Deploy Serverless Machine Learning Models to AWS Lambda
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
- Created AWS Account
- Basic familiarity with Python and Machine Learning
- Basic undestanding of Linux and Terminal
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.
Who this course is for:
- 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
After finishing a bachelor degree in Information Systems, I graduated Information and Software Engineering master study at Faculty of Organization and Informatics, University of Zagreb, in 2016. During the study I was 100% of time in top 2% of students and received 5 Dean's awards in total (2011-2016) and 2 Summa Cum Laude honors.
I worked as a teaching assistant for almost two years, after which I moved to industry. During my academic career, I collaborated with Text Analysis and Knowledge Engineering Lab at the Faculty of Electrical Engineering and Computing, University of Zagreb, where I also successfully completed Machine Learning, Deep Learning and Text Analysis and Retrieval master courses. Through this period I gained a solid understanding of machine learning, deep learning and natural language processing.
Currently I work as a Data Scientist for one Croatian startup. My main fields of expertise are natural language processing (text semantics, classification and retrieval). I build AI-powered systems by creating and deploying machine learning models to production environments.