Join Databricks, Datalytyx and Talend for this free half day seminar on ‘Modernising your data warehouse in 24 hours’ to see how easy it is to get your data-focussed initiatives over the technical barriers and into reality. In this seminar you'll see how to: quickly spin up cloud-based technology, enable business users to integrate, cleanse and master data, automatically scale storage and compute resources, deliver it all with no set up fee – just a monthly subscription cost with no lock-in
We are delighted to announce our graph focused event this summer in Munich. This event will be hosted by b.telligent together with two great speakers from the graphs community:
Dr. Andreas Hopfgartner & Dr. Sebastian Petry (b.telligent)
Martin Junghanns (Neo4j)
Talk 1: Graphs are everywhere!? - Not yet, but maybe soon.
Talk 2: Extending Apache Spark Graph for the Enterprise with Morpheus and Neo4j
During this webinar, Brian McConnell, Solution Architect at Databricks will bring technical solutions - including Databricks Runtime, Delta Lake and Mlflow, to the main challenges that financial services and insurance companies are facing nowadays.
Topics that will be covered:
- the regulatory triangle: Regulators, Legislators and Industry
- regulation and the Data Driven Organisation
- dealing with change, and lots of it
- reproducibility.
Delta Lake is an open source storage layer that brings reliability to data lakes. It has numerous reliability features including ACID transactions, scalable metadata handling, and unified streaming and batch data processing. Delta Lake runs on top of your existing data lake, such as on Azure Data Lake Storage, AWS S3, Hadoop HDFS, or on-premise, and is fully compatible with Apache Spark APIs.
Join this hands-on lab to learn how Delta Lake can help you build robust production data pipelines at scale.
Join our technical breakfast seminar to network with your peers and learn how to easily overcome the challenges of processing and analyzing large geospatial datasets in the cloud with a unified approach to data and analytics.
In this workshop, 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 also 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 on Amazon SageMaker.
Delta Lake is an open source storage layer that brings reliability to data lakes. It has numerous reliability features including ACID transactions, scalable metadata handling, and unified streaming and batch data processing. Delta Lake runs on top of your existing data lake, such as on Azure Data Lake Storage, AWS S3, Hadoop HDFS, or on-premise, and is fully compatible with Apache Spark APIs.
Join this hands-on lab to learn how Delta Lake can help you build robust production data pipelines at scale.
In this workshop, 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.
During this webinar we will explore how to detect model drift using MLflow and Apache Spark Streaming on Databricks, using IoT sensor data from glassware manufacturing to select products for manual quality inspection. We’ll highlight some subtle problems in online model evaluation, like connecting future ground-truth labels to new data.
