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A rejoint le : 13 mai 2022

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What if there are no data sources at all? There's a new machine learning platform to handle that. Kaggle has announced Kite, a platform for building and deploying machine learning applications on containers, with the aim of enabling developers to experiment with machine learning on real data without needing any large data sets or fancy research tools. In the context of machine learning, this is an exciting new development. We've already seen the rise of DAWN, a turnkey solution for deploying machine learning workloads across a wide variety of compute architectures, and the emergence of many automated machine learning tools designed to make it easier to get started. Kite stands out because it's really designed to work with the tools developers are already familiar with, while helping to eliminate much of the setup required. To understand what makes Kite so appealing, I thought I'd highlight some of the highlights. Data science is meant to get to the root of an issue. One of the biggest frustrations for data scientists is accessing data and then transforming it into something that can be analyzed. The Kite service uses a container-based architecture to provide a small layer of abstraction around that process, allowing developers to send and receive data from the Kite service's connector and then launch any number of machine learning pipelines with just a few lines of code. That means developers don't need to set up complicated data pipelines before they can start exploring machine learning ideas. They can just start running code and see how their models perform in the real world. All the while, they don't need to worry about re-purposing the data. Data is already being saved for the Kite service. The other thing that's great about Kite is that it brings up the point that many data scientists may not have the tools they need to get work done. Since containers are all about allowing developers to run pretty much anything from the command line, the Kite service is able to pack all of the functionality needed for the full data science workflow into a single platform. Developers don't need to be data scientists to work with Kite. Kite makes it easy for developers to gain access to data they can then use for experimentation. Kite also makes it easy for machine learning practitioners to get into machine learning development with the minimum of setup. Kite doesn't require any servers to run, so there's no need to worry about having an existing network infrastructure. This is great for getting access to data. Since the Kite service is hosted as an application on the cloud, there's no need to worry about

 

 

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