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 […]

A Guide to AI, Machine Learning, and Deep Learning Talks at Spark + AI Summit Europe

Posted Leave a commentPosted in Apache Spark, Company Blog, Deep Learning Pipelines, Events, Keras, PyTorch, Spark + AI Summit, TensorFlow

Within a couple of years of its release as an open-source machine learning and deep learning framework, TensorFlow has seen an amazing rate of adoption. Consider the number of stars on its github page: over 105K; look at the number of contributors: 1500+; and observe its growing penetration and pervasiveness in verticals: from medical imaging […]

How to Use MLflow, TensorFlow, and Keras with PyCharm

Posted Leave a commentPosted in Apache Spark, Data Science, Deep Learning, Engineering Blog, Keras, Machine Learning, MLflow, Model Management, python, TensorFlow

At Spark + AI Summit in June, we announced MLflow, an open-source platform for the complete machine learning cycle. The platform’s philosophy is simple: work with any popular machine learning library; allow machine learning developers experiment with their models, preserve the training environment, parameters, and dependencies, and reproduce their results; and finally deploy, monitor and […]