Machine learning is hard — delivering models at-scale is even harder. Data scientists often lack the engineering expertise to ensure the models they design will be performant and scalable for a given infrastructure use case; meanwhile, traditional software engineers may not be familiar with the unique combination of tools and languages that ML applications typically rely on.
Cloudera Machine Learning from phData gets your models into production. As the leading specialist provider of data engineering and ML services for Cloudera, our data scientists know engineering, and our engineers know data. From ideation to post-implementation support, we support you across the entire lifecycle of a machine learning project.
We bring the right people with the right skills at each stage of the full machine learning lifecycle
— helping you implement repeatable processes and frameworks at every step to iterate and deliver models faster.
From ideation to model training to deployment to post-implementation support, we help ensure your Cloudera-based ML projects deliver value — whether its integrating with Cloudera Data Science Workbench to help with testing, experiment tracking and model selection, or integrating platform-specific best practices for deploying models on the Cloudera platform.