ONNX Integrations

The workflow automation platform that automates complex, mission-critical data processing and ML processes at large scale. Flyte makes it simple to create machine learning and data processing workflows that are concurrent, scalable, and manageable. Flyte is used for production at Lyft and Spotify, as well as Freenome. Flyte is used at Lyft for production model training and data processing. It has become the de facto platform for pricing, locations, ETA and mapping, as well as autonomous teams. Flyte manages more than 10,000 workflows at Lyft. This includes over 1,000,000 executions per month, 20,000,000 tasks, and 40,000,000 containers. Flyte has been battle-tested by Lyft and Spotify, as well as Freenome. It is completely open-source and has an Apache 2.0 license under Linux Foundation. There is also a cross-industry oversight committee. YAML is a useful tool for configuring machine learning and data workflows. However, it can be complicated and potentially error-prone.

Edge-optimized SQL engine with AI built-in. Azure SQL Edge is a robust Internet of Things database for edge computing that combines data streaming, time series, and graph features with machine learning. Microsoft SQL engine can be extended to edge devices to ensure consistent performance and security throughout your entire data estate. You can develop your applications once, and deploy them anywhere, whether it's on the edge, in your data center or Azure. Data streaming and time series are built-in, and the database includes machine learning and graph features. Data processing at the edge to overcome latency and capacity constraints. Update and deploy from the Azure portal, or your enterprise portal, for consistent security and turnkey administration. Machine learning capabilities allow you to detect anomalies at the edge and apply business logic.

Our high-throughput systems can serve millions small random files to GPU based training servers, accelerating the overall training time. We offer high-bandwidth networks with low latency for connecting training servers and transporting data from storage to servers. You may be charged extra fees by other cloud providers to remove your data from their storage clouds. These charges can quickly add up. We consider ourselves as an extension of your team. We help you set up scheduling, provide best practices and superior support. Workflows vary from one company to another. Cirrascale will work with you to find the best solution for you. Cirrascale works with you to customize your cloud instances in order to improve performance, remove bottlenecks and optimize your workflow. Cloud-based solutions that accelerate your training, simulation and re-simulation times.

Groq's mission is to set the standard in GenAI inference speeds, enabling real-time AI applications to be developed today. LPU, or Language Processing Unit, inference engines are a new end-to-end system that can provide the fastest inference possible for computationally intensive applications, including AI language applications. The LPU was designed to overcome two bottlenecks in LLMs: compute density and memory bandwidth. In terms of LLMs, an LPU has a greater computing capacity than both a GPU and a CPU. This reduces the time it takes to calculate each word, allowing text sequences to be generated faster. LPU's inference engine can also deliver orders of magnitude higher performance on LLMs than GPUs by eliminating external memory bottlenecks. Groq supports machine learning frameworks like PyTorch TensorFlow and ONNX.