Machine learning is taking off. Enterprises use it to automate decisions, hyper-personalize customer experiences, gather new insights, and streamline operational processes. However, enterprises often struggle to get machine learning models built by data science teams into production applications. The problem: existing technical architectures are poorly equipped to handle model management and the multiple data pipelines that are required to infuse machine learning into existing and new applications.
In this session, our guest speaker, Forrester Research Vice President & Principal Analyst Mike Gualtieri, and Attunity's Senior Director of Product Marketing Kevin Petrie will demystify the machine learning lifecycle and enumerate the architectural and data integration requirements that enterprise architects must understand to fill the gaps needed to support machine learning model training and deployment.
Attend to learn how to:
- Better understand the machine learning (ML) lifecycle and architectural requirements
- Support ML model training and deployment
- Leverage real-time CDC and data integration to accelerate ML data pipelines
- Streamline operational processes and enable higher productivity
- Use ML to automate decisions and discover new business insights
