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Because ML is becoming more integrated into daily business operations, datascience teams are looking for faster, more efficient ways to manage ML initiatives, increase model accuracy and gain deeper insights. MLOps is the next evolution of data analysis and deep learning. How MLOps will be used within the organization.
This session covers the technical process, from datapreparation to model customization techniques, training strategies, deployment considerations, and post-customization evaluation. Explore how this powerful tool streamlines the entire ML lifecycle, from datapreparation to model deployment.
With the year coming to a close, many look back at the headlines that made major waves in technology and big data – from Spark to Hadoop to trends in datascience – the list could go on and on. So, we cheer to 2015 and welcome the new year with open arms, ready to embrace the data landscape ahead in 2016.
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TensorFlow The Google Brain team created the open-source deep learning framework TensorFlow, which was made available in 2015. Developed by François Chollet, it was released in 2015 to simplify the creation of deep learning models. Notable Use Cases in the Industry H2O.ai Guidance for Use H2O.ai Further Reading and Documentation H2O.ai
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Detailing ethics practices throughout the AI lifecycle, corresponding to business (or mission) goals, datapreparation and modeling, evaluation and deployment. The method merges best practices in datascience, project management, design frameworks and AI governance. The CRISP-DM model is useful here.
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