At Dataiku Product Days 2024, François Sergot, product manager at Dataiku specializing in XOps, and Chris Helmus, senior sales engineer at Dataiku, tackled a critical challenge: how to move AI projects beyond experimentation and into reliable, scalable production.
Their session broke down the persistent gaps in operationalizing AI, the principles behind effective MLOps and LLMOps, and how Dataiku is designed to streamline the end-to-end AI lifecycle. François laid the foundation for MLOps best practices, and Chris followed with a live demonstration, showing exactly how Dataiku enables automation, governance, and seamless collaboration across teams.
Why Operationalization Remains a Challenge
MLOps isn’t new to Dataiku. For over a decade, we’ve seen organizations struggle with the same core obstacles when bringing machine learning (ML) models, large language models (LLMs), and AI-driven applications into production.
Most AI projects start within small, focused data science teams that handle everything — from experimentation to initial deployment. While this approach works early on, scaling AI across an enterprise introduces complexity. IT, governance teams, and business leaders — each with their own requirements, infrastructure constraints, and risk considerations — must get involved.
Without a structured approach, AI projects often face bottlenecks, misalignment, and inefficiencies. What works for a handful of models quickly becomes unsustainable at scale. As François explained, this is where MLOps comes in. By applying DevOps principles to AI, MLOps introduces automation, standardization, and governance, ensuring AI projects aren’t just functional — but reliable in production.
Dataiku’s Approach to MLOps: A Unified, Scalable Framework
At Dataiku, MLOps is built on three fundamental principles that ensure AI projects are not only operationalized effectively but also scalable and adaptable to enterprise needs.
1. Multi-Persona Collaboration
AI projects involve multiple stakeholders — data scientists, ML engineers, IT teams, and business users — each with different needs and technical expertise.
Dataiku is designed to bridge these gaps by providing a flexible environment where both low-code/no-code users and advanced coders can collaborate seamlessly. Whether teams prefer visual workflows or custom scripting, Dataiku ensures that AI models are not just built, but adopted, deployed, and governed in a way that aligns with organizational goals.
2. Multi-Axis AI Deployment
A successful AI project is more than just a model — it’s a full ecosystem that includes:
- Data pipelines for ingestion and transformation
- Automation and workflows to streamline processes
- Dashboards and applications for decision-making
- Governance and monitoring to ensure reliability and compliance
Dataiku goes beyond model deployment, enabling teams to operationalize entire AI projects — from data preparation to deployment and monitoring — so they scale efficiently and remain reliable in production.
3. Multi-Platform Flexibility
Most organizations operate in a complex tech landscape, using a combination of on-prem, cloud, and hybrid infrastructures.
With Deploy Anywhere, Dataiku allows AI teams to operationalize models across multiple environments — whether on AWS, Azure, GCP, Snowflake, Databricks, or on-prem systems. This flexibility ensures that AI projects remain adaptable, scalable, and aligned with existing infrastructure investments.
By integrating these three principles, Dataiku eliminates friction in AI deployment, making MLOps scalable, repeatable, and aligned with enterprise demands.
Extending MLOps to LLMOps: Adapting AI Operations for GenAI
LLMs introduce new challenges that traditional MLOps frameworks weren’t designed for — including higher costs, stricter compliance requirements, security risks, and evolving model behaviors. These complexities require a structured approach to governance, monitoring, and operationalization.
To address this, Dataiku developed the LLM Mesh, a framework that enables organizations to build enterprise-grade GenAI applications while maintaining control over cost, compliance, and infrastructure. Instead of requiring separate workflows, the LLM Mesh integrates directly into existing AI pipelines, ensuring a unified approach to governance and scalability.
With the LLM Mesh, organizations can:
- Seamlessly integrate LLMs into existing AI workflows rather than managing them in isolation.
- Proactively monitor and control costs to prevent excessive resource consumption.
- Automate evaluations of faithfulness, accuracy, and bias to ensure model reliability and compliance.
By extending MLOps principles to LLMs, Dataiku enables organizations to operationalize GenAI safely, efficiently, and at scale — without losing control or oversight.
XOps in Action
To demonstrate these principles in practice, Chris walked through a real-world AI deployment scenario at a manufacturing company using predictive maintenance models to minimize factory downtime.
Before Dataiku: Fragmented, Siloed Operations
Previously, operational processes were disjointed across teams and technologies, resulting in:
- Limited visibility: Model drift and data quality issues often went undetected, leading to unexpected failures.
- Manual inefficiencies: Retraining and governance workflows lacked automation, resulting in inconsistent model updates.
- Scaling constraints: Integrating GenAI with existing predictive models was complex and difficult to manage effectively.
After Dataiku: A Unified, Automated Workflow
With Dataiku’s XOps framework, these challenges are resolved through a centralized system that streamlines model management, monitoring, and automation:
- Proactive monitoring: Automated alerts flag model drift, notifying the data science team via Slack.
- Smart retraining: Dataiku runs quality checks to determine if retraining is needed, then triggers an automated, governed update process.
- Seamless AI integration: GenAI enhances predictive maintenance by generating technician reports aligned with internal safety guidelines.
At the core of this transformation is the model evaluation store, continuously tracking data drift, prediction drift, and performance metrics to ensure models remain accurate and reliable. Meanwhile, unified monitoring consolidates batch processes, API endpoints, and AI model health into a single dashboard, giving teams real-time oversight across all environments. Governance is embedded at every stage — from model validation to deployment — ensuring compliance and transparency.
Chris demonstrated these capabilities in action through a live walkthrough, showcasing how Dataiku automates key workflows and maintains AI reliability at scale.
To jump straight into the demo, skip to 11:36 in the video.
What’s Next: Strengthening AI Deployment & Governance
For AI to scale successfully, organizations need more automation, better governance, and tighter cost control. Dataiku is focused on two key areas to make this happen.
Automating AI Readiness & Testing
Deploying AI at scale requires clear standards for production readiness. To support this, Dataiku has expanded its readiness checks to:
- Enhance built-in QA testing for Python code, pipelines, and APIs, preventing issues before they reach production.
- Introduce new validation benchmarks with the upcoming Project Standards, strengthening AI governance and deployment readiness.
While QA testing improvements are already available, Project Standards will further refine pre-deployment validation to meet enterprise AI requirements.
Expanding LLM Governance & Cost Control
As LLMs become a core part of AI strategies, organizations need greater oversight and cost management. Dataiku has expanded its capabilities to:
- Enforce proactive cost controls, preventing unnecessary spending.
- Automate auditing of LLM-generated outputs, improving governance and reliability.
- Introduce new best practice benchmarks with the upcoming Project Standards, accelerating the design of production-grade projects.
Bringing Data, LLMOps, and AI Under One Framework
Successfully operationalizing AI goes beyond model deployment and requires a unified approach that integrates data, governance, infrastructure, and cross-team collaboration.
With Dataiku’s XOps framework, organizations can:
- Automate workflows to streamline AI deployment and monitoring.
- Enforce governance and compliance while maintaining flexibility.
- Maximize efficiency by seamlessly integrating AI across business operations.
This is the future of AI at scale — where data, models, and AI applications work together in a structured, governed, and efficient ecosystem.