Company’s CEO, Sivan Metzger, will offer insights to business and technology leaders deploying ML applications in production
WHAT: ParallelM, the leader in MLOps (machine learning operationalization), will host a session, 7 Deadly Sins of ML (machine learning) Deployment. During the session, attendees will learn:
- The 7 pitfalls facing business and technology leaders as they attempt to deploy ML applications in production and how to avoid them
- The differences between ML models generated in training and ML applications in production
- The new lifecycle of ML applications
- The practice of MLOps to scale and deliver business value from AI
The Company will also be exhibiting at booth #704, where it will be offering demos of its flagship MLOps solution, MCenter.
WHO: Sivan Metzger, CEO of ParallelM
WHEN: Thursday, December 6, 2018, at 9:30am ET
WHY: If machine learning (ML) is as valuable as we think, why do around 80% of projects fail to make it into production? Companies that have dared to deploy ML in production have run into many issues including skills and technology gaps, security issues, and governance concerns. The truth is that ML applications are different from traditional software applications and require a different approach.
As AI-first innovators and tech giants continue to create competitive advantage and generate considerable business value by deploying hundreds or even thousands of AI enabled applications, the pressure is mounting across industries to do the same or be left behind. To meet this need, a new paradigm in machine learning operations has emerged – MLOps. An MLOps practice establishes an automated and scalable process to deploy, optimize and govern ML applications in production environments.
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 greater productivity across all ML stakeholders to unlock the business value of AI. To learn more, visit www.parallelm.com and mlops.org.
ParallelM and MCenter are trademarks of Parallel Machines, Inc. All other trademarks are the property of their respective registered owners. Trademark use is for identification only and does not imply sponsorship, affiliation, or endorsement.