In order for AI to be most effective, enterprises need to see a positive ROI. Put another way, the technology needs to help unlock business value.
Data analysis, which helps organizations to lead with data-driven decisions, can be a key driver here. However to do so, the industry needs to be able to leverage models and tools that can efficiently handle vast and complex datasets.
To help address this, tech heavyweight Google has announced that it is teaming up with data acceleration platform SQream for an event on Next-Level ML: Turbocharging workflows.
Attendees can RSVP for the event on Google’s event page.
The event will focus on accelerating ML workflows and transforming data analysis, and will offer the chance for attendees to gain insights from industry leaders, harness Google Cloud Platform (GCP) for faster results, learn about efficient ML with SQream’s high-performance engine, and more.
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At the event, attendees have the chance to hear from a range of experts including VP Product at SQream, Matan Libis (article’s featured photo), who will deliver a session on accelerating ML workflows.
His opening talk will examine at how SQream’s high-performance SQL SaaS analytical engine can positively impact data pipelines and accelerate model training. In addition, Libis will provide examples on new ways to handle data to save time.
Further, when it comes to production-sized data, being able to reach predictions at a faster pace offers an important business edge. At the event Libis will discuss how leveraging Google Cloud GPUs can significantly speed up training and inference and how to simplify ML with Python or SQL.
After, Oren Bouni, Customer Engineer at Google Cloud, will lead a talk on the latest updates from the Google Cloud Platform and its database technologies. As an example, vector databases can transform data analysis outcomes. Bouni will also discuss how embedding data can elevate data analytic outcomes.
The closing at the Google and SQream event will be led by James Pak, VP of Regional Engineering at SQream, and will showcase real-world customer success stories on how technologies can accelerate ML and data-intensive workloads in order to deliver results.
