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Financial Services organisations today want to accelerate innovation by incorporating AI into their businesses. Machine Learning has become essential for use cases such as fraud detection, financial modeling, robo-advisors, client analytics, alternative data, etc. However, most companies struggle with preparing large datasets for analytics, managing the proliferation of ML frameworks, and moving models in development to production.

In this seminar, we’ll cover best practices for enterprises to use powerful open source technologies to simplify and scale your ML efforts. We’ll discuss how to leverage Apache Spark™, the de-facto data processing and analytics engine in enterprises today, for data preparation as it unifies data at massive scale across various sources. You’ll learn how to use ML frameworks (i.e. Tensorflow, XGBoost, Scikit-Learn, etc.) to train models based on different requirements. And finally, you can learn how to use MLflow to track experiment runs between multiple users within a reproducible environment, and manage the deployment of models to production.

Join this half-day workshop to learn how unified analytics can bring data science and engineering together to accelerate your ML efforts. This free seminar will give you the opportunity to:

• Learn how to build highly scalable and reliable pipelines for financial analytics
• Deeper insight into Apache Spark and Databricks, including the latest updates with Databricks Delta
• Train a model against data and learn best practices for working with ML frameworks (i.e. - XGBoost, Scikit-Learn, etc.)
• Learn about MLflow to track experiments, share projects and deploy models in the cloud and on-prem
• Network and learn from your ML and Apache Spark peers

AGENDA AT A GLANCE