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Developing ML-enabled data pipelines on Databricks using IDE & CI/CD

Webinar

Webinar

The CI/CD templates are used by Runtastic for automating deployment processes of their Databricks pipelines. During this webinar Emanuele Viglianisi, Data Engineer at Runtastic will show how Runtasic is using CI/CD templates during their day to day development to run, test and deploy their pipelines directly from PyCharm IDE to Databricks. Emanuele will present the challenges Runtastic has faced and how they successfully solved them by integrating the CI/CD template in their workflow.

Industrialized ML for Governed, Responsible and Explainable AI

Webinar

Streaming from: San Francisco, CA

By combining Accenture’s Industrialized ML best practices with Databricks’ Unified Data Analytics Platform, including MLflow, organizations can efficiently develop an industrialized, end-to-end ML model. Learn how Navy Federal Credit Union leveraged industrialized ML, scaling its capabilities to deliver next-generation member service.

Using SQL to Query your Data Lake with Delta Lake

Regional Event

Streaming from Sydney, Australia

Join Databricks and Microsoft as we share how you can easily query your data lake using SQL and Delta Lake on Azure. We’ll show how Delta Lake enables you to run SQL queries without moving or copying your data. We will also explain some of the added benefits that Azure Databricks provides when working with Delta Lake.

Loan Risk Analysis using Machine Learning in Azure

Partner Event

Virtual Event

Join us on October 21 when we discuss the trends in the financial sector and showcase a machine learning model of loan risk. We will use loan data from the peer-to-peer lending company LendingClub and model this data using Azure Databricks to understand how an institution can maximize profits in a world of uncertainty.

MLOps in Practice: Hyperparameter Optimization with MLflow

Partner Event

Virtual Event

MLflow serves a handful of important purposes in machine-learning projects – environment management, streamlining of deployments, artifact persistence – but in the context of hyperparameter optimization, it is particularly useful for experiment tracking. A good MLOps pipeline enables reproducible research by keeping track of experiments automatically so that data scientists can focus on innovation.

Databricks Learning Series: SQL Workshop

Regional Event

Streaming from Sydney, Australia

This 3 hour tutorial is suited for SQL analysts and developers who want to get hands-on experience and a deeper knowledge of the benefits of using SQL on the Databricks Unified Data Analytics Platform. Domain knowledge and familiarity of SQL is required for this session.

Advanced Geospatial Analytics at Scale with Databricks

Regional Event

Streaming live from Australia

Join this session for a one-hour deep-dive on how companies can apply advanced analytics to geospatial datasets and deliver on a broad range of use cases like mining exploration, oil discovery, asset inspection, flood surveys, environment protection, facility management, transportation planning, fraud detection, and more.

How Apache Spark 3.0 and Delta Lake Enhance Data Lake Reliability

Webinar

Virtual Event

Apache Spark™️ has become the de-facto open-source standard for big data processing due to its ease of use and performance. And the open source Delta Lake project enhances Spark’s lead with new capabilities like ACID transactions, Schema Enforcement and Time Travel. In this webinar, learn the advantages of combining Apache Spark 3.0 and Delta Lake. You’ll also get a walk-through of Apache Spark 3.0 as part of our Databricks Runtime 7.0 Beta.

【Databricks主催、AWS協力ウェビナー】日本経済新聞社様 ご登壇!メディア業界向け 機械学習を活用したレコメンデーションの最前線をご紹介!

Webinar

Virtual Event

日本経済新聞社様より、日経電子版における、新たなレコメンデーションプロジェクトを紹介いただきながら、機械学習の最もポピュラーなユースケースの1つであるリコメンデーションを、どのようにAWSとDatabicksで支えるのかをお伝え致します。

AWS Master Path – Reduce time to market by unifying Data Pipelines and Machine Learning

Virtual Event

Virtual Workshop

In this virtual workshop, we will introduce you to the Databricks Unified Analytics Platform - a fully managed service on AWS that offers a collaborative workspace for Data Engineers, Data Scientists and Business Analysts for faster innovation and less operational overhead. 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 SparkTM, 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 Data and 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.