NVIDIA EGX Platform Integrations

The Domino Enterprise MLOps Platform helps data science teams improve the speed, quality, and impact of data science at scale. Domino is open and flexible, empowering professional data scientists to use their preferred tools and infrastructure. Data science models get into production fast and are kept operating at peak performance with integrated workflows. Domino also delivers the security, governance and compliance that enterprises expect. The Self-Service Infrastructure Portal makes data science teams become more productive with easy access to their preferred tools, scalable compute, and diverse data sets. By automating time-consuming and tedious DevOps tasks, data scientists can focus on the tasks at hand. The Integrated Model Factory includes a workbench, model and app deployment, and integrated monitoring to rapidly experiment, deploy the best models in production, ensure optimal performance, and collaborate across the end-to-end data science lifecycle. The System of Record has a powerful reproducibility engine, search and knowledge management, and integrated project management. Teams can easily find, reuse, reproduce, and build on any data science work to amplify innovation.

Junos Traffic Vision, a licensed traffic sampling app for MX Series 3D Universal Edge Routers, is available. It provides detailed information on network traffic flows, which is useful for many operations and planning activities. Junos Traffic Vision monitors packets while they are being processed by the router and captures information such as source and destination addresses, packet count information, and packet and byte count information. These details are gathered and exported in a standards-based format to allow Juniper and third-party tools to analyze and present them. This includes usage-based accounting, traffic profiling and traffic engineering. Junos Traffic Vision is a high-performance, scale implementation that can be used inline or on service cards. It can be used alongside lawful intercept filtering or port mirroring without affecting performance.

We want ML engineers not to have to worry about data engineering and instead focus on what they are passionate about, building better models in a shorter time. Our product is a powerful framework which makes it 10x faster and easier for developers to explore and understand visual data at scale. Automate the creation of custom interval attributes with pre-trained extractors, or any other model. Custom attributes can be used to visualize data and analyze model performance. You can query, find rare edge cases, and curate training data across your entire data lake using custom attributes. You can easily save, modify, version, comment, and share frames, sequences, or objects with colleagues and third parties. SiaSearch is a data management platform that automatically extracts frame level, contextual metadata and uses it for data exploration, selection, and evaluation. These tasks can be automated with metadata to increase engineering productivity and eliminate the bottleneck in building industrial AI.

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