Scaling AI successfully isn’t just about building models — it’s about ensuring they function in production, at scale, across teams and platforms. This requires a scalable and governed MLOps pipeline, particularly when models are developed across multiple platforms like Dataiku and Databricks.
In this Product Days session, Amanda Milberg, Principal Solutions Engineer at TitanML (and former Partner Sales Engineer at Dataiku), and Prasad Kona, Partner Solutions Architect at Databricks, explore how organizations can overcome common barriers to scaling AI, streamline operations, and achieve incremental business value by integrating Dataiku and Databricks.
But before diving into solutions, let’s first examine the core challenges organizations face when scaling AI.
The Challenges of Scaling AI
Across industries, organizations report similar frustrations as AI adoption expands. Inefficiencies compound, leading to increased errors, operational bottlenecks, and rising costs. Early AI initiatives may start smoothly with just a few models, but as AI programs grow, minor inefficiencies quickly escalate into major roadblocks. Operations teams spend excessive time troubleshooting rather than driving AI innovation.
And as AI complexity increases, so do compliance and governance concerns — especially with the introduction of large language models (LLMs). Managing traditional machine learning (ML) models separately from GenAI models adds governance risks, security concerns, and operational challenges.
The 3 Barriers to Delivering AI Value
A survey of 400 senior AI professionals, conducted by Dataiku and Databricks, revealed the three most significant barriers preventing organizations from scaling AI effectively:
1. Lack of an MLOps Strategy
More than half of senior AI professionals report an inability to quickly operationalize and iterate on data, analytics, and AI projects. A lack of a structured MLOps strategy is one contributing factor, often leaving AI siloed and making it harder for organizations to integrate models into business workflows, ultimately limiting real-world impact.
2. Data Quality and Access Challenges
A strong data foundation is essential for scalable AI pipelines, yet many organizations lack the infrastructure to ensure consistent data quality, accessibility, and governance. Without it, AI projects stagnate before delivering value.
3. The Shortage of Data & AI Talent
Technical AI talent is invaluable, but organizations can’t rely solely on data scientists to drive AI adoption. Instead, they must empower analysts, engineers, and business users with platforms like Dataiku, which democratizes AI across teams.
But just as organizations begin addressing these challenges, another disruptor emerges — the growing complexity of GenAI.
GenAI: Unlocking Value While Managing New Risks
GenAI presents massive opportunities, but it also introduces new operational risks. The survey “A CIO’s Guide to Modern Analytics,” conducted by Dataiku and Cognizant, gathered insights from 200 senior analytics and IT leaders and identified three primary concerns:
- Managing the cost of LLMs
- Keeping up with evolving AI regulations (such as the EU AI Act)
- Ensuring governance across both GenAI and traditional ML models
To address these risks, many organizations purchase additional tools — but this often leads to tool fragmentation, higher costs, and operational complexity.
- 44% of respondents say their current toolkit does not meet their analytical and AI needs.
- 43% of respondents do not consider their current tech stack modern.
- 60% of respondents use more than five tools to manage different AI lifecycle steps.
Instead of adding more disconnected tools, organizations should focus on consolidating and streamlining their AI stack. This is where Dataiku and Databricks together provide a structured, scalable, and governed solution.
Simplifying AI Operations Without Sacrificing Flexibility
To maximize AI investments, organizations need a seamless way to connect people, processes, and tools across the AI lifecycle. Dataiku and Databricks provide an integrated solution that supports AI scalability in two key ways:
- Supporting Multiple User Personas: AI is no longer limited to data scientists — Dataiku and Databricks enable collaboration across all skill levels, making AI development accessible to analysts, engineers, and business users.
- A Fully Integrated ML Development Lifecycle: With Dataiku sitting on top of Databricks, all data remains securely in Databricks while leveraging Databricks Compute for processing. Additionally, all assets, including datasets and models, are logged and stored in Databrick's Unity Catalog, ensuring consistent governance across AI projects.
This integration accelerates AI adoption, improves security, and simplifies AI governance, reducing the burden of managing multiple disconnected tools.
Databricks Unity Catalog: A Unified Approach to AI Governance
Scaling AI requires more than just model development — it demands centralized governance, security, and compliance. Without a unified strategy, organizations risk data silos, fragmented AI workflows, and regulatory challenges.
This is where Databricks Unity Catalog plays a pivotal role. Built on a lakehouse architecture, Unity Catalog centralizes governance for AI and data assets while ensuring seamless data management, security, and collaboration.
With Unity Catalog, organizations can:
- Discover, search, and securely access AI models, datasets, and notebooks across platforms.
- Set fine-grained access controls to enforce consistent governance across AI workflows.
- Track lineage and monitor models throughout their lifecycle, ensuring compliance and auditability.
Since Unity Catalog’s governance rules are inherited in Dataiku, organizations can maintain consistent security policies across both platforms — ensuring only the right users access the right data. But governance alone isn’t enough, organizations also need real-time monitoring of AI pipelines. This is where Dataiku’s Unified Monitoring comes in.
Bringing It All Together: See Unified Monitoring in Action
We’ve explored the challenges of scaling AI, the importance of AI governance with Unity Catalog, and how Dataiku enables unified monitoring, but how does this work in practice?
For a hands-on look at these capabilities in action, skip to 15:00 in the session recording.
In this live demo, Amanda and Prasad walk through how organizations can develop, deploy, and monitor models seamlessly across Dataiku and Databricks. The demo covers:
- How to connect and monitor Databricks models in Dataiku
- Evaluating model performance using feature importance and confusion matrices
- Deploying models seamlessly between Dataiku and Databricks
By using Dataiku as the centralized monitoring hub, organizations gain full visibility into AI and ML pipelines, improve governance, and scale AI adoption with confidence.
Scaling AI With Confidence
Successfully scaling AI isn’t just about building models — it’s about operationalizing AI across platforms, teams, and business functions. As AI initiatives expand, organizations face governance challenges, rising costs, and increasing complexity, especially with the rise of LLMs and GenAI.
By integrating Dataiku and Databricks, businesses can break through these barriers and create a scalable, governed AI ecosystem that delivers real business value.
- Dataiku provides a unified AI platform, empowering both technical and non-technical users to collaborate, build, and monitor AI models.
- Databricks ensures strong governance through Unity Catalog, giving organizations full control over data, models, and AI workflows.
- Unified Monitoring in Dataiku offers end-to-end visibility into AI pipelines, allowing teams to track drift, lineage, and model performance across platforms.
With Dataiku and Databricks, organizations no longer have to choose between scalability and governance — they achieve both within a single, streamlined AI ecosystem.