DataRobot AI Platform 9.0
The DataRobot AI Platform is the only open, complete AI lifecycle platform leveraging machine learning that has broad interoperability, end-to-end capabilities for ML Experimentation and ML Production and can be deployed on-premises or in any cloud infrastructure. Exciting new features, a redesigned ML Experimentation user interface, new integrations with Snowflake and many more advancements make this a very exciting release for DataRobot customers.
Realize value at production scale and maximize your existing investments with the February DataRobot AI Platform release, including new features that provide speed, insights, and ecosystem advancements. This includes native integrations with Snowflake, the introduction of Python Scoring Code, and support for data scientists and software developers to create a seamless user experience
Realize business value from AI more quickly with the January DataRobot release and new features that provide speed, flexibility, and ecosystem advancements. New, hosted Notebooks allow the development of AI/ML projects with code-first or code-free experiences. See 21% faster results* when choosing Quickrun Autopilot mode with DataRobot AutoML. Save time by building No-Code AI Apps directly from a model leaderboard.
The new, user-friendly integration with custom model repositories with existing CI/CD tools like GitHub support ML Engineering teams’ automated workflows, while maintaining the DataRobot platform’s governance standards.
This month, DataRobot also debuts Dedicated Managed AI Cloud on Microsoft Azure. This hosted version of the DataRobot AI Cloud platform is deployed for each customer in a dedicated and separate virtual private cloud that is operated, monitored, and maintained by DataRobot in-house experts. Already available on AWS and Google Cloud marketplaces, Dedicated Managed AI Cloud can now also be purchased on the Azure Marketplace.
Working faster, working with more transparency – those are consistent themes in this month’s DataRobot AI Cloud release. Learn more how to improve the experience when working with custom models using DataRobot GUI – editable number of execution environments, easier tracking process and update training data when custom model has changed.
Change is happening all around – and impacting your business. Two new DataRobot AI Cloud features help you address change.
Learn more about Drift Over Time, which helps you with further insights to identify problems and patterns over time. With more information, you can better manage predictions. Deployment Prediction Processing Usage gives you useful details to show which predictions are delayed, why they are delayed, and the time frame so you can make adjustments as needed.
Today organizations are looking into new ways to apply AI to solve unique business problems—from projecting sales to complex manufacturing development—by adding ML models into the DNA of each business function. The main concern of organizations is how to move fast from experimentation to scaling AI without sacrificing trust and transparency.
In this release, DataRobot is excited to announce that Time Series Clustering is now available for SaaS users. In addition, DataRobot also focused on improving model observability with large-scale monitoring with Python, data drift monitoring over time, prediction processing stats, and more.
Also this month DataRobot Dedicated Managed AI Cloud is available for public preview. With this model, AI Cloud is deployed for each customer in a dedicated and separate VPC. By eliminating implementation time and resources, organizations can more quickly apply data engineering, machine learning, decision intelligence, and ML Ops capabilities.
Learn more about Dedicated Managed AI Cloud and other capabilities only found in the DataRobot AI Cloud platform.
Today’s economy is under pressure with inflation, rising interest rates and disruptions in the global supply chain. Many organizations are moving to reduce costs, improve operations, and revise forecasts. Strong model observability and MLOps process are needed to tackle these challenges for business-critical applications.
This release focuses on significant enhancements for DataRobot MLOps, specifically in the model monitoring area. These new features help you compare and evaluate production models with new insights, create custom metrics that are important to your business, and scale your monitoring to save time.
Learn more about these and other features only found in the DataRobot AI Cloud platform.
With new NLP hyperparameters and the power of automation, you can run new AI experiments much faster with DataRobot AI Cloud, which takes text data prediction explanations to the next level. Understand the impact of text, such as patterns of positive and negative feedback in customer reviews or product feedback. Access more granular insights to help determine new use cases and increase the trust, usability, and explainability of text features.
Find scale and speed with improvements in scoring code for time series models that involve large datasets. Now you can download the scoring code for time series models and run predictions on your largest datasets outside DataRobot AI Cloud in the environment of your choice.
On top of these new features, we are excited to announce that Algorithmia is now integrated with the DataRobot AI Cloud platform. Reduce operational costs for AI projects by deploying custom models to the Algorithmia environment. Take advantage of autoscaling deployment while benefiting from the DataRobot MLOps built-in capabilities.
Learn more about other features that are only found in the DataRobot AI Cloud platform.
Navigate uncertainty with AI-powered forecasting
Customer behavior and needs have changed dramatically. As a result, businesses are becoming more agile to keep up with changes to identify new opportunities that meet customer needs. Identifying trends in data helps to anticipate, which is why companies rely more on forecasting. But forecasting remains complex and laborious. It requires manual updating of data and adjustments to forecast outputs. These steps can delay decisions, preventing businesses from responding immediately to new demand patterns and market changes.
AI-powered forecasting enables organizations to respond to changes faster and make the right decisions. A broader set of data scientists leverage DataRobot AI Cloud advances AI forecasting to combine automation with best-in-class modeling techniques to streamline forecasting. Now you can experiment faster, build models for new segments without sacrificing accuracy, and most importantly, operationalize models in a few clicks completely out-of-the-box.
Learn more about what’s new in DataRobot AI Cloud.
As AI adoption matures across organizations, there are increased business requirements for scale, speed, and ensuring that AI models are fair and accurate. The DataRobot AI Cloud May 2022 Release, the first in a series of monthly releases for the cloud-based AI platform, focuses on new features that help users spend less time on scripting and uploading data and more time focusing on refining models and delivering impactful results.
The DataRobot AI Cloud May 2022 Release delivers nearly 40 new features to help data scientists and business leaders deliver AI results in a rapidly changing world.