Thursday, April 18, 2024

DataRobot: Artificial Intelligence (AI) Portfolio Review

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DataRobot is an artificial intelligence (AI) cloud leader that provides access to AI across the globe. It enables organizations to leverage the transformative power of AI through its AI Cloud platform and a variety of AI solutions.

Founded in 2012, DataRobot serves a third of Fortune “500” companies.

Through its AI Cloud, DataRobot is able to provide a single system to deliver a range of AI products.

DataRobot AI Portfolio

Data Engineering

Data Preparation

Data Prep by DataRobot gives data analysts and scientists the ability to interactively and visually explore, combine, and shape data to train and deploy their machine learning (ML) models. 

  • Self-service data preparation suitable for novice and experienced users
  • Data preparation process is shortened using AI
  • Suitable for enterprise-grade databases
  • Automatic profiling, clustering, and cleaning of data

Feature Discovery

DataRobot’s Feature Discovery is an evolution of automated feature engineering. It improves ML models’ accuracy by automatically discovering, testing, and creating hundreds of valuable new features. Feature Discover intelligently generates the right features for your ML models by using pre-existing relationships across your data sources alongside complex data schemes.

  • Intuitive and visuals-based design
  • Automatic relationship suggestions between data points
  • Built-in time awareness for feature calculation
  • Offers transparent and traceable steps

Machine Learning


Automated Machine Learning by DataRobot enables you to meet the ever-growing demands of the AI industry. It provides the resources necessary for AI creators to deliver quality models with minimum strain on time, energy, and trust.

  • Automated, out-of-the-box data science practices and capabilities
  • Flexible model experimentation and building
  • Maximized data use through diversifying data types and open-source models
  • Model-agnostic frame that allows you to interpret and adjust results
  • Ready-for-deployment products

Automated Time Series

Automated Time Series is a companion solution by DataRobot that utilizes AI to forecast models at scale, allowing you to create, deploy, and maintain high-impact forecasts across various models.

  • AI-driven, automated forecasting using advanced algorithms and time-aware guardrails
  • Hyper-granular forecasting that breaks time series into individual components parts
  • Real-time and predictive anomaly detection and explanations
  • Cold start forecasting with minimal to no historical data
  • Nowcasting feature enables prediction of current events with minimal access to information and data

Zepl Notebooks

Zepl Notebooks is a company by DataRobot. It’s an open-source data science notebook for data scientists and analysts that enables them to do exploratory work in Python, R, and Scala in code-centric environments. Zepl also provides a single, centralized location for secure team collaboration, allowing you to build models using the power of DataRobot’s AI alongside their data science capabilities.

  • Built-in scheduler
  • Supports Python, R, Scala, and SQL
  • Connectable to data sources in seconds
  • Allows for importing and version control
  • Built-in resource and task monitoring

Decision Intelligence

No Code App Builder

DataRobot’s No Code App Builder enables you to create AI-powered applications with no need for any code. The tool was designed to make the creation of AI apps easier and more accessible to business users.

  • Pre-built templates and drag-and-drop widgets
  • Automatic return of detailed predictions and what-if scenarios
  • Supports collaboration with your AI consumer and business community
  • Pre-configured app optimization for the target outcome

AI Applications

DataRobot’s AI Applications enable organizations to access and consume the output of their predictive models and harness the full transformational power of AI.

  • Accessibility to a wide array of apps created by you and others
  • Turning predictive models AI apps in minutes
  • No-code app building
  • Easy-to-share and accurate insights
  • What-if scenario analysis
  • Flexible prediction making individually or in bulk

Decision Intelligence Flows

DataRobot Decision Intelligence Flows enable organizations to build rules based on complex business logic and use them to automate and accelerate the decision-making process. The Intuitive Decision Flow editor supports the creation of rules that combine multiple ML models, which can then be operationalized via DataRobot’s API.

  • Built-in support for complex rules
  • Fast decision-making at scale
  • Explainable and data-supported decisions
  • Documented and traceable decision-making process

MLOps and Trusted AI

DataRobot MLOps

DataRobot MLOps is a centralized starting point for producing AI models. It provides a single location to deploy, monitor, manage, and govern all of your ML models in production regardless of creation and deployment variables.

  • Low-code custom model monitoring
  • Versatile options for deploying models — on-premises, in the cloud, or hybrid deployment
  • Automated, out-of-the-box model health monitoring and production diagnosis
  • Built-in lifecycle management capabilities
  • Built-in governance, humility, and fairness policies and frameworks

Bias and Fairness

Bias and Fairness are DataRobot’s tools that ensure your AI and ML models share your ethics and values. It enables you to fix any bugs and imperfections that may cause the model to unintentionally behave in a biased way. DataRobot’s Bias and Fairness model also allows you to monitor your AI and ML machines in the long term to ensure they are still according to the set fairness and anti-bias standards.

  • Five different industry-standard fairness metrics
  • Result analysis that enables you to understand the root cause behind the machine’s bias
  • Proactive bias-monitoring for incoming model results

DataRobot AI Partners

The DataRobot AI Partner Program aims to help customers become more AI-driven organizations and enterprises through the implementation and adoption of ML models.

Becoming a DataRobot AI partner would grant you access to their expertise in AI as well as various partners, such as technology alliances, value-added resellers, solution partners, system integrators, and consulting firms.

DataRobot AI Use Case

DemystData is a New York-based FinTech software company looking to help clients explore and access the world of data to benefit their finances.

They wanted to be able to handle increasingly complex and massive datasets from various sources. However, their data scientists were spending a lot of time and energy manually building data science and ML pipelines.

Using DataRobot’s Automated Machine Learning platform, they were able to automate and streamline the bulk of the process with Feature Engineering, Model deployment, and Model Feature Selection. 

“DataRobot allowed us to take an inductive approach to problem-solving; a throw-spaghetti-at-the-wall approach to see what sticks,” says Jason Mintz, VP of product at DemystData. “We could take a step back and form a blank slate, find all the data we could and throw it into DataRobot and see what pops, what’s correlated, what’s truly predictive.”

The collaboration delivered a 10x efficiency increase at 10% of the cost. They were also able to democratize data science and open it up to all levels of the organization.

User Reviews of DataRobot AI

DataRobot’s portfolio of AI solutions has garnered consistently positive reviews through third-party websites over the past few years.

Gartner Peer Insights: 4.7 out of 5

G2: 4.4 out of 5

Capterra: 5 out of 5

TrustRadius: 8.8 out of 10

Industry Recognition of DataRobot AI

In 2021, DataRobot was named to the Forbes 2021 “Cloud 100”.

Additionally, in 2018, DataRobot won both Editors’ and Readers’ Choice Awards by Datanami.

DataRobot in the AI Market

DataRobot holds 2.8% of the AI software market, while market leader IBM holds 8.8%, as of 2019, according to IDC. DataRobot also holds 1.06% of the big data analytics market share and just under 0.5% of the predictive analytics market share.

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