Advanced Computing in the Age of AI|Wednesday, September 23, 2020
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MLOps Initiatives Seek to Boost Stalled Deployments 

The deployment of machine learning models in production is failing to keep pace with the everyday operations of hyper-scalers. Those scaling and deployment gaps are being addressed through collaborations and a new batch of DevOps tools tuned to scaling machine learning deployments.

The latest initiative aimed at expanding the MLOps ecosystem comes via a series of technology partnerships and native integration with tools such as TensorFlow along with expanded cloud infrastructure support. Dotscience, a machine learning operations specialist, announced partnerships with GitLab and Grafana Labs along with platform integrations to TensorFlow, H2O.ai and Scikit-learn.

The London-based MLOps vendor also announced expanded multi-cloud support with Amazon Web Services (NASDAQ: AMZN) and Microsoft Azure (NASDAQ: MSFT).

Collectively, the goal is “setting the bar for MLOps best practices for building production ML pipelines today,” said Luke Marsden, CEO and founder at Dotscience.

The partners also hope to tap into a projected $3.9 trillion market for AI-based opportunities over the next two years. Vendors such as Dotscience and Algorithmia are rolling out new tools aimed at enterprises currently struggling to deploy machine learning models in production. Among the initiatives announced by Dotscience on Wednesday (Dec. 18) is an effort to develop an industry benchmark for enterprise AI deployments.

To that end, platform monitoring specialist Grafana Labs and Dotscience are joining forces to improve visibility into machine learning workloads in production. The partnership would allow statistical monitoring of model behavior, including workloads using unlabeled data.

The partners also said their monitoring framework would help simplify model deployments via the Kubernetes cluster orchestrator.

“By bringing DevOps practices to ML, data science and ML teams can eliminate silos,” said Tom Wilkie, Grafana Labs’ vice president for products.

Separately, Dotscience announced a native integration with GitLab, the open source software repository. The collaboration would allow ML developers to use the Dotscience platform for machine learning data and model management. More than 100,000 developers currently use GitLab as a DevOps platform.

“We are enabling data scientists to deploy on their preferred ML framework.” Dotscience CEO Marsden said.

Those MLOps enhancement will be augmented with expanded cloud support via the AWS Marketplace and Microsoft Azure. The Dotscience platform is available either as a software service or on-premises.

The MLOps specialist also this week said financial API provider TrueLayer will use the Dotscience platform to improve reproducibility, model and data versioning along with provenance tracking. The partnership comes in response to growing demand for improved productivity, collaboration, governance and compliance as AI initiatives ramp up, the partners said.

 

About the author: George Leopold

George Leopold has written about science and technology for more than 30 years, focusing on electronics and aerospace technology. He previously served as executive editor of Electronic Engineering Times. Leopold is the author of "Calculated Risk: The Supersonic Life and Times of Gus Grissom" (Purdue University Press, 2016).

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