Diamanti joins AI/ML sector with its GPU platform that supports containerized workloads on Kubernetes

Diamanti announced availability of its enterprise platform with GPU support for running containerized workloads under Kubernetes, ideal for the demanding requirements of emerging artificial intelligence (AI) and machine learning (ML) applications. 

In conjunction with its recent announcement of Diamanti Spektra, customers can now provide to their end users GPU capacity in cloud clusters for scaling AI/ML workloads on premises out to public clouds to accelerate model development and training.

Diamanti recently announced the close of a $35 million Series C funding round that the company plans to use to ramp global go-to-market initiatives, along with increased investment in engineering resources to drive the roadmap for Diamanti Spektra, as well as new software, SaaS, and hardware solutions for emerging AI and ML workloads.

“AI is quickly proving to be the most disruptive set of new technologies in decades thanks to breathtaking advances in computing power, volume, velocity and variety of data,” said Tom Barton, CEO of Diamanti. “Our platform offers unmatched extensibility and flexibility for containerized workloads and now our customers can add GPU support for a heterogeneous environment under the same Kubernetes umbrella to help with even the most demanding AI/ML requirements.”

The Diamanti platform for AI/ML workloads fully supports Nvidia’s NVLink cross connect GPU card technology for higher performing workloads, as well as Kubeflow, a machine learning framework for Kubernetes that provides highly-available Jupyter notebooks and ML pipelines.

“Cloud-native methodology and software are crossing over with AI and machine learning, with Kubernetes an increasingly attractive option for data scientists to orchestrate the distributed architecture required to run multiple machine learning libraries and frameworks in production at scale,” said Matt Aslett, research vice president, 451 Research. “One key use case across several verticals involves infrastructure that can be quickly spun up or down to support massive simulations.”

Early access Diamanti customers are already benefiting from the new platform support for GPUs in industries as varied as financial services, energy and travel, among others.

For AI/ML applications requiring GPUs, the new Diamanti Spektra solution can also now manage the full lifecycle of containerized workloads across on-premises and public clouds, moving applications and data between Kubernetes clusters as necessary. 

Diamanti Spektra combines the power of Diamanti’s hardware-boosted x86 platform along with cloud-based infrastructure to provide Kubernetes-as-a-Service. Diamanti Spektra is in technology preview.

Dotscience announces advancements to deploy and monitoring for ML models to unblock AI in enterprises

Dotscience announced on Wednesday new platform advancements that offer the easiest way to deploy and monitor machine Learning models on Kubernetes clusters, making Kubernetes simple and accessible to data scientists. New Dotscience Deploy and Monitor features simplify the act of deploying ML models to Kubernetes and setting up monitoring dashboards for the deployed models with cloud-native tools Prometheus and Grafana, reducing the time spent on these tasks from weeks to seconds. 

Dotscience now also enables hybrid and multi-cloud scenarios where, for example, model training can happen on-prem using an attached Dotscience runner, and models can then be deployed to a Kubernetes cluster in the cloud for inference using a Dotscience Kubernetes deployer. 

Dotscience also announced a joint effort with S&P Global to develop best practices for collaborative, end-to-end ML data and model management that ensure the delivery of business value from AI.

While other solutions on the market aim to solve only specific parts of ML development and operations, requiring further integration work in order to provide end-to-end functionality, Dotscience enables data science and ML teams to own and control the entire model development and operations process, from data ingestion, through training and testing, to deploying straight into a Kubernetes cluster, and monitoring that model in production to understand its behavior as new data flows in. 

Furthermore, alongside the built-in Jupyter environment, Dotscience users can now use any development environment they like by using the Dotscience Python library.

Data science and ML teams can use Dotscience to ingest data, perform data engineering, train and test models and then deploy them to CI for further testing before final deployment to production with a single click, command or API call where the models can then be statistically monitored.

Dotscience’s Deploy gives users the ability to handle both building the ML model into a Docker image and deploying it to a Kubernetes cluster; hand the entire CI/CD responsibility over to existing infrastructure, if preferred, or use lightweight built-ins; and track deployment of the ML model back to the provenance of the model and the data it was trained on to maintain accountability across the entire ML lifecycle.

Dotscience’s statistical monitoring feature allows ML teams to define which metrics they would like to monitor on their deployed models and then bring those metrics straight back into the Dotscience Hub interface where the team first developed the model. This allows ML teams to “own” the health of the model throughout the entire development lifecycle and avoids integrations with other monitoring solutions and costly handovers between teams. 

By enabling data science teams to own the monitoring of their models, Dotscience brings the notion of integrated DevOps teams to ML, eliminating silos, maximizing productivity and minimizing mean time to recovery (MTTR) if there are issues with a model.

“While there are visionaries like S&P in the market who also recognize the need for reproducibility, provenance and enhanced collaboration in the model development phase of the lifecycle, our push to simplify deployment and monitoring of AI/ML is based on the market insight that many businesses are still struggling with deploying their ML models, blocking any business value from AI/ML initiatives,” said Luke Marsden, CEO and founder of Dotscience. 

“In addition, monitoring models in ML-specific ways is not obvious to software-focused DevOps teams. By dramatically simplifying deployment and monitoring of models, Dotscience is making MLOps accessible to every data scientist without forcing them to set up and configure complex and powerful tools like Kubernetes, Prometheus and Grafana from scratch,” Marsden added.