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December 08, 2018
Cloudera and ParallelM Partner to Accelerate the Industrialization of AI
Cloudera and ParallelM Partner to Accelerate the Industrialization of AI
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December 03, 2018
5 trends that will impact machine learning projects in 2019
5 trends that will impact machine learning projects in 2019
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November 16, 2018
ParallelM to Speak on the “7 Deadly Sins of Machine Learning Deployment” at AI Summit New York...
Company’s CEO, Sivan Metzger, will offer insights to business and technology leaders deploying ML applications in production
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November 13, 2018
ParallelM Enables the Next Generation of AI Deployments with New Capabilities
Supports a Wide Variety of Data Science Platforms, New ML Health Techniques, and Capabilities for AI-First Tech Providers
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November 08, 2018
ParallelM Appoints Industry Veteran Dan Darnell as Vice President of Marketing
Brings nearly 20 years of experience to continue to drive market leadership and growth as the need for MLOps increases
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November 05, 2018
ParallelM to Host Webinar on How to Take Machine Learning and Deep Learning Programs Into...
Company’s CTO, Nisha Talagala, will offer insights to data scientists faced with deploying and managing ML and DL models in business...
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November 02, 2018
Google’s AdaNet (Auto-ML for Deep Neural Networks) Meets MLOps
As more and more industries move to Machine Learning and Deep Learning, a lot has been written about the shortage of data scientists...
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October 29, 2018
“Above the Trend Line” – Your Industry Rumor Central for 10/29/2018
ParallelM was selected by Wisely to help operationalize its machine learning...
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October 26, 2018
Wisely Deploys ParalleM's MCenter to Ease Deployment, Operationalize Machine Learning
Wisely, a provider of AI-powered fundraising solutions for not-for-profits, has selected ParallelM’s MCenter to operationalize its...
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October 24, 2018
Wisely Selects ParallelM to Streamline Model Deployment for its AI-Based Fundraising Platform
Ease of Deployment, Built-in ML Health and Production Model Governance Key Factors in the Selection Process
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October 18, 2018
ParallelM Executives to Present and Exhibit at ODSC West in San Francisco
Company to Demo its MLOps solution, MCenter™, at Table 8
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October 17, 2018
Aricent Partners with ParallelM to Enhance AI-Driven Network Operation for Communication Service...
The combined offering will include the leading MLOps solution, MCenter™, as part of Aricent’s Intelligent Autonomous Networking...
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September 27, 2018
ParallelM to Exhibit The Leading MLOps Solution at Strata + AI Summit Europe
The ParallelM team will be talking machine learning operationalization at booth #16 at ExCel London on October 3 and 4.
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September 24, 2018
ParallelM Named a 2018 Gartner Cool Vendor in Data Science and Machine Learning
ParallelM, one of the fastest-growing companies in machine learning operationalization (MLOps), today announced that it has been named a...
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September 10, 2018
ParallelM to Speak and Exhibit The First MLOps Solution at The AI Summit San Francisco
ParallelM CTO, Nisha Talagala, will be presenting "MLOps: From Data Science to Business ROI" on September 19.
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September 07, 2018
Machine Learning in Production Challenges Developers' Skills
Deploying machine learning models requires an entirely different skill set than developing them, and data scientists and engineering teams...
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August 31, 2018
ParallelM to Showcase MCenter at the O’Reilly Artificial Intelligence Conference
Come visit the ParallelM team in booth #309 on September 6-7 to learn more about MLOps and MCenter
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August 28, 2018
Transforming The Enterprise With MLOps
ParallelM CTO, Nisha Talagala, discusses how enterprises can start to think about operationalizing machine learning in everyday operations.
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August 24, 2018
How Do ML Algorithms Differ from Traditional Algorithms?
In this ask the expert, ParallelM CTO Nisha Talagala lays out the similarities and differences between traditional software engineering and ...
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August 10, 2018
Lack of Collaboration Within Banks Holds Back ML Initiatives
ParallelM CTO, Nisha Talagala, talks with Bank Innovation about how collaboration is an obstacle to ML implementation.
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