Better Machine Learning through Active Learning
Try this notebook to reproduce the steps outlined below Machine learning models can seem like magical savants. They can distinguish hot dogs from not-hot-dogs, but that's long since an easy trick. My aunt's parrot can do that too. But machine-learned models power voice-activated assistants that effortlessly understand noisy human speech, and cars that drive themselves...
Processing Geospatial Data at Scale With Databricks
The evolution and convergence of technology has fueled a vibrant marketplace for timely and accurate geospatial data. Every day billions of handheld and IoT devices along with thousands of airborne and satellite remote sensing platforms generate hundreds of exabytes of location-aware data. This boom of geospatial big data combined with advancements in machine learning is...
Streamlining Variant Normalization on Large Genomic Datasets with Glow
Cross posted from the Glow blog. Many research and drug development projects in the genomics world involve large genomic variant data sets, the volume of which has been growing exponentially over the past decade. However, the tools to extract, transform, load (ETL) and analyze these data sets have not kept pace with this growth. Single-node...
Migration from Hadoop to modern cloud platforms: The case for Hadoop alternatives
Companies rely on their big data and analytics platforms to support innovation and digital transformation strategies. However, many Hadoop users struggle with complexity, unscalable infrastructure, excessive maintenance overhead and overall, unrealized value. We help customers navigate their Hadoop migrations to modern cloud platforms such as Databricks and our partner products and solutions, and in this...
Using AutoML Toolkit’s FamilyRunner Pipeline APIs to Simplify and Automate Loan Default Predictions
Introduction In the post Using AutoML Toolkit to Automate Loan Default Predictions, we had shown how the Databricks Labs’ AutoML Toolkit simplified Machine Learning model feature engineering and model building optimization (MBO). It also had improved the area-under-the-curve (AUC) from 0.6732 (handmade XGBoost model) to 0.723 (AutoML XGBoost model). With AutoML Toolkit’s Release 0.6.1, we...
Scalable near real-time S3 access logging analytics with Apache Spark™ and Delta Lake
The original blog is from Viacheslav Inozemtsev, Senior Data Engineer at Zalando, reproduced with permission. Introduction Many organizations use AWS S3 as their main storage infrastructure for their data. Moreover, by using Apache Spark™ on Databricks they often perform transformations of that data and save the refined results back to S3 for further analysis. When...
Scaling Hyperopt to Tune Machine Learning Models in Python
Try the Hyperopt notebook to reproduce the steps outlined below and watch our on-demand webinar to learn more. Hyperopt is one of the most popular open-source libraries for tuning Machine Learning models in Python. We’re excited to announce that Hyperopt 0.2.1 supports distributed tuning via Apache Spark. The new SparkTrials class allows you to scale out...
Scaling Financial Time Series Analysis Beyond PCs and Pandas: On-Demand Webinar, Slides and FAQ Now Available!
On Oct 9th, 2019, we hosted a live webinar —Scaling Financial Time Series Analysis Beyond PCs and Pandas — with Junta Nakai, Industry Leader Financial Services at Databricks, and Ricardo Portilla, Solution Architect at Databricks. This was a live webinar showcasing the content in this blog- Democratizing Financial Time Series Analysis with Databricks. Please find...
Democratizing Financial Time Series Analysis with Databricks
Introduction The role of data scientists, data engineers, and analysts at financial institutions includes (but is not limited to) protecting hundreds of billions of dollars worth of assets and protecting investors from trillion-dollar impacts, say from a flash crash. One of the biggest technical challenges underlying these problems is scaling time series manipulation. Tick data,...
Simple, Reliable Upserts and Deletes on Delta Lake Tables using Python APIs
We are excited to announce the release of Delta Lake 0.4.0 which introduces Python APIs for manipulating and managing data in Delta tables. The key features in this release are: Python APIs for DML and utility operations (#89) - You can now use Python APIs to update/delete/merge data in Delta Lake tables and to run...