Pavilion secures VMware Ready status for its Hyperparallel flash array offering; boosts data management features

Pavilion Data Systems announced Monday release 2.3 of its Hyperparallel Flash Array with VMware Ready status. The no-compromise enterprise storage platform now supports a complete suite of data management features for consolidation of VM sprawl and native Container Storage Interface (CSI) for simplified migration to PKS, Docker and Kubernetes containers. 

Release 2.3 includes native VMware support for vCenter, VMware Ready support for NFS along with backward compatibility for block-based iSCSI connectivity. Release 2.3 has been beta tested with numerous customers worldwide and is currently available.

The system is capable of supporting hundreds of thousands of Virtual Machines across 72 standard NVMe SSDs with capacities reaching 1+ PB using 16 TB NVMe drives. All of the Pavilion data management functions, including snapshots, thin provisioning, encryption and RAID-6 volume provisioning are now enabled under VMware vCenter for simplicity of scaling and consolidating enterprise VM sprawl.

One of the customers for Pavilion’s VMware consolidation is the Netherlands Central Bureau of Statistics (CBS). CBS selected Pavilion over several traditional all-flash arrays based on performance and footprint manageability. 

Last month, Pavilion Data Systems announced record-breaking results of STAC-M3 benchmarks in a new Securities Technology Analysis Center (STAC) Report.

The STAC-M3 benchmark specifications are maintained by the STAC Benchmark Council, which consists of more than 300 financial institutions and more than 50 vendor organizations. User firms include the largest global banks, brokerage houses, exchanges, hedge funds, proprietary trading shops, and other market participants. 

Such firms designed the STAC-M3 benchmark suite to represent a common set of performance-related challenges in financial time-series analytics. The STAC-M3 results were audited by the STAC, which facilitates the Council.

A solution using the Pavilion Data Hyperparallel Flash Array populated with standard SSDs for the test produced world-record performance in four analytical benchmarks against all other publicly disclosed systems, including systems with direct-attached storage and Intel Optane drives. The Pavilion array came ahead of all other publicly disclosed solutions involving kdb+ and flash arrays in eight tick analytics benchmarks.

“Release 2.3 extends Pavilion’s leadership from webscale and HPC customers directly into the heart of the Fortune 100 where VMware is the predominant operating environment,” said Gurpreet Singh, Pavilion CEO. “By natively supporting VMware NFS, demonstrating the upcoming VMware-native NVMe-oF functionality and enabling all of our data management functions inside vCenter, we enable orders of magnitude more consolidation than the nearest competitor and simplify operations in a familiar way for VMware administrators.”

Pavilion Data announced in August that its Series C funding of US$25 million, bringing the total funding to $58 million. New investors are Taiwania Capital and RPS Ventures.

All existing investors: Kleiner Perkins Caufield & Byers, Korea Investment Partners, DAG Ventures, Artiman Ventures, SK Telecom, and Tyche Partners participated in this round. The investment will accelerate the delivery of the company's NVMe-oF products, expanding to new markets, and growing the team to support customer demand.

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.