François Sergot, senior product manager at Dataiku, hosted a 2024 Product Days session to unpack a core challenge that businesses face when scaling AI: managing increasing complexity as more people and teams get involved.
According to François, the more people you have, the more friction you can get. People spend an excessive amount of time answering questions, meeting requirements, and addressing different expectations. These become bottlenecks, leading to frustration and inefficiency.
This bottleneck is particularly pronounced when moving from a few successful AI projects to deploying at scale. Many companies find that what worked for a small data science team does not necessarily translate to hundreds or thousands of projects in production. So, what’s the solution?
MLOps: Making AI Work at Scale
MLOps plays a critical role in bridging the gap between successful experiments and scalable AI deployment. François made it clear that MLOps is not about magic but about practical processes that enable smooth deployment, monitoring, and collaboration across teams.
MLOps is not just about tools; it’s about collaboration between teams toward a single goal. It ensures AI is deployed properly and that teams work together efficiently.
The 3 Pillars of Scaling AI With MLOps
1. Seamless Operationalization
Operationalization refers to transforming an idea into a production-ready AI solution with minimal friction. François emphasized that AI is more than just models — it includes data connections, applications, dashboards, and regulatory compliance.
When deploying, models are just one piece of the puzzle. Connecting to data, building dashboards, and ensuring compliance are just as important.
Dataiku, the Universal AI Platform, enables seamless deployment by integrating documentation generation, governance, and model deployment tools that make the process efficient and transparent.
2. Replicating AI at Scale
A major roadblock to scaling AI is the lack of specialized talent and fragmented technical ecosystems. Dataiku addresses these challenges by promoting reusability:
- Data Catalogs: Centralized access to high-quality, pre-approved datasets
- Feature Stores: Shared feature engineering for machine learning models
- Project Bundles: Pre-packaged AI projects that can be deployed and reused across teams
According to François, "Your Center of Excellence should focus on innovation, while other teams should be able to reuse proven AI solutions without reinventing the wheel."
3. Embracing Generative AI Without Reinventing Everything
The rise of generative AI (GenAI) has introduced new complexities in AI deployment. Despite the hype however, many principles of traditional MLOps still apply.
GenAI broke the puzzle — but not completely. Many MLOps principles remain intact. The key is to integrate new pieces, such as cost monitoring and LLM-specific evaluation methods, into existing workflows.
— François Sergot, senior product manager at Dataiku
The Dataiku LLM Mesh is their response to these new challenges, providing a structured way to integrate GenAI while maintaining control over security, costs, and data governance.
What’s Next? The Future of MLOps and AI Scaling
François closed the session by highlighting upcoming advancements Dataiku MLOps, including:
- Unified Ops: Strengthening Dataiku’s capabilities to perform xOps across platforms like SageMaker, Azure ML, Databricks and Snowflake
- Scaling and Governance: Design Standards for production-grade projects, testing entire AI projects (not just models), and improving data lineage tracking
- Enhanced GenAI Guardrails: Control cost, toxicity, and privacy for LLM applications and build LLM-focused evaluation processes
MLOps is evolving, but the core remains the same. The real efficiency gains will come when MLOps, DataOps, and LLMOps are seamlessly integrated. That’s the next frontier.