Key Considerations for Optimal Machine Learning Deployments
Watch NowMachine 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.
Also, check out 5 Questions When Considering a Machine Learning Deployment Q&A with Forrester Analyst, Mike Gualtieri.
