Latest
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...
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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
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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.
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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....
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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...
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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.
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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
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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...
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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...
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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
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April 30, 2018
Operational Machine Learning: Seven Considerations for MLOps
Seven key areas to take into account for successful operationalization and lifecycle management (MLOps) of your ML initiatives.
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March 22, 2018
ParallelM Brings Machine Learning Application to Enterprise IT
With its machine learning application MLOps, ParallelM says it is helping enterprises to automate ML deployments.
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March 07, 2018
22 Startups to Watch in ’18
Startups with a mission to address an unanswered problem continue to emerge in the tech sector. These companies tap into new and still...
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March 05, 2018
ParallelM to Showcase MLOps™ Machine Learning Operationalization Solution at Strata Data...
First Software Solution for Automating the Production-ML Lifecycle of Deployment, Model-Management and Governance
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February 21, 2018
ParallelM Aims to Close the Gap in ML Operationalization
A startup named ParallelM today unveiled new software aimed at alleviating data scientists from the burden of manually deploying,...
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February 21, 2018
ParallelM Launches First Machine Learning Operationalization Solution to Deploy, Manage, and Scale...
MLOps™ Accelerates and Streamlines Delivery of Machine Learning Across the Enterprise to Maximize the Business Value of AI
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January 30, 2018
Why MLOps (and not just ML) is your Business’ New Competitive Frontier
From advertising to IoT to healthcare and beyond, virtually all industries are adopting or investigating machine learning (ML) to benefit...
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January 22, 2018
How MLOps Helps Automate ML Deployment Lifecycles
ParallelM’s MLOps software allows an organization to construct production ML workflows that manage end-to-end deployment cycles
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November 28, 2017
Model Governance: What is it and why is it needed in production ML?
Model governance capability is essential for auditing, report generation, business decision analysis and fault analysis
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November 16, 2017
Why your ML should be deployed as a Micro-service
Machine Learning (and Deep Learning) are popping up everywhere.
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