
Andrey Mukomolov enhanced the datarobot-community/terraform-provider-datarobot repository by developing features that expanded agentic playground support and enabled custom model selection for LLM blueprints, aligning Terraform provider capabilities with DataRobot 11.1 APIs. He implemented these features using Go and Terraform, updating provider schemas, API models, and test coverage to support flexible agentic workflows and reduce manual configuration. In the datarobot/datarobot-user-models repository, Andrey addressed a critical security vulnerability by removing insecure dependencies and upgrading packages, applying Python development and dependency management best practices. His work demonstrated depth in backend integration, security hardening, and robust automation for infrastructure-as-code environments.
March 2026 monthly summary for datarobot-user-models focused on security hardening and dependency hygiene. Implemented critical CVE remediation by removing pydantic-ai-slim and upgrading datarobot-genai, accompanied by a version update. Changes delivered with minimal disruption to existing models and validated through CI/tests; release notes prepared for stakeholders.
March 2026 monthly summary for datarobot-user-models focused on security hardening and dependency hygiene. Implemented critical CVE remediation by removing pydantic-ai-slim and upgrading datarobot-genai, accompanied by a version update. Changes delivered with minimal disruption to existing models and validated through CI/tests; release notes prepared for stakeholders.
September 2025 monthly summary focused on enhancing configurability and flexibility of the Terraform provider for the DataRobot Terraform integration. Delivered a feature enabling custom model selection in the DataRobot LLM blueprint, specifically through support for llm_settings.custom_model_id in the datarobot_llm_blueprint payloads, aligning with DataRobot 11.1 agentic playground capabilities. This work reduces manual configuration, accelerates experimentation with different models, and strengthens our ability to tailor LLM deployments to customer needs.
September 2025 monthly summary focused on enhancing configurability and flexibility of the Terraform provider for the DataRobot Terraform integration. Delivered a feature enabling custom model selection in the DataRobot LLM blueprint, specifically through support for llm_settings.custom_model_id in the datarobot_llm_blueprint payloads, aligning with DataRobot 11.1 agentic playground capabilities. This work reduces manual configuration, accelerates experimentation with different models, and strengthens our ability to tailor LLM deployments to customer needs.
Concise monthly summary for 2025-08: Delivered Agentic Playground Type support in the DataRobot Terraform provider, enabling agentic playground_type selection and alignment with 11.1 API; updated API models, provider schema, and tests; committed and validated changes to support agentic workflows, delivering measurable automation value.
Concise monthly summary for 2025-08: Delivered Agentic Playground Type support in the DataRobot Terraform provider, enabling agentic playground_type selection and alignment with 11.1 API; updated API models, provider schema, and tests; committed and validated changes to support agentic workflows, delivering measurable automation value.

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