Name: DataRobot
Brand: DataRobot
SKU: 62cfea87a10437da5c6c6616
Rating: 4.6 (143 reviews)

Question 1

Implementation Process

Accepted Answer

Pre-Implementation Planning: Define business objectives and use cases. Identify stakeholders and form a project team. Gather data sources and understand data requirements. Data Preparation: Data extraction from various sources (databases, APIs, etc.). Cleaning, preprocessing, and merging datasets. Conduct exploratory data analysis to understand data characteristics. Modeling: Upload prepared datasets to DataRobot. Use DataRobot’s automated modeling features to build models. Explore model performance, interpret results, and select the best model. Validation & Testing: Validate model performance on holdout datasets. Conduct A/B testing or other validation methods in production environments. Deployment: Deploy models into production using DataRobot’s deployment options (API, batch scoring, etc.). Monitor performance and ensure integration with existing systems. Monitoring & Maintenance: Continuously monitor model performance and drift. Regularly retrain and update models as necessary.

Question 2

Customisation

Accepted Answer

Generative AI Catalyst Program : This program is designed to build lasting generative AI capabilities across an organization, customized to its specific needs. It ensures that the AI solutions are tailored to the unique requirements and goals of the business. Unified Platform for Generative and Predictive AI : DataRobot provides a single platform that integrates both generative and predictive AI capabilities. This allows businesses to customize their AI solutions to address a wide range of use cases, from predictive analytics to generative content creation. Full Visibility and Governance : The platform offers comprehensive governance and observability features, enabling businesses to customize their AI operations with full visibility and control. This ensures that the AI solutions are aligned with the organization’s compliance and regulatory requirements. Agility and Flexibility : DataRobot’s open AI ecosystem allows businesses to innovate rapidly and adapt their AI solutions as their needs evolve. This flexibility is crucial for businesses that operate in dynamic environments and need to continuously update their AI models and strategies. Deep Ecosystem Integrations : The platform supports deep integrations with various data sources and systems, including Microsoft Azure. This allows businesses to pull data directly from their existing ecosystems, saving time and ensuring seamless integration with their current workflows. LLMOps Capabilities : DataRobot’s LLM operations (LLMOps) capabilities and out-of-the-box LLM performance monitoring enable businesses to implement cutting-edge generative AI techniques while monitoring for factors such as toxicity, truthfulness, and cost. This ensures that the AI solutions are not only effective but also responsible and ethical.

Question 3

Additional Costs

Accepted Answer

Setup Fees : Professional Services : These fees cover the initial setup and customization of the DataRobot platform to fit the specific needs of the organization. This can include data integration, model development, and deployment services. The cost of professional services can vary widely based on the complexity and scope of the project. Maintenance and Support Charges : Ongoing Maintenance : This includes regular updates, patches, and system maintenance to ensure the platform runs smoothly. Maintenance costs are typically included in the subscription fee but can vary based on the level of service required. Support Plans : DataRobot offers multiple support plans, which can include 24/7 live support, access to DataRobot University, and dedicated support from data scientists and AI engineers. The cost of these support plans can vary based on the level of support and the specific needs of the organization. Training Costs : Training Programs : DataRobot provides various training options, including in-person training, live online sessions, webinars, and access to DataRobot University. The cost of these training programs can vary depending on the format and depth of the training required. Customization and Integration : Custom Solutions : Organizations may need to customize the DataRobot platform to meet specific business goals, which can involve additional costs for custom development and integration with existing systems. Additional Licenses : User Licenses : The cost of additional user licenses can increase the overall cost, especially for large organizations with many users. The pricing is typically based on the number of users and the volume of data processed. Cloud Hosting Fees : Cloud Marketplaces : If DataRobot is purchased through cloud marketplaces like AWS, Azure, or Google Cloud, there may be additional hosting fees associated with using these cloud services. Specialty Packages : Specialty Services : DataRobot offers specialty packages such as AI and ML road mapping, model migration, and hackathons, which can incur additional costs. These packages are designed to help organizations with specific AI needs and can be tailored to their requirements.

