Course Information
Registration period
Year-round Recruitment
Course Level
Study Mode
Duration
7 Hour(s) 45 Minute(s)
Language
English
Taught by
Milan Pavlović
Rating
3 views
Course Overview
Use Serverless Framework for fast deployment of different ML models to scalable and cost-effective AWS Lambda service.
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 Content
- 8 section(s)
- 62 lecture(s)
- Section 1 Introduction
- Section 2 Setting up your system
- Section 3 Program Code and Solutions Availability
- Section 4 Hello World from Lambda
- Section 5 Deploying scikit-learn Regression Model
- Section 6 Post Deployment Activities
- Section 7 Deploying spaCy NLP Model
- Section 8 Deploying Keras ResNet50 Model
What You’ll 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
Reviews
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AAnonymized User
This course is my starter course of AWS lambda. I was unaware about lambda and it's wonderful services, Now I know about lambda, Docker and many more things along the course. Thank you for preparing such an awesome course starting from beginning to advance. I really learn a lot from it. Thank you!
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SSérgio Veloso
It was what I was expecting, a good course to introduce to ML in AWS using lambda.
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FFrancisco Parrilla Andrade
A good introductory course to using AWS Lamba. There are some compatibility errors and from checking the answers, the presenter excuses in the fact that using the same library version might solve the problem. An update of the serverless framework version and packages should be done.
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EEugene Yan
Generally a good introduction to serverless deployment on AWS Lambda, with a great code-along flow to the chapters to implement a prediction model. However, code and packages used are slightly dated, considering how fast this domain is moving forward in terms of updates (Packages, as well as the AWS platform itself). SpaCy project was undeployable because at time of this review, serverless was already at 2.23.0, compared to the version 1.38.0 that was used for the instructional videos, which resulted in many seemingly unsolvable errors. The errors disappeared after I downgraded serverless, which meant that I spent quite a bit of time debugging code that was already good in the first place. For anybody attempting to follow the code-work in this course, remember that every time you see a package being stated in the command to be executed, if it doesn't have a specific version being specified, it would be prudent on your part to watch the video a little more, see version that Milan has used, then modify the command to use that specific package version via the '==' operator. Technical issues aside, I feel that this course is a great hands-on primer to serverless deployment on AWS platform, and has motivated me to explore this subject further. Thank you Milan for sharing your knowledge on this subject.
