In the News
In the News
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.
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...
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
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.
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 ...
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.
August 07, 2018
ParallelM to Host Webinar on Simplifying the Deployment and Scaling of Machine Learning in the...
The CTO will provide insight on how the right technology and processes can positively impact ML initiatives
July 24, 2018
Cognitive Technology: Machine Learning Platforms
ParallelM CEO, Sivan Metzger, discusses machine learning platforms, their role in the enterprise, and the importance of MLOps.
July 17, 2018
Putting AI to Work with MLOps
This episode of the Big Data Beard Podcast features ParallelM CEO, Sivan Metzger, and CTO, Nisha Talagala, discussing MLOps.
July 16, 2018
DZone Research: Issues Affecting AI ROI
ParallelM CTO, Nisha Talagala, provides her insights into the issues affecting the ROI of artificial intelligence.
July 10, 2018
MLOps Health: Taking the Pulse of ML in Production
ML pipelines are code, and as such are subject to similar issues as other production software (bugs, etc.) However, the unique nature of...
July 02, 2018
ParallelM to Host Webinar on Why Enterprises Are Not Seeing ROI from AI
The MLOps Company will discuss the barriers that are keeping enterprises from experiencing value from their AI initiatives
June 25, 2018
DZone Research: How AI Is Changing
Organizations are beginning to see real business value from their data and they are able to do so because GPUs have become affordable.
June 21, 2018
DZone Research: How Organizations Benefit From AI
Using automation to improve the efficiency of the operations and empowering employees to focus on more meaningful and less repetitive tasks....
June 13, 2018
Operational ML Spanning Edge to Cloud: What We Showcased at Spark Summit
Last week, ParallelM participated at the Spark + AI Summit held in San Francisco where we presented ‘Operationalizing Edge Machine...
June 13, 2018
DZone Research: Keys to AI Success
AI and ML experts, including ParallelM’s Sivan Metzger and Nisha Talagala, discuss the keys to successful AI strategy.
June 12, 2018
ParallelM Launches its MCenter™ MLOps Solution in Europe; First Software Solution to Deploy,...
The first software solution for operationalizing machine learning and deep learning across the enterprise is now available in Europe
June 11, 2018
ParallelM Named AIconics Award Finalist for Best Application of AI in Financial Services
We are excited to announce that ParallelM has been named as a finalist for Best Application of AI in Financial Services at The AIconics...
June 01, 2018
Is Machine Learning Everywhere? Not Quite, But It Could Be
Successfully deploying ML across an enterprise is not an easy feat. All areas of the organization must learn how to work together and...
April 30, 2018
ParallelM Selected for Microsoft ScaleUp Tel Aviv Accelerator Program
Prestigious program will help drive the continued development of ParallelM MLOps™ and its adoption on Microsoft Azure
Try MCenter and See How Much Easier ML In Production Can Be