Every enterprise today wants to accelerate innovation by building AI into their business. However, most companies struggle with preparing large datasets for analytics, managing the proliferation of ML frameworks, and moving models in development to production. 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.
Every enterprise today wants to accelerate innovation by building AI into their business. However, most companies struggle with preparing large datasets for analytics, managing the proliferation of ML frameworks, and moving models in development to production.
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.
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.
Join the Databricks teams for a half-day training dedicated to Apache Spark and Databricks Unified Analytics Platform, a cloud-based platform hosted on AWS and Azure.
Save your seat now!
The field of genomics has matured to a stage where organizations are sequencing DNA at population scale. However, taking raw DNAseq data and transforming it into a format suitable for analysis has become the new bottleneck to genomic discovery. Typically, teams are gluing together a series of bioinformatics tools with custom scripts and processing data on single node machines, one sample at a time. Bioinformatics scientists are spending more time building and maintaining pipelines than modeling data. To ease the burden of analyzing population scale genomic data, a number of open-source bioinformatics tools have moved to use Apache Spark™, such as the GATK4, Hail, and ADAM, but mastering these tools is no easy task.
Join this half day seminar for Financial Services companies, hosted by Ion Stoica, Co-Founder of Databricks. We’ll address the basics of Apache Spark™ as the top analytics engine, best practices for applying Machine Learning specifically for Financial Services enterprises, and how to track the lifecycle of these experiments into production with MLFlow. This seminar is focused on decision makers, practitioners, and business leaders, that are responsible for data and its value to an organization.
The Big Data & Analytics Summit, launched in 2012, provides an ideal venue for leaders from around the region to share ideas, insights, and best practices that will help organization’s approach to big data & business analytics.
So if you want to create a more robust big data practice or implement new equipment, platforms, and software then you need to be in Detroit for the region’s premier Big Data & Analytics Summit.
Join the Databricks team in London for a breakfast session dedicated to Delta. Our team of experts will share with you th basics and best practices on this unified data management system. You will learn how to:
Create high performance data pipelines while providing reliability and data quality
Simplify data pipelines
Improve data scientist productivity with advanced features such as Time Travel.
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.
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.
