Key Considerations for Optimal Machine Learning Deployments

Presented by

Mike Gualtieri, John DesJardins

About this talk

Machine learning (ML) is being used almost everywhere, but the ubiquity has not been equated with simplicity. If you solely consider the operationalization aspect of ML, you know that deploying your models into production, especially in real-time environments, can be inefficient and time-consuming. Common approaches may not perform and scale to the levels needed. These challenges are especially true for businesses that have not properly planned out their data science initiatives. In this webinar, Forrester VP and Principal Analyst Mike Gualtieri will share his research on the challenges that businesses face around ML inferencing in production. Mike will then lead a discussion with technology experts John DesJardins of Hazelcast around what data science and IT teams should consider to optimize the outcomes of their ML strategies.

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Hazelcast is a real-time data platform that enables enterprises to capture value at every moment by consolidating transactional, operational and analytical workloads into a single data platform. With Hazelcast, enterprises can increase actionable insights by unifying event streams with contextual insights from traditional data stores at in-memory speeds. From the cloud to the data center to the edge, Hazelcast is unique in its ability to transform processes to help its customers achieve a competitive advantage via material revenue generation, risk management or cost reduction.