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Guest authors: Jerry Xu, Co-founder and CEO Datatron; Lekhni Randive, Product Manager, Datatron Qubole author: Jorge Villamariona, Sr. Product Marketing Manager, Qubole In today’s world,… The post...
Data scientists use Notebooks for data exploration, interactive data analytics, machine learning, and collaboration. Once set up, a Notebook provides a convenient way to save,… The post Analytics...
Real-world data science practitioners offer perspectives and advice on six common Machine Learning problems
TDWI drills into the data, tools, and platform requirements for machine learning
Enabling Machine Learning on the Data Warehouse with Apache Spark on Qubole
How to acquire, transform and analyze semi-structured data and apply predictive analytics to predict future performance
Strategies to address common challenges data science teams face as they scale up ML operations
Build and use a time-series analysis model to forecast future sales from historical sales data
The data on big data -- what engines are used most, for what, and which are the rising stars.
How to make all of your data available to users for a multitude of use cases, ranging from analytics to machine learning and artificial intelligence.
AgilOne runs a variety of workloads for querying data, running ML models, orchestrating ML workflows, and more on Qubole
TrafficGuard relies on big data processing to detect and prevent ad fraud, which requires a robust infrastructure.
Return Path drove down compute costs and was able to deliver self-service access to data with Qubole
Migrating to Qubole saved Spotad more than 50 percent in its operating costs almost instantly.
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