What is Composable ML?
It is often said that building best-in-class models requires experimentation with data and algorithms. But with a significant portion of time spent on repetitive and mundane tasks like writing code for feature transformations or model operationalization, most data scientists have little time left to experiment with advanced algorithms.
DataRobot helps expert data scientists experiment and save time. With Composable ML, you can define your unique best-in-class machine learning algorithm, then combine it with built-in DataRobot capabilities that automate repetitive tasks, resulting in a dramatic boost in productivity.
Ultimate Flexibility to Define Your Unique Machine Learning Algorithm
Using Composable ML, expert data scientists have full flexibility to leverage their machine learning and domain knowledge to build the best model for any particular unique use case.
You can construct your own machine learning pipeline using out-of-the-box tasks mixed with custom tasks designed by you in Python or R. You can always install additional dependencies by using custom container environments for tasks.
World-Class Automation to Streamline Non-Modeling Tasks
Once you have defined your custom machine learning pipeline, you can use a wide range of additional built-in capabilities to help you automate even more complex, repetitive tasks.
In addition to our secure and reliable training infrastructure, you can experience automated feature discovery and engineering for your new model, compare modeling results on the leaderboard, and get instant access to a huge variety of explainable AI capabilities, such as feature impact and effects, prediction explanations, and automatic compliance documentation.
You can also deploy your new model and get specialized model monitoring, governance, and lifecycle management from DataRobot MLOps.
All of these capabilities are just a few clicks away and work right out of the box, with no extra DevOps work.
Share Your Expertise across the Organization
No matter how complex an algorithm is or what language or framework it uses, after a reusable machine learning algorithm has been packaged as a custom task or a pipeline, it can be shared and reused across the organization in just a few clicks. This helps to break barriers between teams across the organization: data scientists and machine learning engineers from the Center of Excellence can empower citizen data scientists and analysts by sharing their machine learning assets. Citizen data scientists can start with generic pipelines using AutoPilot. Then expert data scientists can bring in advanced methods before deploying the model, making data science truly a team sport.