Break down knowledge silos to identify and prioritize high-value work.
Work together in your favorite open source tools, like Jupyter and Spark.
Build applications and reports with the languages you already use.
Deploy and manage models, from prototype to production.
Bring data scientists and business users together with project-based workflows.
Reduce overhead with elastic compute resources and templated environments.
Use self-service features to build and deploy more models that drive real business impact.
Create a governed, central architecture with standard environments for data science.
Explore data, share analyses, and deploy predictive models from one platform.
Optimize consumer experiences by using data science to power campaigns.
Identifying and attracting the right data science talent is often more challenging than hiring managers anticipate. Check out our data scientist hiring webinar, presented in partnership with recruiting firm Burtch Works.Learn More
We asked data science professionals from Google, Oracle, Live Nation, ALG, Rubicon Project, and eHarmony to share what blockers worry them the most —and can impact the ability of data science teams to deliver value.LEARN MORE