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 […]

Databricks Runtime 5.2 ML Features Multi-GPU Workflow, Pregel API, and Performant GraphFrames

Posted Leave a commentPosted in Apache Spark, Databricks Runtime 5.2 ML, Deep Learning, Engineering Blog, GraphFrames, HorovodRunner, Machine Learning, Platform, PyTorch, TensorFlow

We are excited to announce the release of Databricks Runtime 5.2 for Machine Learning. This release includes several new features and performance improvements to help developers easily use machine learning on the Databricks Unified Analytics Platform. Continuing our efforts to make developers’ lives easy to build deep learning applications, this release includes the following features […]

Introducing Databricks Runtime 5.1 for Machine Learning

Posted Leave a commentPosted in Announcements, Apache Spark, Company Blog, Databricks Runtime 5.1 ML, Deep Learning, Engineering Blog, Machine Learning, PyTorch, TensorFlow

Last week, we released Databricks Runtime 5.1 Beta for Machine Learning. As part of our commitment to provide developers the latest deep learning frameworks, this release includes the best of these libraries. In particular, our PyTorch addition makes it simple for a developer to simply import the appropriate Python torch modules and start coding, without […]

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 […]

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 […]

Identify Suspicious Behavior in Video with Databricks Runtime for Machine Learning

Posted Leave a commentPosted in Apache Spark, Company Blog, Deep Learning, Deep Learning Pipelines, Education, Engineering Blog, Machine Learning, OpenCV, Platform, Product, TensorFlow, Video Analytics

Try this notebook series in Databricks With the exponential growth of cameras and visual recordings, it is becoming increasingly important to operationalize and automate the process of video identification and categorization. Applications ranging from identifying the correct cat video to visually categorizing objects are becoming more prevalent.  With millions of users around the world generating […]