Why Your Company Needs MLOps on Databricks

September 2, 2025

Every company aims to be data-driven. But in reality, many machine learning projects get stuck in development and never help to support the business. Failure to deliver ML projects to product leads to wasted investment, frustrated teams, and missed opportunities to deliver real business value.

What is MLOps — and Why It Matters

MLOps is all about taking the principles of DevOps and applying them to machine learning. It covers the whole lifecycle:

Tracking experiments and models
Automating training and deployment
Monitoring performance and drift
Managing governance, security, and compliance

Without MLOps, ML projects often stall. Different teams build their own workflows, model versions get lost, and no one knows which model is actually running in production. Worse still, compliance and governance gaps can leave companies exposed to risk.

With MLOps, your ML pipelines become repeatable, reliable, and auditable. You can trust your models, your teams can collaborate, and your business can actually capture value from AI.

Why Databricks is the Best Platform for MLOps

Plenty of tools promise to help with MLOps. But Databricks has a unique advantage: it brings data engineering, analytics, and machine learning together on one platform.

Unified Lakehouse Platform – One place for raw data, clean data, and ML-ready features. No more juggling multiple platforms.
MLflow integration – Experiment tracking, model versioning, and deployment are built right in. No bolted-on extras.
Unity Catalog governance – Centralised security, lineage, and compliance across all your data and models.

Real Benefits for Your Business

What does all of this mean in practice? When you get MLOps right on Databricks, you see benefits across the board:

Faster time to production – Models move from notebook to endpoint quickly and consistently.
Lower costs – Efficient pipelines and scalable compute reduce wasted spend.
Governance and trust – Unity Catalog ensures compliance and full visibility into your models and data.
True collaboration – Data engineers, scientists, and business stakeholders can finally work together in one environment.

These aren’t just technical wins. They’re business wins. Faster insights. Lower risk. Clearer value.

How to Get Started

Getting started with MLOps on Databricks doesn’t need to be overwhelming. Here’s a simple roadmap:

Review your current workflows
Where do models currently live? How are they deployed? What breaks most often?
Identify the gaps
Look at reproducibility, governance, monitoring, and cost. Where are the risks?
Leverage the platform
Use Databricks features like AutoML, MLflow, Delta Live Tables, and Unity Catalog to close those gaps.
Bring in expertise
Partner with specialists who’ve solved these challenges before. It’s the quickest way to avoid costly mistakes.

Why Work With Us

At Kelvin Analytics, we’ve helped teams move from “proof of concept” to production-ready ML on Databricks. We know the pitfalls, the shortcuts, and the best practices that really work in the real world.

We don’t just drop in tools and leave. We help you design robust architectures, build reliable pipelines, and upskill your team so they can confidently evolve with the platform.

👉 Get in touch with us today to explore how we can help your business succeed.

How to realise Data Value

September 1, 2025

Managing data is complex and challenging. Often the technical diffulties distact our focus from the real goal of data - driving value for the business. Too often we encounter data teams who are working flat out to deliver data, but with unsatified stakeholders.

One of the key tools we use in Kelvin Analytics are data value propositions. A data value proposition is a clear and concise statement that communicates how this data will bring value to the business.

We work alongside the business to help them articulate these propositions in a way that data teams can understand. These propositions provide a guiding star for data teams who are swamped with complexity and competing demands. Your business stakeholders won’t care about your data pipelines but they do care about their pricing model.