Predict, Optimize, Deploy: What's New in ML Studio

Building industrial AI models doesn't require a PhD in machine learning—but it does require the right tools. ML Studio democratizes model development for process engineers and reliability experts, eliminating the months typically spent waiting for data science backlogs. With one-click model builds and full lifecycle management, teams can now experiment, compare, and deploy predictive models at enterprise scale.

This October, we're expanding what's possible. Since the July release of AutoML Regression, engineers have automated the tedious work of feature engineering and hyperparameter tuning. Now, Golden Batch Optimization and Remaining Useful Life join the problem-type library—addressing critical manufacturing challenges: identifying optimal process conditions and predicting asset lifespan to prevent unplanned downtime.

In this 45-minute session, we build a complete model live in the UI—walking through the full workflow from experiment design through model comparison and deployment. You'll see how the platform handles dataset management, iterative testing, and production integration without requiring custom code. We use a real manufacturing scenario to show how these new capabilities translate to faster time-to-insight and lower barrier to adoption across your engineering teams.

What you'll learn:

• How one-click model builds and full lifecycle management let process engineers and reliability experts build models without waiting on data science backlogs.
• How AutoML Regression automates feature engineering and hyperparameter tuning.
• How the new Golden Batch Optimization and Remaining Useful Life problem types identify optimal process conditions and predict asset lifespan.
• The full workflow, live in the UI: experiment design, dataset management, iterative testing, model comparison and deployment, without custom code.

Whether you're scaling predictive maintenance, optimizing batch processes, or trying to democratize ML across your organization, this session connects the expanding capability set to practical engineering workflows.

Featured speakers: Amit Chandrakar (Principal Data Scientist, SymphonyAI) · Ravi Subramanyan (Sr. Director, Industrial Solutions, SymphonyAI)