Join this half-day workshop to learn how unified analytics can bring data science and engineering together to accelerate your ML efforts. 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.
Join this half-day workshop to learn how unified analytics can bring data science and engineering together to accelerate your ML efforts. 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.
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.
Learn how LoyaltyOne—a loyalty marketing services provider—used open-source Delta Lake on Databricks and Amazon Web Services (AWS) to create automated data pipelines that feed thousands of ML and analytics models, accelerating and enhancing customer churn and recommendation engines. LoyaltyOne is also scaling their analytics by using AutoML on Databricks, accommodating developers with a range of experience.
Building and deploying a machine learning model can be difficult to do once. Enabling other engineers (or yourself, one month later) to reproduce your pipeline, to compare the results of different versions, to track what’s running where, and to redeploy and rollback updated models is much harder.
In this talk, Matei Zaharia, the Co-founder and Chief Technologist from Databricks, will introduce MLflow, a new open-source project launched by Databricks that simplifies the machine learning lifecycle.
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.
Join this half-day workshop to learn how unified analytics can bring data science and engineering together to accelerate your ML efforts. 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.
Since being open sourced in 2015, TensorFlow has had a significant impact on many industries. With TensorFlow 2.0's eager execution, intuitive high-level APIs, and flexible model building on any platform, it's cementing its place as the production-ready, end-to-end platform driving the machine learning revolution. At TensorFlow World you'll see TensorFlow 2.0 in action, discover new ways to use it, and learn how to successfully implement it in your enterprise.
Join us at imarc in Melboorne at the Microcost stand and learn how unified analytics can bring data science and engineering together to accelerate your ML efforts; including best practices for enterprises to use powerful open source technologies to simplify and scale your ML effort, as well as how to leverage Apache Spark™, the de-facto data processing and analytics engine in enterprises.
Join this half-day workshop to learn how unified analytics can bring data science and engineering together to accelerate your ML efforts. 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.
