Deep learning and machine learning applications are becoming the backbone of many businesses in both technological and traditional companies. Once organizations have achieved their first success in using ML/AI algorithms, the main issue they often face is how to automate and scale up their ML/AI workflows. This course will help you to design, develop, and train deep learning applications faster on the cloud without spending undue time and money.

This course will heavily utilize contemporary public cloud services such as AWS Lambda, Step functions, Batch and Fargate. Serverless infrastructures can process thousands of requests in parallel at scale. You will learn how to solve problems that ML and data engineers encounter when training many models in a cost-effective way and building data pipelines to enable high scalability. We walk through some techniques that involve using pre-trained convolutional neural network models to solve computer vision tasks. You'll make a deep learning training pipeline; address issues such as multiple frameworks, parallel training, and cost optimization; and save time by importing a pre-trained convolutional neural network model and using it for your project.

By the end of the course, you'll be able to build scalable and maintainable production-ready deep learning applications directly on the cloud.

About the Author

Rustem Feyzkhanov is a machine learning engineer at Instrumental and creates analytical models for the manufacturing industry. He is also passionate about serverless infrastructures and using them to deploy AI. He has ported several packages on AWS Lambda from TensorFlow/Keras/scikit-learn for ML to PhantomJS/Selenium/WRK to carry out web scraping. One app was featured on AWS serverless repo home page.