Master Designing, Integrating & Deploying Enterprise AI Apps

Target Audience Machine Learning Engineers & Data ScientistsWhat is unique about this course & What will you learn?Why What & How of designing, integrating & deploying Enterprise Level Data Science/AI/ML applicationsHow to translate requirements into scalable architectural components?How to break a big complex problem into simple & manageable parts using microservices style architecture?An End-to-End real-world enterprise-level machine learning solutionAsynchronous IO - Foundations & Writing I/O bound applications in python 3NATS - A Cloud Native Computing Foundation open source project to connect distributed applicationsFlatBuffers - A language-independent, compact and fast binary structured data representation languageDocker & Docker-compose - The gold standard in deploying and orchestrating applicationsWhy should you learn all this?A statistical or deep learning model is not an application rather it is an important component of a solution to real-world problems. A sophisticated solution to a complex problem generally consists of multiple applications written using different languages and running on a cluster of machines.Your role as a Data Scientist and Machine Learning engineer is not just limited to a model building or tuning its performance rather it is expected that at the very minimum you will design your applications so that they can easily integrate with other applications of a big solution as well as are easily deployable using modern DevOps methodologies. Mastering how to make AI applications integrate with other applications while ensuring scalability and upgradability will offer you a competitive advantage over others. The good news is that mastering them is not difficult at all!<