Databricks Machine Learning
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.
Databricks machine learning offerings
Get the support and multidisciplinary expertise you need, across the full Databricks machine learning project lifecycle:
Find solutions for your organization’s most daunting data challenges, with our data science team at the ready to help you unearth the hidden potential of your data.
Machine Learning Engineering
Our multidisciplinary machine learning specialists bring the perfect combination of data science expertise and hands-on engineering knowhow to help you harden, scale, and integrate machine learning applications that deliver measurable results.
Deploy and manage your machine learning models, with 24x7 monitoring and alerting to rectify problems (such as model drift) before they take a toll on your bottom line.
Solving the toughest machine learning problems on Databricks
Proven paths to accelerate ML success
We get your machine learning models into production. Thanks to our proven deployment patterns, implementation frameworks, and automation, we do it faster and with less risk — all while freeing your team to focus on your core business needs.
Machine learning that delivers at-scale
One-off solutions are not the path to sustainable machine learning success. Our multidisciplinary machine learning engineers have the skills to implement the proper infrastructure and processes you need to build, optimize, and scale production-ready ML applications that integrate with key business systems.
All-new ways to get value from your data
From use-case exploration to data acquisition, we help you identify machine learning opportunities, challenges, and goals. Whether it’s data discovery or model training, we put our engineering expertise to work, helping ensure your models deliver value back to the business.
Model compliance and confidence
Model drift leads to faulty predictions, which lead in turn to financial damage and risk. We help make sure your models are properly validated and tested, adhering to organizational and regulatory policies, then work to monitor and proactively refit drifting models.
Why phData for Machine Learning on Databricks?
Machine learning expertise meets Databricks know-how
Support and expertise across the full machine learning lifecycle
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.
A vigilant, systematic approach to machine learning security
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.