Dataiku, The Universal AI Platform™, combinesDataOps, MLOps, LLMOps, and AgentOps for a powerful approach that breaks down silos and accelerates delivery.
By reconciling the needs tied to data, machine learning (ML), large language model (LLM), and AI agent operations in one place, your data and AI initiatives are built on solid foundations, governed effectively, and scaled confidently.
Read More on Dataiku’s XOps Philosophy
AI engineering operations in Dataiku ensures a smooth handoff between teams by enabling all stakeholders to work within a unified platform. Data scientists, IT professionals, and business analysts can seamlessly collaborate, each contributing within their area of expertise to move projects faster from development to production.
This unified approach eliminates the need for complex handovers between disparate systems, reducing friction and accelerating time to production. Using Dataiku, an FSI organization saw a 90% reduction in overall time to deployment compared to their prior desktop solutions.
Deep Dive Into How a Leading Financial Services Firm Leveraged Ops in Dataiku
DataOps capabilities in Dataiku ensure users can design, deploy, and maintain robust data pipelines with smooth data flow, quality checks, and governance across the entire AI lifecycle.
Foster consistency and trust in analytics outcomes with tools like data quality rules, the data catalog, golden datasets, column-level lineage tracking, and more.
Explore DataOps in Dataiku
The Dataiku MLOps framework simplifies the deployment, monitoring, and maintenance of ML models at scale. It automates key processes (such as model retraining and data drift detection) while allowing human oversight to ensure high-quality performance.
With built-in governance and monitoring, teams can efficiently manage the entire model lifecycle, from experimentation to production. Regardless of the infrastructure, Dataiku will ensure models remain accurate and relevant over time.
Learn more about MLOps in Dataiku
Matthieu Dupuis
Head of AI at Bouygues Télécom
Subhadip Roy
Head of Machine Learning Engineering, AI, and Data at DeloitteWhether using open source or cloud-hosted LLMs, Dataiku provides the required layer of governance and control via LLMOps and AgentOps capabilities.
From prompt validations and standardized evaluation metrics to compliance policies, Dataiku lets you deploy agentic and generative AI use cases with confidence.
DIVE DEEPER ON LLMOPS
Dataiku emphasizes operational flexibility. With Deploy Anywhere and Unified Monitoring, Dataiku lets you deploy models on platforms like AzureML, Databricks, SageMaker, Snowflake, and VertexAI.
Leverage models from other platforms by connecting them directly to Dataiku. Use Dataiku’s top-tier explainability and make your models from other platforms accessible to non-technical users.
Dataiku unified monitoring provides a comprehensive oversight of production environments across all deployments — including models built outside of Dataiku. This centralized view ensures that teams can monitor performance, identify risks, and maintain compliance across the organization with ease.
Specific insights include deployment status, model health, sign-off progress of each project, and much more.
Learn About Dataiku’s Unified Monitoring
Move from manual to automation with scenarios managing full orchestration of both analytics and AI projects.
For example, build alerts for data drift, model retrain, and trigger pushes to deployment — all without manual intervention.
Learn More About Automation Scenarios in Dataiku
MandM uses Dataiku’s deployment infrastructure and MLOps capabilities to amplify the impact of their modest data science team, delivering impactful use cases across the company.
We sat down with Subhadip Roy, Head of Machine Learning Engineering, AI, & Data at Deloitte, to talk MLOps. Learn how Dataiku can help make MLOps a success.
The NHS uses Dataiku for MLOps, model monitoring, and more to achieve its vision. Instead of merely describing and detailing the prevalence of disease in a population, they can start to predict disease for individuals.
