Forecasting lays the foundation for many critical business assumptions such as turnover, profit margins, cash flow, capital expenditure, and capacity planning.
However, tedious and redundant tasks in exploratory data analysis, model development, and model deployment can stretch the time to value of your machine learning projects.
Real-world complexity, scale, and siloed processes between teams can also add challenges to your forecasting.
Learn how data scientists can accelerate time to value by simplifying steps needed to create time series forecasting models. Reduce weeks and months of work into hours and days by automating feature engineering, feature reduction, and model development. Save time and resources while complying with industry regulations by generating model compliance documentation automatically.
Machine learning deployment processes today are manual, complex, and span multiple teams.
This session will also discuss how teams can accelerate the delivery of AI to production by centralizing collaboration across business, data science, and IT. Reduce your time maintaining production models by centralizing deployment, monitoring, management, and governance.