Discovering Business Insights with Open Source Machine Learning: Tools, Techniques and Tips

While Generative AI and Large Language Models are gaining traction, old-school machine learning is still a powerful tool for uncovering hidden patterns in corporate data. This data can be a goldmine of information, revealing insights into customer behavior, market trends, and operational inefficiencies. By leveraging key machine learning techniques, businesses can gain a competitive edge by identifying new opportunities, improving decision-making, and streamlining operations. 

With predictive modeling, we can now quickly consume and analyze large datasets to uncover hidden patterns and trends. 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.