Scaling Enterprise Data Science Through AI-Based Automation

The rapid deployment of AI and ML models is pivotal for businesses looking to optimize processes and capitalize on market opportunities. However, many organizations need better tools for the data engineering and AI/ML pipeline deployment phases, which can extend the journey from raw data to production-ready models to 2-4 months. Understanding and overcoming these obstacles is crucial for achieving faster and more effective AI/ML outcomes. This webinar will explore the intricacies of data preparation, feature engineering, and ML pipeline deployment, providing attendees with valuable insights into streamlining these processes and improving collaboration across teams. We are gathering a distinguished panel of experts in ML engineering, data science, and domain-specific applications to share their knowledge, experiences, and insights. Our panelists will engage with the audience in a dynamic and interactive format, addressing key challenges and discussing practical solutions for accelerating AI/ML deployment. What You Will Learn: Identify the primary challenges in data engineering and pipeline deployment and strategies to address them. Effective methods for improving communication and collaboration, and reducing dependence between domain experts, data scientists, and ML engineers. Innovative tools, agents, and frameworks to automate and streamline data prep, feature engineering, and ML model deployment processes. Best practices for managing and monitoring ML feature sets to ensure a smooth transition to production.