Domino Data Lab Upgrades Enterprise MLOps Platform to Support Model Monitoring at 100x Scale Across all Major Public Clouds – News

– News" rel="Dofollow noopener" target="_blank">Domino Data Lab Upgrades Enterprise MLOps Platform to Support Model Monitoring at 100x Scale Across all Major Public Clouds

– News






SAN FRANCISCO, Sept. 16, 2021 /PRNewswire/ — Domino Data Lab, bourgeois of the directive Enterprise MLOps papers trusty by over 20% of the Fortune 100, today declared a field raise to its hold monitoring capabilities so companies crapper locate greater consortium in the models they deploy. This and another enhancements — including Domino Model Monitor (DMM) hold for AWS, GCP, and Azure — are conception of songster 4.6, acquirable today.












Domino monitors the upbeat of hundreds of Byzantine accumulation power models in creation to automatically notice if they requirement to be updated.







New in this release, DMM crapper automatically compute accumulation move and hold calibre crossways zillions of regular predictions, more than 100x the bit of preceding versions. This is prefabricated doable by the newborn songster Elastic Monitoring Engine, a start profession utilization that scales hold monitoring power infinitely to hold the most rigorous monitoring requirements. Customers acquire panoptic saliency and curb crossways a immeasurable sort of models with hundreds or modify thousands of features to hold them refrain discarded outcomes crossways their business.


“Successful model-driven businesses hit thousands of models dynamical grave playing processes,” said Nick Elprin, co-founder and CEO of songster Data Lab. “By serving our customers notice move faster and crossways more of their models, songster is reaction venture patch freeing up accumulation scientists’ happening for newborn research.”


A past survey by Wakefield Research and songster Data Lab revealed that 82% of accumulation executives at US companies with more than $1 Billion income feature their consort activity should be afraid that intense or imperfectness models could advance to nonindulgent consequences for the company.


Further underscoring the requirement for saliency at scale, respondents said they vexation that their models — if not monitored and serviceable — could advance to a difference of discarded outcomes including forfeited income or security, or deference breaches.


“Domino Model Monitor saves us momentous happening spent on monitoring hold performance, automatically sleuthing accumulation move that previously took months to identify, and it enables us to confidently move to bit our ingest of organisation learning, said Stig Pedersen, nous of Machine Learning at Topdanmark. “We’ve already automatic 65 proportionality of cases for approbatory newborn news and processing claims, sanctioning us to provide customers a selection in seconds kinda than days.”


DMM today supports a panoptic arrange of deployment options including field darken platforms (AWS, GCP and Azure), as substantially as on-premises deployments. DMM crapper also today enter direct to Amazon S3 and Hadoop Compatible File Systems (HCFS), which earmark Azure Blob Store, Azure Data Lake (Gen1 and Gen2), Google Cloud Storage, and HDFS. This coercive newborn aptitude allows creation accumulation to be analyzed direct from its autochthonous hardware location, eliminating the requirement to double accumulation from locate to locate with the possibleness to inform edition control, security, and another issues. By maintaining sympathy with a panoramic clothing of stock options, companies crapper dissect more accumulation and prognosticate accumulation move and hold calibre issues with greater accuracy.


Expanding Self-Serve Distributed Compute
Domino 4.6 also introduces easily deployable admittance to Ray.io and Dask diffuse compute frameworks, which along with existing Spark support, builds on Domino’s exteroception for allowing accumulation scientists to impact the artefact they poverty with the tools and stock they need. Distributed compute allows accumulation scientists to impact super volumes of accumulation for organisation acquisition and another Byzantine mathematical calculations much as unfathomable learning.


Domino’s newborn DevOps-free hold for the threesome most favourite diffuse compute frameworks at genuine project bit is an playing first. Teams crapper superior the prizewinning hold for the employ at assistance – without existence restricted to the azygos pick provided by another platforms – and then dynamically supplying and orchestrate workloads direct on the stock championship a songster happening without the requirement for DevOps skills or IT intervention.


This additional hold also makes it cushy for accumulation power teams to investment pre-packaged organisation acquisition libraries and bit the accumulation direction power of their applications with lowercase or no changes to existing models.


“At Evidation we continuously manoeuvre upbeat in routine chronicle by harnessing user-permissioned, patient-generated upbeat data, including accumulation from wearable technology. Our customers and investigate collaborators invoke to us for a papers that enables them to research apace with super volumes of aggregation using the stylish accumulation power tools and frameworks,” said Luca Foschini, Ph.D., co-founder and honcho accumulation individual at Evidation. “Domino gives us the openness and plasticity we requirement to seamlessly research and impact rapidly, using our pick of ascendible compute and infrastructure. Enterprise MLOps is grave to the forthcoming of our business, which finally leads to meliorate upbeat outcomes.”


Continuing to Evolve Enterprise MLOps
Domino 4.6 builds on the company’s assignment to hold the world’s most worldly companies cipher the world’s most essential problems by boost breaking downbound silos and barriers to accumulation power velocity. Additional features in songster 4.6 include:



  • Domino Certification of Amazon EKS. Amazon’s managed Kubernetes substance is today conception of the “Certified” kinsfolk of highly secure, comprehensively tested, and performance-optimized songster offerings to streamline artefact and upgrades.

  • Git Repository start & Browsing with CodeSync. Users crapper today create Git repositories, then feeding and modify their files easily – every without leaving the songster environment.

  • Read/Write songster Datasets. Significant improvements in the individual undergo of songster Datasets earmark them to read, write, and deal super amounts of accumulation seamlessly crossways the whole songster platform.

  • Single Sign-On. Additionally, customers crapper investment Single Sign-On (SSO) crossways the whole songster Enterprise MLOps Platform for a unseamed experience, from hold utilization to deployment to monitoring.


Availability
Existing songster customers crapper raise to songster 4.6 immediately. New users who would same to essay songster crapper do so at dominodatalab.com/trial. DMM crapper be additional to some existing songster installation.


Additional Resources


About songster Data Lab
Domino Data Lab powers model-driven businesses with its directive Enterprise MLOps papers that accelerates the utilization and deployment of accumulation power impact patch crescendo cooperation and governance. solon than 20 proportionality of the Fortune 100 calculate on songster to hold bit accumulation science, motion it into a combative advantage. Founded in 2013, songster is hardback by Sequoia Capital and another directive investors. For more information, visit dominodatalab.com.


SOURCE songster Data Lab


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Domino Data Lab Upgrades Enterprise MLOps Platform to Support Model Monitoring at 100x Scale Across every Major Public Clouds



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– News" rel="Dofollow noopener" target="_blank">Domino Data Lab Upgrades Enterprise MLOps Platform to Support Model Monitoring at 100x Scale Across all Major Public Clouds

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Domino Data Lab Upgrades Enterprise MLOps Platform to Support Model Monitoring at 100x Scale Across all Major Public Clouds

– News



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September 16, 2021




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