The Artificial Intelligence Infrastructure Workshop: Build your own highly scalable and robust data storage systems that can support a variety of cutt

Chinmay Arankalle has been working with data since day 1 of his career. In his 7 years in the field, he has designed and built production-grade data systems for telecommunication, pharmaceutical, and life science domains, where new and exciting challenges are always on the horizon. Chinmay started as a software engineer and, over time, has worked extensively on data cleaning, pre-processing, text mining, transforming, and modeling. Production-ready big data systems are his forte.

Gareth Dwyer hails from South Africa but now lives in Europe. He is a software engineer and author and is currently serving as the CTO at the largest coding education provider in Africa. Gareth is passionate about technology, education, and sharing knowledge through mentorship. He holds four university degrees in computer science and machine learning, with a specialization in natural language processing. He has worked with companies such as Amazon Web Services and has published many online tutorials as well as the book Flask by Example.

Bas Geerdink is a programmer, scientist, and IT manager. He works as a technology lead in the AI and big data domain. Having an academic background in artificial intelligence and informatics, he has his research on reference architectures for big data solutions published at the IEEE conference ICITST 2013. Bas has a background in software development, design, and architecture with a broad technical view from C++ to Prolog to Scala. He occasionally teaches programming courses and is a regular speaker at conferences and informal meetings, where he presents a mix of market context, his own vision, business cases, architecture, and source code in an interesting way.

Kunal Gera has been involved in unleashing solutions with the help of data. He has successfully implemented various projects in the field of predictive analytic and data analysis using the analytical skill gained over his professional experience and education.

Kevin Liao has a rich experience in applying data science in different industries by building classes of data science solutions for applications ranging from startup fintech products to web-scale consumer-facing web/mobile pages. Kevin started his career as a statistician/quant in a fintech startup. As data scaled, he honed his data engineering skills and established best practices for web-scale data science solutions. Even after moving to a consumer-facing product company, Kevin has continued to develop data science experiences in an online environment space, which requires extremely low latency solutions.

Anand N.S. has more than two decades of technology experience working, with a strong hands-on track record of application of artificial intelligence, machine learning, and data science to create measurable business outcomes. He has been granted several US patents in the areas of data science, machine learning, and artificial Intelligence. Anand has a B.Tech in Electrical Engineering from IIT Madras and an MBA with a Gold Medal from IIM Kozhikode.

1. Data Storage Fundamentals
2. Artificial Intelligence Storage Requirements
3. Data Preparation
4. Ethics of AI Data Storage
5. Data Stores: SQL and NoSQL Databases
6. Big Data File Formats
7. Introduction to Analytics Engine (Spark) for Big Data
8. Data System Design Examples
9. Workflow Management for AI
10. Introduction to Data Storage on Cloud Services (AWS)
11. Building an Artificial Intelligence Algorithm
12. Productionizing Your AI Applications