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Building Reliable Data and AI Systems

Webinar Speaker: James Kobielus, Senior Research Director, Data Management

Date: Wednesday, April 24, 2024

Time: 9:00 a.m. PT / 12:00 p.m. ET

Modern enterprises run on artificial intelligence (AI), and AI’s performance depends on the underlying machine learning (ML) models being built and trained on reliable, high-quality data.

AI/ML developers must ensure that low-quality data doesn’t produce models that perform poorly. A key technique for addressing this “garbage in, garbage out” problem is to proactively detect data reliability, integrity, and quality issues before they wreak havoc on an AI/ML model’s accuracy and performance. Collaboration among data engineers, data scientists, and business stakeholders can help enterprises identify and resolve data quality issues faster. Also helpful: Centralizing data lineage tracking, monitoring data quality metrics, and ensuring data compliance.

Join us to hear James Kobielus, TDWI senior research director, engage with Monte Carlo’s Scott O’Leary and an expert from Databricks in a panel discussion that will explore:

  • The challenges organizations face ensuring high-quality data for AI/ML models
  • How ensuring that data is of high quality contributes to the reliability of AI/ML models
  • How organizations should strengthen data governance to ensure data quality in their AI/ML systems
  • The collaboration platforms, tools, and methodologies data engineers, data scientists, and business stakeholders can use to improve data quality and AI/ML model accuracy

Date: April 24, 2024

Time: 9:00 am PT

James Kobielus

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