For many data scientists in the enterprise, the deployment of machine learning into production environments has become a second job – and one that most do not want. Current IT and operations teams and tools can’t account for the complexities of deploying, managing and scaling ML applications, leaving data science and data engineering teams on the hook for the success – or failure – of ML and AI initiatives.
In this webinar, data scientists will be introduced to MLOps – an approach for machine learning operationalization that:
· Breaks down the silos between data science and IT
· Streamlines deployment and orchestration
· Adds advanced functionality like ML Health, governance and business metrics
Join Nisha Talagala, CTO, and Craig Michaud, Sales Engineer, from ParallelM – the MLOps Company – for a look at how much easier machine learning can be with the right technology and processes in place. You’ll see how to upload code from your existing data science platforms, run it in a sandbox against production data, conduct AB tests and perform timeline captures.