An Overview of Algorithmia: How to Deploy, Manage, and Scale Your Machine Learning Model Portfolio


Learn about Algorithmia's machine learning operations and management platform that empowers teams to deploy models, connect to various data sources, automatically scale model inference, and manage the ML lifecycle in a centralized model catalog. We'll demonstrate the process of deploying a credit risk model for real-time inference as well as a sentiment analysis ML pipeline to Algorithmia that uses different languages and frameworks. We'll demonstrate data science workflows for deploying a model, adding an OCR tool to the pipeline, testing a new version of the model, then calling the model from different languages. We'll also discuss how Algorithmia handles the underlying MLOps infrastructure and operations related to security, scalability, and governance.


Kristopher Overholt is a Sales and Solution Engineer at Algorithmia who works with machine learning operations, enterprise architecture, and data science workflows. He studied civil engineering at The University of Texas at Austin, where he completed his PhD in 2013. He has been working with enterprise customers for the last 6 years to help them move their data science and machine learning code into production.

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