Book | Brief | |
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Companies are spending billions on machine learning projects, but it's money wasted if the models can't be deployed effectively. In this practical guide, Hannes Hapke and Catherine Nelson walk you through the steps of automating a machine learning pipeline using the TensorFlow ecosystem. You'll learn the techniques and tools that will cut deployment time from days to minutes, so that you can focus on developing new models rather than maintaining legacy systems. Data scientists, machine learning engineers, and DevOps engineers will discover how to go beyond model development to successfully productize their data science projects, while managers will better understand the role they play in helping to accelerate these projects. |
Why? - build a strategic, smart and strong analytics capability to transform your institution and ensure a future proof competitive advantage. This type of transformation impacts top-line growth—such as those related to institutional transformation and data utilization—as well as productivity and performance. This discipline includes: Agile and rapid prototyping. Analytics capability assessment and transformation. Remember the conviction: #Analyticship:#BI,#ML,#AI,#BigData,#Analytics,#HiEd:
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Saturday, August 15, 2020
Building Machine Learning Pipelines...
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