Are you a federal security practitioner? Are you looking for ways to improve threat detection, contextualization and threat hunting? Are you a data engineer or data scientist wanting to work more closely with security ops teams? Does your agency struggle with increasing SIEM costs, especially in the face of recent mandates like OMB M-21-31?

In this hands-on course, you will learn how easy it is to ingest data into Delta Lake, analyze DNS data, enrich it using threat intel, create detections using ML models, detect cybercriminals and integrate with your SIEM tool (Splunk). You will use Databricks notebooks to collaborate and ML Flow to deploy your models for automated, future use. Not familiar with data science or Databricks? Not to worry. The course’s live-support staff has decades of security operations and data science experience.

 
This workshop will give you the opportunity to:

• Learn how to ingest DNS data into Delta Lake for use in threat detection 
• Train a model against data and learn best practices for working with ML frameworks (TensorFlow, XGBoost, scikit-learn, etc.)
• Learn about MLflow to track experiments, share projects and deploy models in the cloud 
• Understand how to augment your SIEM with Databricks and leverage our Splunk connector for cost-effective compliance with Federal mandates like OMB M-21-31
• Network and learn from your ML and Apache SparkTM peers

Watch this on-demand workshop to build a cybersecurity lakehouse and train machine learning models in Databricks to tackle OMB M-21-31 compliance.