February 01, 2019
Notes from an ML Ninja – Six Best Practices for Moving Python Code from Development to...
Six Best Practices for Moving Python Code from Experimentation to Production
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December 21, 2018
A Design Pattern for Explainability and Reproducibility in Production ML
How to design ML applications for Interpretability and reproducibility in production
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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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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 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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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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November 03, 2017
What Tesla’s Autopilot Teaches Us about DevOps for High Performance AI-Powered Applications
Tesla’s semi-autonomous Autopilot system has drawn a lot of attention in the automotive industry.
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September 30, 2017
Showcasing Edge/Cloud Machine Learning Management at MEC 2017
As part of the demo we show the improvements in prediction accuracy as both Cloud and Edge collaborate to achieve the best levels of...
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September 27, 2017
More Machine Learning Models than ever, but are they making it into Production?
The above is not a precisely measured statistic, but having engaged with hundreds of data scientists over the past
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