More and more companies are investing in artificial intelligence and machine learning to bring better experience to their customers and to take better decisions But there has been acute shortage of talent in the industry and this has been preventing companies from implementing ML systems. One way to solve this problem is by removing all the barriers that typically slow down developers who want to use machine learning.
Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. With Amazon SageMaker, you needn’t worry…
Ramanan Balakrishnan is a member of the data science team at Semantics3 — building data-powered software for ecommerce-focused companies. Over the years, he has had the chance to dabble in various fields covering data processing, pipeline setup, database management and data science. When not picking locks, or scuba diving, he usually blogs about his technical adventures on his team’s engineering blog.
Even though AI research and machine learning systems are growing at great speed, there seems to exist a gulf between appreciating these developments and subsequently deploying them. …
Web applications evolve faster than people can keep up. If you are a one-person startup working on a prototype, you might be able to get away with manual system administration for your app. There are a lot of tools available today to help small teams automate a lot of their system administration.
In this workshop, Clifford is not just going to teach you how to use few of these tools, but he will also help define the problem space. You’ll then go through the following systems engineering challenges:
Works at @hasgeek. Malayali. Hillhacker