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RESEARCH & RESOURCES

Available On-Demand - This webinar has been recorded and is now available for download.

Simplifying Scalable Cloud Analytics, Data Science, and Machine Learning Workflow

Webinar Speaker: David Loshin, President of Knowledge Integrity

Date: Thursday, May 18, 2023

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

In this online panel discussion, we will cover how a modernized cloud-based data and analytics architecture empowers data analysts, data scientists, and business users. With the increasing data volumes and demand for sophisticated machine learning and AI models, it's crucial to ensure computational performance meets data consumer expectations. We will explore how a multilayered data and analytics framework can provide real-time data insights, analysis, and visualization for various data workloads.

Along with machine learning and AI models, business intelligence platforms can help streamline this work by providing simplified and intuitive data access, delivery, and presentation.

During this webinar, attendees will learn about:

  • Developing scalable data and analytics solutions
  • Enabling data consumers for advanced data workflows
  • Simplifying ad hoc analysis configuration for business decision-making
  • Integrating platform technologies for scalable data solutions development, deployment, and management

Join us to discover how a modernized data environment can revolutionize your data workflows!

Guest Speakers

Keon Shahab
Solution Architect
Databricks

Keon Shahab is an experienced data professional and solutions architect at Databricks working with large financial services organizations. Keon has been a solutions architect at Databricks since 2021 and is passionate about enhancing the adoption of the data lakehouse architecture while helping to modernize the financial services industry with the most cutting-edge technologies.

Stipo Josipovic
Director of Product Management
Sigma Computing

Stipo is director of product management at Sigma, where he focuses on how users can contribute and generate their own data for their analysis. Prior to Sigma, he held leadership and practitioner roles in data engineering, analytics, and modeling in both start-up and traditional domains.

David Loshin


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