Sarahumdani

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In the next two categories, infrastructure and operations, Penchikala said the data space, which typically lags behind in innovation, is now catching up to trends we've seen in development and architecture. For infrastructure, companies are leveraging containers to run Spark jobs, using Kubernetes to scale up and down as demand requires for machine learning solutions, and are implementing GPU-based solutions.

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Sarahumdani03 Aug, 2022Computer & Internet

Data Ops is a DevOps-based approach to operating data systems, which then leads to ML Ops. Typical machine learning life cycles take models, training data and test data, and then require iterating to find which model will work well and be deployed into production. Doing this manually does not meet the needs of business users, and ML Ops tries to automate the process, with versioning and dynamically applying the models.