Question 4

Training

Accepted Answer

Training Programs : DataRobot University : This is a central hub for all training resources, offering a variety of learning paths based on role, skill level, and area of interest. Users can choose from instructor-led or self-paced courses, labs, and more. On-Demand Courses & Labs : These self-paced courses allow users to learn at their own convenience. The lab courses provide hands-on experience with specific skills, which can be taken anytime for a refresher. Live Training : For those who prefer a more interactive learning experience, DataRobot offers live training sessions with expert data scientists. These virtual classroom sessions provide real-time instruction and the opportunity to ask questions. Instructor-Led Training : DataRobot provides deep-dive technical training for machine learning practitioners and high-level courses for all team members involved in AI initiatives. This training helps businesses understand machine learning at every level. Support Services : 24/7 Live Support : DataRobot offers round-the-clock support to address any critical issues that may arise. This includes access to a team of data scientists and AI engineers who can provide hands-on expertise. Support Portal : Users can log into the Support Portal to submit help tickets, track the status of their cases, and access a wealth of troubleshooting articles, API documentation, and product release notes. Phone and Chat Support : DataRobot provides phone and chat support for immediate assistance. This is particularly useful for resolving urgent issues quickly. Knowledge Base and Community Forum : The platform includes a comprehensive knowledge base and a community forum where users can find answers to common questions, share insights, and connect with other DataRobot users. Specialty Support Packages : Applied AI Support : This package provides ongoing access to DataRobot’s team of Applied AI Experts, who can help users progress their AI use cases and initiatives. It includes monthly access to DataRobot University and real-time answers to product questions or issues. Technical Support : Real-time technical support is available to address any platform-related issues. This includes assistance with product questions and troubleshooting. AI Expert Packages : These packages offer support from experienced data scientists, AI strategists, and AI engineers. They help accelerate AI initiatives by providing deep AI expertise and broad use case experience. Generative AI Catalyst Program : This program is designed to help organizations quickly start driving real-world value from generative AI. It includes hands-on labs, hackathons, and insights from hundreds of DataRobot customers to upskill teams and instill an AI-driven culture. Specialty Packages : AI and ML Roadmapping : This service helps translate strategic business objectives into machine learning projects and build an extensive pipeline of applied AI use cases. Model Migration and Governance : These services assist with converting or importing legacy custom models into the DataRobot platform and adopting a risk-based approach to model governance. Hackathons : For code-first teams, DataRobot offers competition-based, hands-on hackathons to solve specific data science problems or advance data science proficiency.

