Data Reliability Engineering—Why AI and ML Pipelines Fail: The Exploratory Data Analysis Problem

The automation that AI and ML provide has been widely seen as a solution to dealing with the complex nature of real-world data. Companies have rushed to take advantage of AI and ML to supercharge their businesses. Yet, most AI and ML initiatives fail. Why? Because data scientists aren’t effectively exploring the data.

Data scientists are bogged down by huge volumes of data in increasingly dynamic environments without the tools to effectively explore their data. As a result, shifting patterns in the data erode even the highest-performing pipelines.

In this webinar, Henry Li, senior data scientist at Bigeye and former data scientist at Uber, will discuss the importance of exploratory data analytics for AI and ML pipelines, how it’s done, why it’s a struggle for most data teams, and how data monitoring can help.

Watch this presentation to learn:

• Why exploratory data analysis keeps so many companies from making the leap to being data-driven
• How it affects the day-to-day work of data scientists
• How Bigeye can help by giving data teams have complete awareness of what’s happening with their data at all times