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Machine Learning Deployments Stalling? Low-Ops/No-Ops Hacks for Data Scientists

Empower your team to scale production ML despite the shortage of ML engineering resources.

Just like no-code/low-code opened up the design of applications beyond developers, no-ops/low-ops for production machine learning empowers data science teams to scale production ML even with few or no ML engineering resources.

Enterprise data teams are having a difficult time hiring enough ML engineers to scale AI across the business.

Often the business resigns itself that getting a model into production will take weeks, if not months, and monitoring the model for ongoing accuracy will be a laborious process requiring additional point solutions.

Scale AI/ML with Operational Automation

But what if you could automate many of the operational challenges for taking a model from the dev environment into production (and, once live, monitoring and managing models on an ongoing basis)?

What you’ll learn in this session:

Join Wallaroo VP of Data Science Nina Zumel and Senior Community Manager Martin Bald as they provide an overview of how simple and fast it can be for data scientists or a small number of ML engineers to support model deployment, validation, observability, and management for dozens, hundreds, or even thousands of models through the Wallaroo solution.

Nina Zumel

VP of Data Science

Martin Bald

Senior Community Manger

Wallaroo levels the machine learning playing field:

Easy, Secure and Scalable Deployment
Launch 3X Faster
12.5X Faster Inferencing
80% Lower Server Costs