Managed MLflow Now Available on Databricks Community Edition

Posted Leave a commentPosted in Announcements, Data Science and Machine Learning, Databricks Community Edition, Databricks Managed Manage Flow, Engineering Blog, Keras, Machine Learning, Managing Machine Learning Lifecycle., MLflow, TensorFlow

In February 2016, we introduced Databricks Community Edition, a free edition for big data developers to learn and get started quickly with Apache Spark. Since then our commitment to foster a community of developers remains steadfast: to date, we have over 150K registered Community Edition users; we have trained thousands of people at meetups and […]

A Guide to Training Sessions at Spark + AI Summit, Europe

Posted Leave a commentPosted in Apache Spark, Company Blog, Data and ML Industry Use Case, Data and ML Research, Data Engineering, Data Science, Data Science and Machine Learning, data-science, Delta Lake, Education, Events, Keras, MLflow, Productionizing Machine Learning, PyTorch, Spark + AI Summit, Spark SQL, Structured Streaming, TensorFlow, training

Education and the pursuit of knowledge are lifelong journeys: they never complete; there is always something new to learn; a new professional certification to add to your credit; a knowledge gap to fill. Training at Spark + AI Summit, Europe is not only about becoming an Apache Spark expert. Nor is it only about being […]

Simple Steps to Distributed Deep Learning: On-Demand Webinar and FAQ Now Available!

Posted Leave a commentPosted in Deep Learning, Horovod, HorovodRunner, Keras, Machine Learning, Platform, Product, PyTorch, TensorFlow

Try this notebook in Databricks On February 12th, we hosted a live webinar—Simple Steps to Distributed Deep Learning on Databricks—with Yifan Cao, Senior Product Manager, Machine Learning and Bago Amirbekian, Machine Learning Software engineer at Databricks. In this webinar, we covered some of the latest innovations brought into the Databricks Unified Analytics Platform for Machine […]

Accelerating Machine Learning on Databricks: On-Demand Webinar and FAQ Now Available!

Posted Leave a commentPosted in Company Blog, Data Science, Databricks Runtime, Deep Learning, Ecosystem, Engineering Blog, Horovod, HorovodRunner, Keras, Machine Learning, MLflow, Platform, Product, TensorFlow

Try this notebook in Databricks On January 15th, we hosted a live webinar—Accelerating Machine Learning on Databricks—with Adam Conway, VP of Product Management, Machine Learning, at Databricks and Hossein Falaki, Software Development Engineer and Data Scientist at Databricks. In this webinar, we covered some of the latest innovations brought into the Databricks Unified Analytics Platform […]

Introducing HorovodRunner for Distributed Deep Learning Training

Posted Leave a commentPosted in Apache Spark, Deep Learning, Distributed Learning, Engineering Blog, Keras, Project Hydrogen, TensorFlow

Today, we are excited to introduce HorovodRunner in our Databricks Runtime 5.0 ML! HorovodRunner provides a simple way to scale up your deep learning training workloads from a single machine to large clusters, reducing overall training time. Motivated by the needs of many of our users who want to train deep learning models on datasets […]

Applying your Convolutional Neural Network: On-Demand Webinar and FAQ Now Available!

Posted Leave a commentPosted in Deep Learning, Ecosystem, Engineering Blog, Keras, Machine Learning, Neural Networks, Platform, TensorFlow

Try this notebook in Databricks On October 25th, we hosted a live webinar—Applying your Convolutional Neural Network—with Denny Lee, Technical Product Marketing Manager at Databricks. This is the third webinar of a free deep learning fundamental series from Databricks. In this webinar, we dived deeper into Convolutional Neural Networks (CNNs), a particular type of neural […]

Training your Neural Network: On-Demand Webinar and FAQ Now Available!

Posted Leave a commentPosted in Deep Learning, Ecosystem, Engineering Blog, Keras, Machine Learning, Neural Networks, Platform, TensorFlow

Try this notebook in Databricks On October 9th, we hosted a live webinar—Training your Neural Network—on Data Science Central with Denny Lee, Technical Product Marketing Manager at Databricks. This is the second webinar of a free deep learning fundamental series from Databricks. In this webinar, we covered the principles for training your neural network including […]

MLflow v0.7.0 Features New R API by RStudio

Posted Leave a commentPosted in Announcements, Apache Spark, Company Blog, Deep Learning, Ecosystem, Education, Engineering Blog, GPyOpt, Hyperopt, Java, Keras, Machine Learning, MLflow, multistep workflow, Partners, python, R, RStudio

Today, we’re excited to announce MLflow v0.7.0, released with new features, including a new MLflow R client API contributed by RStudio. A testament to MLflow’s design goal of an open platform with adoption in the community, RStudio’s contribution extends the MLflow platform to a larger R community of data scientists who use RStudio and R […]

Introduction to Neural Networks: On-Demand Webinar and FAQ Now Available!

Posted Leave a commentPosted in Deep Learning, Ecosystem, Keras, Machine Learning, Neural Networks, Platform, TensorFlow

Try this notebook in Databricks On September 27th, we hosted a live webinar—Introduction to Neural Networks—with Denny Lee, Technical Product Marketing Manager at Databricks. This is the first webinar of a free deep learning fundamental series from Databricks. In this webinar, we covered the fundamentals of deep learning to better understand what gives neural networks […]

How to Use MLflow To Reproduce Results and Retrain Saved Keras ML Models

Posted Leave a commentPosted in Apache Spark, Engineering Blog, Keras, Machine Learning, MLflow, Model Management, Platform, TensorFlow, Unified Analytics Platform

In part 2 of our series on MLflow blogs, we demonstrated how to use MLflow to track experiment results for a Keras network model using binary classification. We classified reviews from an IMDB dataset as positive or negative. And we created one baseline model and two experiments. For each model, we tracked its respective training […]