Deploy Serverless Machine Learning Models to AWS Lambda
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.
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
-
Milan Pavlović
Data Scientist
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
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