing serverless.yml File

Lecture 9: Finishing serverless.yml File

Lecture 10: Testing Lambda Function Locally

Lecture 11: Setting Up Requirements

Lecture 12: Deploying and Global Testing

Lecture 13: Image Upload Settings on AWS

Lecture 14: Visualizing Predictions on the Web Page

Instructors

Rating Distribution

  • 1 stars: 10 votes
  • 2 stars: 3 votes
  • 3 stars: 20 votes
  • 4 stars: 110 votes
  • 5 stars: 148 votes

Frequently Asked Questions


How long do I have access to the course materials?


You can view and review the lecture materials indefinitely, like an on-demand channel.


Can I take my courses with me wherever I go?


Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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