Question 5

Security Measures

Accepted Answer

Encryption : Data at Rest : DataRobot uses encryption to protect data stored on its servers. This includes encrypting data at rest using industry-standard encryption algorithms. Data in Transit : DataRobot also encrypts data in transit using secure protocols such as TLS (Transport Layer Security) to prevent unauthorized access during data transmission. Access Controls : Role-Based Access Control (RBAC) : DataRobot implements RBAC to ensure that only authorized users have access to specific data and functionalities. This helps in minimizing the risk of unauthorized access. Audit Logs : The platform maintains detailed audit logs to track who accessed or modified data, providing a trail for security audits and investigations. Compliance with Industry Standards : GDPR and HIPAA Compliance : DataRobot is compliant with various industry standards and regulations, including the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA). This makes it suitable for handling sensitive data in regulated industries. ISO 27001 and SOC 2 Type II Certification : DataRobot adheres to international standards for information security management systems, such as ISO 27001, and has obtained SOC 2 Type II certification, which verifies that the system will keep client or customer sensitive data secure. Data Privacy and Tokenization : Tokenization : For highly regulated types of personal data, DataRobot offers tokenization, which replaces sensitive data with non-sensitive tokens. This allows for secure data processing while maintaining the ability to re-identify the data outside of the DataRobot platform. On-Premises Deployment : For customers with stringent data privacy requirements, DataRobot offers on-premises deployment options, ensuring that data remains within the organization’s infrastructure. Secure Configurations and OAuth : OAuth-Based Authentication : DataRobot supports OAuth-based authentication for secure data connections. IT administrators can configure and securely share OAuth-based authentication parameters without exposing sensitive fields. Secure Configuration Sharing : Administrators can create and share secure configurations for data connections, ensuring that sensitive credentials are protected and only accessible to authorized users. Physical and Organizational Security : Data Centers : DataRobot’s SaaS customer data is hosted in secure AWS data centers in the US and Ireland. These data centers are equipped with robust physical security measures to prevent unauthorized access. Organizational Measures : DataRobot takes organizational precautions to protect against unauthorized access, alteration, or destruction of personal data. This includes employee training and strict access policies. Monitoring and Incident Response : Real-Time Monitoring : DataRobot continuously monitors its systems for security threats and vulnerabilities. This includes monitoring for data breaches, unauthorized access attempts, and other security incidents. Incident Response : In the event of a security incident, DataRobot has established protocols for incident response, including notifying affected customers and taking immediate action to mitigate the impact. Regular Security Audits and Updates : Security Audits : DataRobot conducts regular security audits to identify and address potential vulnerabilities. These audits help ensure that the platform remains secure and compliant with industry standards. Security Updates : The platform is regularly updated to address new security threats and vulnerabilities. This includes applying patches and updates to maintain the security and integrity of the system.

Question 6

Updates

Accepted Answer

Frequency of Updates : Monthly Releases : DataRobot typically releases updates on a monthly basis. These updates include new features, enhancements, and bug fixes for both the SaaS and self-managed AI platforms. Feature Announcements : Each monthly release is accompanied by announcements that detail the new features and improvements available in the update. These announcements are accessible through the DataRobot documentation and release notes. Types of Updates : General Availability (GA) Features : These are fully tested and stable features that are made available to all users. GA features are ready for production use and are included in the monthly releases. Preview Features : These are features that are still in the testing phase and are made available to users for feedback. Preview features allow users to try out new functionalities before they are officially released as GA. Deprecations and Migrations : DataRobot also announces the deprecation of older features and guides migrating to newer functionalities. This ensures that users are aware of changes and can plan accordingly. Update Management : Release Notes : Detailed release notes are provided for each update, outlining the new features, enhancements, and bug fixes. These notes help users understand the changes and how they can benefit from the new functionalities. Documentation : Comprehensive documentation is available for each release, including user guides, API documentation, and technical support materials. This documentation helps users navigate the new features and understand how to implement them effectively. Automated Updates : For SaaS deployments, updates are typically applied automatically, ensuring that users always have access to the latest features and security patches. Users are notified of upcoming updates and any potential impact on their workflows. Manual Updates : For self-managed AI platforms, administrators are responsible for applying updates. DataRobot provides detailed instructions and support to help administrators manage the update process. Feature Rollout and Feedback : Feature Flags : DataRobot uses feature flags to control the rollout of new features. This allows the platform to enable or disable specific features for different user groups, facilitating a controlled and gradual rollout. User Feedback : Users are encouraged to provide feedback on new features, especially those in the preview phase. This feedback is used to refine and improve the features before they are made generally available. Security and Maintenance Updates : Security Patches : DataRobot regularly releases security patches to address vulnerabilities and protect against emerging threats. These patches are prioritized to ensure the platform remains secure. Maintenance Releases : In addition to feature updates, DataRobot also releases maintenance updates that focus on improving the stability and performance of the platform. These updates address known issues and enhance the overall user experience. Communication and Support : Update Notifications : Users are notified of upcoming updates through various channels, including email notifications, in-app messages, and the DataRobot documentation portal. This ensures that users are aware of the changes and can prepare accordingly. Support Resources : DataRobot provides extensive support resources, including a support portal, knowledge base, and community forums. Users can access these resources to get help with updates and troubleshoot any issues that arise.

