Training your Neural Network: On-Demand Webinar and FAQ Now Available!
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 activation and loss functions, batch...
MLflow v0.7.0 Features New R API by 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!
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 their expressive power: the potential...
How to Use MLflow To Reproduce Results and Retrain Saved Keras ML Models
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...
Simplify Market Basket Analysis using FP-growth on Databricks
When providing recommendations to shoppers on what to purchase, you are often looking for items that are frequently purchased together (e.g. peanut butter and jelly). A key technique to uncover associations between different items is known as market basket analysis. In your recommendation engine toolbox, the association rules generated by market basket analysis (e.g. if...
Identify Suspicious Behavior in Video with Databricks Runtime for Machine Learning
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 and consuming billions of minutes of...
Building a Real-Time Attribution Pipeline with Databricks Delta
In digital advertising, one of the most important things to be able to deliver to clients is information about how their advertising spend drove results. The more quickly we can provide this, the better. To tie conversions or engagements to the impressions served in an advertising campaign, companies must perform attribution. Attribution can be a...
Loan Risk Analysis with XGBoost and Databricks Runtime for Machine Learning
For companies that make money off of interest on loans held by their customer, it’s always about increasing the bottom line. Being able to assess the risk of loan applications can save a lender the cost of holding too many risky assets. It is the data scientist’s job to run analysis on your customer data...
MLflow 0.4.2 Released
Today, we’re excited to announce MLflow v0.4.0, MLflow v0.4.1, and v0.4.2 which we released within the last week with some of the recently requested features. MLflow 0.4.2 is already available on PyPI and docs are updated. If you do pip install mlflow as described in the MLflow quickstart guide, you will get the recent release. In this...
Simplify Advertising Analytics Click Prediction with Databricks Unified Analytics Platform
Advertising teams want to analyze their immense stores and varieties of data requiring a scalable, extensible, and elastic platform. Advanced analytics, including but not limited to classification, clustering, recognition, prediction, and recommendations allow these organizations to gain deeper insights from their data and drive business outcomes. As data of various types grow in volume, Apache...
