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There are now hundreds of tools and at least 40 platforms available for MLOps. The rush to the space has created a new problem - **too much choice** There are also a range of open-source tools addressing MLOps addressing MLOPs. Organizations have to figure it out because there is an imperative to get value from ML. We’ve launched two new resources to help with the understanding of the trade-offs in the ML space. We also released a guide to Evaluating MLOps Platforms and a matrix comparison matrix.