Machine Learning Operations, or MLOps, is a set of practices that aims to deploy and maintain machine learning models reliably and efficiently.
Similar to the DevOps approach, MLOps seeks to increase automation and improve the quality of production models while focusing on business requirements.
In the first part of this three-episode series on the ‘Transformational Serverless Acceleration for Analytics’, Analyst Christina Stathopoulos speaks to Richard Heyns, Founder and CEO of Brytlyt to cover:
- Why do two-thirds of models built with the intention to deploy never make it to deployment
- Databases and the critical role they play
- Scaling up analytics and ML
Brytlyt is on a mission to make deep learning and instant insight accessible.
Based in the cloud, Brytlyt offers the world’s first all-in-one serverless platform that enables GPU-accelerated analytics and visualisation with in-database AI. We aim to empower the next generation of data scientists and decision makers in every sector – including telecoms, finance, oil & gas, logistics, genomics and retail.
Instant insight and effortless deep learning are truly at the core of what we do. We don’t think that it should take hours to query a complex dataset, nor should it take days to train an effective AI model. Brytlyt can help you dive into deep learning or just add some power to your existing visualisations to bring insight, instantly. Brytlyt will help you retrieve valuable insights from your data in milliseconds.
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