Hyperparameter Tuning with MLflow, Apache Spark MLlib and Hyperopt

Posted Leave a commentPosted in Apache Spark, AutoML, Data Science, Databricks Runtime 5.4 ML, Deep Learning, Ecosystem, Engineering Blog, Hyperopt, Hyperparameter Tuning, Machine Learning, MLflow, MLlib

Hyperparameter tuning is a common technique to optimize machine learning models based on hyperparameters, or configurations that are not learned during model training.  Tuning these configurations can dramatically improve model performance. However, hyperparameter tuning can be computationally expensive, slow, and unintuitive even for experts. Databricks Runtime 5.4 and 5.4 ML (Azure | AWS) introduce new […]

Enhanced Hyperparameter Tuning and Optimized AWS Storage with Databricks Runtime 5.4 ML

Posted Leave a commentPosted in Announcements, AutoML, Company Blog, Data Science, Databricks Runtime 5.4 ML, Deep Learning, Ecosystem, Engineering Blog, Hyperopt, Hyperparameter Tuning, Machine Learning, MLflow, MLlib, Platform, Product

We are excited to announce the release of Databricks Runtime 5.4 ML (Azure | AWS). This release includes two Public Preview features to improve data science productivity, optimized storage in AWS for developing distributed applications, and a number of Python library upgrades. To get started, you simply select the Databricks Runtime 5.4 ML from the […]

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 […]