Question 7

Data Ownership and Portability

Accepted Answer

Data Ownership : Customer Data : Customers retain all rights, title, and interest in their data. This includes any data uploaded into the DataRobot platform, models created by the customer, and predictions generated from customer data. Intellectual Property : While customers own their data and models, DataRobot retains ownership of the software, algorithms, and any enhancements or derivative works created using the platform. This includes any feedback or suggestions provided by customers, which DataRobot may incorporate into its services. Data Portability : Exporting Data : DataRobot allows customers to export their data, models, and predictions. This ensures that customers can move their data out of the platform if needed. The platform supports various export formats to facilitate data portability. APIs and Integrations : DataRobot provides APIs and integration capabilities that allow customers to connect with other systems and platforms. This enhances data portability by enabling seamless data transfer between DataRobot and other tools used by the organization. Data Processing and Compliance : Data Processing : DataRobot processes customer data only as necessary to perform its obligations under the agreement. This includes using the data to train models, generate predictions, and provide other AI services. Compliance with Regulations : DataRobot complies with various data protection regulations, including GDPR and CCPA. This ensures that customer data is handled in accordance with legal requirements, providing additional assurance regarding data ownership and privacy. Data Deletion : Data Retention and Deletion : Customers can request the deletion of their data from the DataRobot platform. DataRobot has policies in place to ensure that data is securely deleted upon request, in compliance with applicable data protection laws. Security Measures : Data Security : DataRobot employs robust security measures to protect customer data, including encryption, access controls, and regular security audits. These measures help ensure that customer data remains secure and under the control of the customer.

Question 8

Scaling Up / Down

Accepted Answer

Subscription-Based Pricing : Annual Subscription : DataRobot operates on an annual subscription model, which allows organizations to adjust their usage and scale their subscription based on their evolving needs. Customizable Solutions : Organizations can customize their DataRobot subscription to include the specific functionalities they need. This flexibility ensures that they only pay for the features and capabilities that are relevant to their business goals. Scaling Up : Adding Users and Data Volume : Organizations can scale up by adding more users or increasing the data volume processed by the platform. This can be done by contacting DataRobot’s sales team to adjust the subscription plan accordingly. Enhanced Support and Services : As organizations scale up, they may require additional support and services. DataRobot offers various support plans, including access to data scientists and AI engineers, to help organizations manage their growing AI initiatives. Scaling Down : Reducing Users and Data Volume : If an organization needs to scale down, they can reduce the number of users or the data volume included in their subscription. This adjustment can be made during the renewal period of the subscription. Flexible Terms : DataRobot’s subscription model is designed to be flexible, allowing organizations to make changes to their plan as their needs evolve. This includes the ability to scale down without incurring significant penalties, provided the changes are made in accordance with the terms of the subscription agreement. Cloud Marketplaces : Purchasing Through Cloud Marketplaces : DataRobot can be purchased through cloud marketplaces such as AWS, Azure, and Google Cloud. This provides additional flexibility for organizations to scale their usage based on their cloud infrastructure and existing cloud credits. Professional Services : Implementation and Customization : DataRobot offers professional services to help organizations implement and customize the platform as they scale. These services ensure that the platform is configured to meet the specific needs of the organization, whether they are scaling up or down. Monitoring and Management : Usage Monitoring : DataRobot provides tools and dashboards to monitor usage and performance. This helps organizations track their AI initiatives and make informed decisions about scaling their subscription. Automated Scaling : For organizations with fluctuating needs, DataRobot offers automated scaling features that adjust resources based on usage patterns. This ensures that the platform can handle varying workloads without manual intervention.

