GigaSpaces Version 15.0 set to operationalize and optimize machine learning; gain actionable insights from data

GigaSpaces announced this week availability of GigaSpaces Version 15.0, including InsightEdge Platform and XAP, to operationalize and optimize machine learning with the required speed, scale, accuracy and management tools. GigaSpaces Version 15.0 powers machine learning operations (MLOps) initiatives, helping enterprises maximize the business value derived from big data. 

Deploying machine learning models in production remains a major challenge for many enterprises. The Gartner Accelerate Your Machine Learning and Artificial Intelligence Journey Using These DevOps Best Practices says that, “according to the 2019 Gartner CIO Survey, AI and ML continue to be viewed as the No. 1 game-changing technology by CIOs. However, most organizations underestimate how long it will take to move AI and ML projects into production.”

GigaSpaces delivers fast in-memory computing platforms for real-time insight to action and extreme transactional processing.  With GigaSpaces, enterprises can operationalize machine learning and transactional processing to gain real-time insights on their data and act upon them in the moment.  

The always-on platforms for mission-critical applications across cloud, on-premise or hybrid, are leveraged by organizations across various verticals, including financial services, retail, transportation, telecom and healthcare. GigaSpaces offices are located in the US, Europe and Asia.

GigaSpaces Version 15.0 simplifies integrating AI workloads with the organization’s core infrastructure, accelerating machine learning deployment and enabling enterprises to more readily experience the business benefits of machine learning models.

GigaSpaces Version 15.0 introduces a new enterprise-grade monitoring and administration tool, Ops Manager, that provides visibility into the components of systems running models including logs, inputs, outputs and exceptions, using different performance visualization techniques. 

The Ops Manager enables continuous monitoring of machine learning pipelines, starting at the cluster level and drilling through to individual services so users can maintain accurate data models and ensure that problems are resolved before they affect overall performance. 

The new AnalyticsXtreme Batch Indexing included in GigaSpaces InsightEdge Version 15.0 optimizes and automates data access and storage with the added ability to move data between the more frequent (cold data) access and infrequent (archive data) access tiers on data lakes and data warehouses.  

The performance of ML models is enhanced since frequently accessed cold data can be retrieved 80 times faster directly from data lakes and processing costs are reduced as data access patterns change. 

GigaSpaces Version 15.0 also provides a native smart space client in Kubernetes that supports remote CRUD operations, task execution, and event-driven analytics providing high throughput and fast serialization, as well as automatic load balancing. Writing and updating of data without a predefined schema allows easy changes to the data model, while ensuring compatibility with JDBC and BI tools so code can be integrated more reliably and faster with lower administrative overhead.

“Machine learning is becoming an essential component of mission critical applications to optimize operations and deliver superior real time customer experiences,” said Yoav Einav, VP product at GigaSpaces. “GigaSpaces Version 15.0 provides enterprises with the machine learning model management capabilities, speed and scale that they need to accelerate their machine learning and artificial intelligence journey.”

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.