Ai+ Training

As data is rapidly becoming the catalyst for driving innovation, the world is seeing growing adoption of data analytics/Artificial Intelligence technologies. 

Today, there is an evolving paradigm shift from "Web and mobile first" to "AI first". Enterprises are investing massively in data analytics/Artificial Intelligence technologies to drive innovation in order to gain competitive advantage.

However, the foundation of any data analytics and Artificial intelligence is the data infrastructure and pipeline. The first 3 steps of the 6-steps AI hierarchy of need emphasizes the need to build the right data infrastructure. This means that the effort of analytics/Artificial Intelligence will be a complete effort in futility if the data infrastructure is not done right.

In this presentation, Kelvin will be highlighting the importance of building that right data infrastructure to support any analytics/AI initiative.

He will further discuss the foundational concepts and tools needed to build a robust and scalable data infrastructure - data warehouse/data lakes as well as the pipelines to consolidate data into the infrastructure. Further, we will discuss the pros and cons of the various data engineering tools, making choices between proprietary and open-source solutions such as Hadoop, Python, etc. 

Speaker will conclude with a practical end-to-end data engineering use-case showing how enterprises can implement/build the right data infrastructure.

Below are key highlights of the talk:

1. Understanding the importance of building the right data infrastructure

2. The AI hierarchy of need

3. Foundational concepts in data engineering (data warehouse, data lake, and data pipelines). 

4. Choosing the right tools/technology to build your data infrastructure - Pros & Cons. 

5. Open source versus proprietary solutions

6. End-to-end data engineering use-case.