Question 9

The terms & conditions for contract renewal and cancellation

Accepted Answer

Contract Renewal : Term and Renewal of Orders : The subscription term specified in the Order Form will commence upon the Order Form Effective Date and will continue for the period set forth therein (the "Initial Term"). The agreement is non-cancelable during the Subscription Term set forth in the Order Form. No Automatic Renewals : DataRobot does not automatically renew licenses or services tied to periodic payments. Renewal must be explicitly agreed upon by both parties. Renewal Process : Customers are typically notified in advance of the end of their subscription term to discuss renewal options. This allows for any necessary adjustments to the subscription based on changing needs. Contract Cancellation : Termination for Breach : Either party may terminate the agreement for any material breach not cured within thirty (30) days following written notice of such breach. Termination for Insolvency : Either party may terminate the agreement immediately upon written notice if the other party files for bankruptcy, becomes the subject of any bankruptcy proceeding, or becomes insolvent. Refunds : If DataRobot is unable to correct a non-conformity in the software within 30 calendar days of written notice from the customer, the customer may terminate the license to the affected solution and receive a pro-rata refund of the license fees paid in advance for the remainder of the term. Non-Cancellable Orders : Except as otherwise set out in the agreement, the agreement and any applicable orders are non-cancellable, and all fees are non-refundable. Return of Software : Upon termination of the license, the end user must return all copies of the software and user documentation licensed under all terminated order forms within thirty (30) days following the date of such termination. Support and Maintenance : Support Suspension : DataRobot may suspend the customer's access to support, maintenance, and the solution if any payments are not received within 60 days of the date of invoice that has not been disputed in good faith. Audit Rights : DataRobot may conduct an audit of the use by the end user of the software to ensure compliance with the terms of the agreement, not more than once every twelve months. General Terms : Assignment : The agreement may not be assigned by the customer without DataRobot’s prior written consent. Notices : All notices or other communications required to be given under the agreement must be in writing and delivered either by U.S. mail, certified, return receipt requested, postage prepaid; by overnight courier; or as otherwise requested by the receiving party.

Question 10

Compliance

Accepted Answer

ISO 27001 Certification : Description : ISO 27001 is an internationally recognized standard for information security management systems (ISMS). It provides a framework for managing and protecting sensitive information through a systematic approach to managing sensitive company information so that it remains secure. Certification : DataRobot has received ISO 27001 certification, which validates that the company has implemented necessary safeguards to increase the reliability and security of systems and information. SOC 2 Type II Certification : Description : SOC 2 Type II is a certification that verifies a service provider’s controls relevant to security, availability, processing integrity, confidentiality, and privacy. It is particularly important for SaaS providers that store customer data in the cloud. Certification : DataRobot undergoes an annual independent assessment of its cloud control environment to ensure compliance with SOC 2 Type II standards. HIPAA Compliance : Description : The Health Insurance Portability and Accountability Act (HIPAA) sets the standard for protecting sensitive patient data. Companies that deal with protected health information (PHI) must have physical, network, and process security measures in place and follow them to ensure HIPAA compliance. Compliance : DataRobot’s Single-Tenant SaaS offering is HIPAA-compliant, making it suitable for healthcare organizations that need to manage sensitive healthcare data securely. GDPR Compliance : Description : The General Data Protection Regulation (GDPR) is a regulation in EU law on data protection and privacy in the European Union and the European Economic Area. It also addresses the transfer of personal data outside the EU and EEA areas. Compliance : DataRobot complies with GDPR requirements, ensuring that personal data is processed in a manner that respects the privacy rights of individuals. CCPA Compliance : Description : The California Consumer Privacy Act (CCPA) is a state statute intended to enhance privacy rights and consumer protection for residents of California, USA. Compliance : DataRobot adheres to CCPA regulations, providing transparency and control over personal data for California residents. Other Security Measures : Data Encryption : DataRobot employs robust encryption measures for data both in transit and at rest, ensuring that sensitive information is protected from unauthorized access. Access Controls : The platform uses role-based access control (RBAC) and multi-factor authentication (MFA) to ensure that only authorized users can access sensitive data and functionalities. Regular Audits and Penetration Testing : DataRobot conducts regular security audits and penetration testing to identify and mitigate potential vulnerabilities. Compliance Documentation : Automated Compliance Documentation : DataRobot automates the creation of compliance documentation for models, which can be customized to meet specific regulatory requirements. This includes generating reports that provide comprehensive guidance on effective model risk management. Bias Mitigation and Monitoring : Bias Mitigation : DataRobot offers customizable bias mitigation workflows to ensure that models are fair and compliant with regulatory standards. This includes monitoring for bias and providing tools to mitigate it.

