In this 3-hour introductory session, we’ll cover how data teams can use powerful open source technologies to simplify and scale your data and 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 subject matter experts in this virtual workshop to learn how unified data analytics can bring data science, business analytics and engineering together to increase the precision in churn and customer lifetime value models across industries like retail, media, telco, insurance, retail financial services, and other.
The need for visibility across the organization and flexibility to analyze deeper in your data is greater than ever during these unprecedented times. Join Thorogood and Databricks to understand how, together, we’ve helped the finance arm of leading organizations understand their data better, as we showcase solutions that use financial data to drive intelligent business decisions.
Watch Azure Databricks live in action! This is a great session for those getting started with Azure Databricks and an opportunity for live Q&A with a Databricks expert. Watch how Azure Databricks tackles the full range of data science activities, including data management, transformation, and machine learning (ML-Ops). You will get an introduction to the following product components that make it easy to solve your toughest data problems.
A new breed of Financial Services companies are now incorporating new, external, real-time sources of data to make financial decisions: Smart Asset Managers are “Now-casting” what’s happening in their portfolio companies to generate superior alpha and banks are analyzing broader sets of data for superior underwriting performance and upsell/cross-sell. Insurance companies now predict claim expenses instead of reacting to them. These days, there are outstanding vendors offering access to valuable alternative data and 3rd party datasets, including geolocation, social media, and transaction datasets. With the increased availability of high-quality alternative data, the opportunities for competitive advantages are in the efficient storing of data, cleaning it, and combining insights across disparate datasets. In this webinar, you’ll learn how to start with a sample ticker (e.g. plant-based meat stock), optimize storage of alternative data sources related to this ticker, and analyze ticker-related foot traffic data to guide investment decisions and increase to alpha.
Watch Azure Databricks live in action! This is a great session for those getting started with Azure Databricks and an opportunity for live Q&A with a Databricks expert. Watch how Azure Databricks tackles the full range of data engineering and analytics activities, including data management, transformation, and streaming analytics. You will get an introduction to the following product components that make it easy to solve your toughest data problems.
Apache Spark is the dominant processing framework for big data. Delta Lake adds reliability to Spark so your analytics and machine learning initiatives have ready access to quality, reliable data. In this webinar, we will cover the use of Delta Lake to enhance data reliability for Spark environments. Join us and discover how you can deliver reliable, high-quality data for analytics and machine learning with Delta Lake.
Hear from open source and industry thought leaders about the latest trends in big data, analytics and AI. Get an in-depth look at open-source technologies like Apache Spark™, Delta Lake, MLflow, Koalas, TensorFlow and PyTorch.
In this webinar, we'll show you how unified analytics can bring data science and engineering together to accelerate your ML efforts. You’ll learn enterprise best practices for using powerful, open source technologies to simplify and scale ML efforts. We'll discuss how to leverage Apache Spark for data preparation, unifying data at massive scale across various sources.
Learn how leading enterprises deploy scalable data science solutions. Connect with leaders in this new field of data science architecture and learn best practices to support data team productivity and enabling AI at scale.