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Description

Daft is an advanced framework designed for ETL, analytics, and machine learning/artificial intelligence at scale, providing an intuitive Python dataframe API that surpasses Spark in both performance and user-friendliness. It integrates seamlessly with your ML/AI infrastructure through efficient zero-copy connections to essential Python libraries like Pytorch and Ray, and it enables the allocation of GPUs for model execution. Operating on a lightweight multithreaded backend, Daft starts by running locally, but when the capabilities of your machine are exceeded, it effortlessly transitions to an out-of-core setup on a distributed cluster. Additionally, Daft supports User-Defined Functions (UDFs) in columns, enabling the execution of intricate expressions and operations on Python objects with the necessary flexibility for advanced ML/AI tasks. Its ability to scale and adapt makes it a versatile choice for data processing and analysis in various environments.

Description

Dash & Dash Enterprise allow you to build and deploy analytic web applications using Python, R, or Julia. No JavaScript or DevOps are required. The world's most successful companies offer AI, ML and Python analytics at a fraction of the cost of full-stack development. Dash is the way they do it. Apps and dashboards that run advanced analytics such as NLP, forecasting and computer vision can be delivered. You can work in Python, R, or Julia. Reduce costs by migrating legacy per-seat license software to Dash Enterprise's unlimited end-user pricing model. You can deploy and update Dash apps faster without an IT or DevOps staff. You can create pixel-perfect web apps and dashboards without having to write any CSS. Kubernetes makes it easy to scale. High availability support for mission-critical Python apps