Company to showcase its flagship software solution, MCenter, which helps enterprises deploy and scale machine learning
WHAT: ParallelM, a rapidly growing new company pioneering in the machine learning operationalization (MLOps) space, will showcase MCenter, the first solution that delivers a unique approach for MLOps and addresses the issues associated with machine learning (ML) in production.
WHO: Sivan Metzger, CEO, and others
WHEN: Wednesday, October 3rd, from 10:00am-8:00pm BST; Thursday, October 4th, from 10:00am-4:30pm BST
WHERE: The ParallelM Booth (#16) at ExCeL London
One Western Gateway, Royal Victoria Dock, London E16 1XL
WHY: Currently, enterprises face significant challenges when operationalizing machine learning in production. Today’s solutions, processes and collaboration methods inhibit ML from being scaled and delay the desired ROI and benefits that ML generate. ParallelM’s MLOps solution, MCenter, incorporates technology as well as best practices for developing processes and collaboration in order to help enterprises get the most out of their machine learning initiatives.
ParallelM is the first and only company completely focused on delivering machine learning operationalization (MLOps) at scale. ParallelM’s breakthrough MCenter™ solution is built specifically to power the deployment and management of machine learning pipelines in production, so that companies can scale machine learning delivery across their business applications. ParallelM’s approach is that of a single, unified MLOps solution that embeds best practice processes in technology, enabling collaboration across all ML stakeholders to unlock the business value of AI. To learn more, visit www.parallelm.com.
MLOps (a compound of “machine learning” and “operationalization”) is the practice of operationalizing and managing the lifecycle of ML in production. MLOps establishes a culture and environment where ML technologies can generate business benefits by optimizing the ML lifecycle to automate and scale ML initiatives and optimized business return of ML in production. MLOps enables collaboration across diverse users (such as Data Scientists, Data Engineers, Business Analysts and ITOps) on ML operations and enables a data-driven continuous optimization of ML operations’ impact or ROI (Return on Investment) to business applications. For more information, visit MLOps.org.