Learn about Apache Spark, Delta Lake, MLflow, TensorFlow, deep learning, applying software engineering principles to data engineering and machine learning LEARN MORE >
In this Oil and Gas focused 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 Delta Lake, the de facto data lake framework in enterprises today, to unify data at massive scale across various sources from well logs to drilling reports, geospatial data and more. 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.
As data volume, variety and velocity accelerate, organisations need to leverage modern data engineering. Join Databricks and Microsoft to learn how to create a modern data architecture with Azure Data Factory, Azure Databricks, Azure Synapse Analytics and Power BI.
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. It also offers DML commands to update, delete, and merge data for your data lifecycle, such as for GDPR/CCPA. 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 virtual hands-on lab to learn how Delta Lake can help you build robust production data pipelines at scale.
In this virtual workshop, we’ll cover best practices for enterprises to 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. 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.
In this webinar, we’ll cover why companies are switching to modern cloud based platforms like Databricks on AWS, and how they use it to drive innovation, productivity, business outcomes and reduce TCO of their data lake. We’ll also share a best practice framework for how to successfully migrate data and workloads to the cloud safely and securely.
Built on Spark, billions of images are collected for computer vision applied to two projects: City Stream & Virtual City Camera.
Rotem Tamir, Data Platform Tech Lead at Nexar will walk through how Nexar is providing value to the platform user by leveraging Apache Spark and Databricks on deep learning models.
Join this virtual hands-on lab to learn how Delta Lake can help you build robust production data pipelines at scale. This virtual event will give you the opportunity to gain an understanding of the Delta Lake open source project, learn how to build highly scalable and reliable data pipelines using Delta Lake, see Delta Lake in action with a demo and hands-on code walkthrough and ssk Databricks experts your most challenging data questions.
In this virtual workshop, we’ll cover best practices for enterprises to 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.
In this virtual workshop, we’ll share how a unified approach to data analytics can accelerate analytics and ML projects to deliver on a wide range of use cases in the Healthcare and Life Sciences industry. Learn how to build a scalable clinical data lake with powerful open-source technologies like Delta Lake and Apache SparkTM. We'll walk-through how to ingest and prepare streaming EHR data for downstream analytics.
We'll use MLflow to collaboratively build and track ML experiments in a reproducible and HIPAA-compliant environment.
In this virtual workshop, we’ll cover best practices for organizations to use powerful open source technologies to build and extend your AWS investments to make your data lake analytics-ready. You’ll learn about the advantages of cloud-based data lakes in terms of security and cost. And finally, you’ll learn how data professionals are having a huge impact - lowering costs, changing time to market, and even revolutionizing industries.
