End-to-End MLOps: Bringing Value from Modeling to Deployment and Monitoring

Tittle: End-to-End MLOps: Bringing Value from Modeling to Deployment and Monitoring

Abstract: Andrew NG, computer scientist and founder of deeplearning.ai, estimates, that developing an ML models is only 25% of the ML work. According to a recent study by NewVantage Partners, of 70 leading enterprise companies, only 15% have deployed AI capabilities into widespread production. AI that is not deployed to generate value is only a very costly experiment. These experiments are complex technical accomplishments, but they don’t translate into ROI. MLOps allows companies to easily deploy, monitor, and update models in production, paving the way to AI with ROI. So we will discuss where MLOps can help:

1. Issues with Deployment 2. Issues with Monitoring 3. Issues with Lifecycle Management 4. Issues with Model Governance

For that we will discuss, compare and demonstrate some of the leading MLOps tools in the market, free and open-source as well as commercial: WandB, Arize, MLFlow, Qwak, DataBricks, Snowpark, HuggingFace

Program: 17:30 Welcome chat 18:00 Talk 18:50 Discussion 19:10 Networking (Impact Hub)

About MLMUs: Machine Learning Meetups (MLMU) is an independent platform for people interested in Machine Learning, Information Retrieval, Natural Language Processing, Computer Vision, Pattern Recognition, Data Journalism, Artificial Intelligence, Agent Systems and all the related topics. MLMU is a regular community meeting usually consisting of a talk, a discussion and subsequent networking. Except of Prague, MLMU also spread to Brno, Bratislava and Košice.

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