Presented by

  • Hema Veeradhi

    Hema Veeradhi

    Hema Veeradhi is a Senior Data Scientist working in the Emerging Technologies team part of the office of the CTO at Red Hat. Her work primarily focuses on implementing innovative open AI and machine learning solutions to help solve business and engineering problems.

  • Surya Prakash Pathak

    Surya Prakash Pathak

    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.


We are living in a digital era where vast amounts of data is constantly being generated, evaluated, and updated. 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. With the help of AI/ML technology, we have the power to make knowledgeable data driven decisions and effectively identify new trends and patterns, leading to more creative solutions and innovative approaches to problem-solving. In light of the recent advancements in AI, particularly in predictive modeling, we now have a powerful tool at our disposal to quickly consume and analyze vast amounts of data. By using open source time series forecasting ML models like 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 talk we will discuss: 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.