Predictive Open Source ML Modeling: A Guide to Multiple Time Series Forecasting


We find ourselves in an era defined by the rapid generation, evaluation, and constant updates of vast amounts of data. As a result, the need for enterprises to keep up with this pace has grown and we are rapidly moving towards a more data-driven society. Predictive modeling has emerged as a pivotal discipline in the era of data-driven decision-making. Its ability to forecast future outcomes based on historical data and patterns has revolutionized decision-making processes, enabling organizations to make informed and proactive choices.

Using open source time series forecasting ML models such as ARIMA and Prophet, we can provide more accurate predictions and insights in real-time, enabling organizations and teams to streamline processes and increase efficiency, improve and manage customer risk, and adapt to changing market conditions. In this session, we will discuss a use case where we predict high growth potential customers for some of Red Hat’s leading hybrid cloud services and provide guidance for the customer success teams to better navigate the dynamic landscape of product sales. In addition, we will also cover:

1. Open Source tooling for building predictive ML models (Python, Jupyter, MLFLow)
2. Time series forecasting techniques
3. Tips for managing ML workflows and model interpretations

Attendees will leave this talk with a deeper understanding of predictive ML models and how open source can empower us to be more data driven.


Surya is a Data Scientist, currently working on the Emerging Technologies team at Red Hat. He is experienced in the field of Machine Learning and Artificial Intelligence. He spent the past year developing models for gaining customer insights, navigating open source tools for data scientists, and doing NLP using transformers models.

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