DataRobot

Pre-Implementation Planning:

Data Preparation:

Modeling:

Validation & Testing:

Deployment:

Monitoring & Maintenance:

Generative AI Catalyst Program:

Unified Platform for Generative and Predictive AI:

Full Visibility and Governance:

Agility and Flexibility:

Deep Ecosystem Integrations:

LLMOps Capabilities:

Setup Fees:

Maintenance andSupport Charges:

Training Costs:

Customization andIntegration:

Additional Licenses:

Cloud HostingFees:

Specialty Packages:

Training Programs:

Support Services:

Specialty Support Packages:

Generative AI Catalyst Program:

Specialty Packages:

AI and ML Roadmapping: This service helps translate strategic business objectives into machine learning projects and build an extensive pipeline of applied AI use cases.

Model Migration and Governance: These services assist with converting or importing legacy custom models into the DataRobot platform and adopting a risk-based approach to model governance.

Hackathons: For code-first teams, DataRobot offers competition-based, hands-on hackathons to solve specific data science problems or advance data science proficiency.

Encryption:

Data at Rest: DataRobot uses encryption to protect data stored on its servers. This includes encrypting data at rest using industry-standard encryption algorithms.

Data in Transit: DataRobot also encrypts data in transit using secure protocols such as TLS (Transport Layer Security) to prevent unauthorized access during data transmission.

Access Controls:

Role-Based Access Control (RBAC): DataRobot implements RBAC to ensure that only authorized users have access to specific data and functionalities. This helps in minimizing the risk of unauthorized access.

Audit Logs: The platform maintains detailed audit logs to track who accessed or modified data, providing a trail for security audits and investigations.

Compliance with Industry Standards:

GDPR and HIPAA Compliance: DataRobot is compliant with various industry standards and regulations, including the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA). This makes it suitable for handling sensitive data in regulated industries.

ISO 27001 and SOC 2 Type II Certification: DataRobot adheres to international standards for information security management systems, such as ISO 27001, and has obtained SOC 2 Type II certification, which verifies that the system will keep client or customer sensitive data secure.

Data Privacy and Tokenization:

Tokenization: For highly regulated types of personal data, DataRobot offers tokenization, which replaces sensitive data with non-sensitive tokens. This allows for secure data processing while maintaining the ability to re-identify the data outside of the DataRobot platform.

On-Premises Deployment: For customers with stringent data privacy requirements, DataRobot offers on-premises deployment options, ensuring that data remains within the organization’s infrastructure.

Secure Configurations and OAuth:

OAuth-Based Authentication: DataRobot supports OAuth-based authentication for secure data connections. IT administrators can configure and securely share OAuth-based authentication parameters without exposing sensitive fields.

Secure Configuration Sharing: Administrators can create and share secure configurations for data connections, ensuring that sensitive credentials are protected and only accessible to authorized users.

Physical and Organizational Security:

Data Centers: DataRobot’s SaaS customer data is hosted in secure AWS data centers in the US and Ireland. These data centers are equipped with robust physical security measures to prevent unauthorized access.

Organizational Measures: DataRobot takes organizational precautions to protect against unauthorized access, alteration, or destruction of personal data. This includes employee training and strict access policies.

