Machine learning is complex — especially when it comes to actually delivering models at scale. Most data scientists are focused mainly on designing models to solve business problems, rather than ensuring those models will run stably and scale on production infrastructure. On the other hand, traditional software engineers might not know the particular mix of tools and languages used by most machine-learning applications.
Databricks Machine Learning from phData gives you access to the expertise and tested frameworks you need to get models into production. As a leading specialist provider of data engineering and machine learning services, we help ensure your projects succeed across every step of your project’s lifecycle — from ideation to post-implementation support.
We bring the right people with the right skills at each stage of the 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 Databricks-based machine learning projects deliver value — whether it’s training and validation with Databricks notebooks, experiment tracking and model management with MLflow, or integrating platform-specific best practices for deploying models on the Databricks platform and beyond.
With experience in implementing successful projects in highly regulated industries, we understand how to help your machine learning initiatives deliver value without sacrificing security. We assist with model authorization, model cataloging, data-set and feature cataloging, model interpretability, audit, and monitoring to ensure your ML projects adhere to appropriate legal, ethical, and regulatory constraints.
Also, our emphasis on automation, repeatability, data transparency, and process standardization minimizes human error — and therefore minimizes risk.