INSIGHT
MACHINE LEARNING SANDCASTLES: Considerations on why we need a dominant design & not another startup building a machine learning platform / By Gregg Barrett, Head of Cirrus /
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t most organisations machine learning (ML) represents a disparate mix of tasks and tools, with data engineers working on data pipelines, data scientists on the data analysis, model training, validation and testing, hardware engineers on the compute configuration, and software engineers on the deployment. That tasks and tools are so segregated contributes to the high overall failure rates of machine learning projects and constrains the quality and quantity of ML project throughput.
This has led to the development of ML platforms. When I refer to an ML platform, I am referring to an all-in-one product for data and model development, scaling experiments across multiple machines, tracking and versioning models, deploying models, and monitoring performance. The landscape for these all-in-one platforms is still underdeveloped though and while this might sound appealing for those with engineering and startup aspirations there is a need for caution. Notes: Gartner: Data Science and Machine Learning (ML) Platforms
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SYNAPSE | 2ND QUARTER 2021