Monitoring and Incident Response:

Incident Response: In the event of a security incident, DataRobot has established protocols for incident response, including notifying affected customers and taking immediate action to mitigate the impact.

Regular Security Audits and Updates:

Security Audits: DataRobot conducts regular security audits to identify and address potential vulnerabilities. These audits help ensure that the platform remains secure and compliant with industry standards.

Security Updates: The platform is regularly updated to address new security threats and vulnerabilities. This includes applying patches and updates to maintain the security and integrity of the system.

Frequency of Updates:

Monthly Releases: DataRobot typically releases updates on a monthly basis. These updates include new features, enhancements, and bug fixes for both the SaaS and self-managed AI platforms.

Feature Announcements: Each monthly release is accompanied by announcements that detail the new features and improvements available in the update. These announcements are accessible through the DataRobot documentation and release notes.

Types of Updates:

General Availability (GA) Features: These are fully tested and stable features that are made available to all users. GA features are ready for production use and are included in the monthly releases.

Preview Features: These are features that are still in the testing phase and are made available to users for feedback. Preview features allow users to try out new functionalities before they are officially released as GA.

Deprecations and Migrations: DataRobot also announces the deprecation of older features and guides migrating to newer functionalities. This ensures that users are aware of changes and can plan accordingly.

Update Management:

Release Notes: Detailed release notes are provided for each update, outlining the new features, enhancements, and bug fixes. These notes help users understand the changes and how they can benefit from the new functionalities.

Documentation: Comprehensive documentation is available for each release, including user guides, API documentation, and technical support materials. This documentation helps users navigate the new features and understand how to implement them effectively.

Automated Updates: For SaaS deployments, updates are typically applied automatically, ensuring that users always have access to the latest features and security patches. Users are notified of upcoming updates and any potential impact on their workflows.

Manual Updates: For self-managed AI platforms, administrators are responsible for applying updates. DataRobot provides detailed instructions and support to help administrators manage the update process.

Feature Rollout and Feedback:

Feature Flags: DataRobot uses feature flags to control the rollout of new features. This allows the platform to enable or disable specific features for different user groups, facilitating a controlled and gradual rollout.

User Feedback: Users are encouraged to provide feedback on new features, especially those in the preview phase. This feedback is used to refine and improve the features before they are made generally available.

Security and Maintenance Updates:

Security Patches: DataRobot regularly releases security patches to address vulnerabilities and protect against emerging threats. These patches are prioritized to ensure the platform remains secure.

Maintenance Releases: In addition to feature updates, DataRobot also releases maintenance updates that focus on improving the stability and performance of the platform. These updates address known issues and enhance the overall user experience.

Communication and Support:

Update Notifications: Users are notified of upcoming updates through various channels, including email notifications, in-app messages, and the DataRobot documentation portal. This ensures that users are aware of the changes and can prepare accordingly.

Support Resources: DataRobot provides extensive support resources, including a support portal, knowledge base, and community forums. Users can access these resources to get help with updates and troubleshoot any issues that arise.

Data Ownership:

Customer Data: Customers retain all rights, title, and interest in their data. This includes any data uploaded into the DataRobot platform, models created by the customer, and predictions generated from customer data.

Data Portability:

Exporting Data: DataRobot allows customers to export their data, models, and predictions. This ensures that customers can move their data out of the platform if needed. The platform supports various export formats to facilitate data portability.

APIs and Integrations: DataRobot provides APIs and integration capabilities that allow customers to connect with other systems and platforms. This enhances data portability by enabling seamless data transfer between DataRobot and other tools used by the organization.

Data Processing and Compliance:

Data Processing: DataRobot processes customer data only as necessary to perform its obligations under the agreement. This includes using the data to train models, generate predictions, and provide other AI services.

Compliance with Regulations: DataRobot complies with various data protection regulations, including GDPR and CCPA. This ensures that customer data is handled in accordance with legal requirements, providing additional assurance regarding data ownership and privacy.