Enterprise machine learning platform on Snowflake

Project highlights

• Delivered one of New Zealand's first Snowflake-native end-to-end machine learning platforms.
• Consolidated data engineering, feature engineering, model development and model serving into a single governed environment.
• Embedded machine learning outputs directly into operational systems rather than leaving them within analytics teams.
• Delivered the essential platform in approximately three months.
• Reduced integration complexity, infrastructure overhead and security risk through a unified architecture.
• Created a repeatable machine learning platform that can be extended across additional government organisations.\
• Ongoing client engagement.
  

About the client

The client is a major New Zealand government agency responsible for delivering critical public services.

As demand grew for faster, more informed operational decisions, the organisation increasingly relied on machine learning to identify patterns, generate predictions and support decision-making. However, the underlying data, models and supporting infrastructure had evolved separately over time.

Models, data and development environments were spread across multiple systems and, in some cases, local devices. This made machine learning difficult to scale, increased security risk and slowed the process of turning analytical insights into operational decisions.

Client request

The client wanted to establish machine learning as an enterprise capability rather than an isolated analytical function.

They needed a secure, governed platform where development teams could build, train, help deploy and operate machine learning models from a single environment, allowing AI-driven insights to be embedded directly into public service processes rather than remaining within analytics teams.

Challenge

The organisation has already invested in machine learning. The challenge was turning it into something the business could reliably use.

Models were difficult to develop, deploy and maintain because data, infrastructure and development environments were fragmented across multiple systems. As a result, valuable insights often remained within technical teams instead of supporting operational decisions.

The solution needed to:

1. Consolidate fragmented machine learning development into one governed environment.
2. Reduce the complexity, cost and security risks of deploying machine learning into production.
3. Embed model outputs directly into operational systems so AI could support faster, more informed public service decisions.

Solution

A single platform for the entire AI lifecycle

The Snowflake-native platform supports every stage of enterprise machine learning, from automated data ingestion and transformation through to model development, deployment and ongoing monitoring.

Rather than moving data and models between multiple tools, development teams can build, test and operationalise AI within one governed environment using shared data products, reusable feature stores and integrated machine learning services.

AI built into operational workflows

A key innovation was the orchestration layer that connects Snowflake-hosted machine learning models directly with operational systems.

Instead of waiting for analysts to interpret model outputs, operational systems can consume AI outputs automatically, allowing machine learning to support day-to-day decision-making while maintaining the governance, privacy and security expected within government.

Designed to evolve

The platform was designed as a long-term enterprise capability rather than a point solution. Emerging Snowflake capabilities including Workspace Notebooks, Workspace Streamlit, SnowConvert AI and Cortex Code (CoCo) can be adopted without redesigning the underlying architecture, allowing the organisation to continually expand its AI capability over time.

Results

The project transformed machine learning from an isolated analytical capability into an operational capability.

Instead of developing and deploying models across multiple disconnected environments, the agency now operates from a single governed platform where AI can move efficiently from development into production.

Key outcomes include:

• Delivered one of New Zealand's first Snowflake-native end-to-end enterprise machine learning platforms.
• Reduced technical debt, integration complexity and security risk by consolidating the machine learning lifecycle into a single governed environment.
• Enabled machine learning outputs to be embedded directly into operational systems, supporting faster and more automated public service decisions.
• Delivered the essential platform in approximately three months, with continuous enhancement through rolling releases.
• Created a reusable architecture that has generated strong interest across New Zealand and can be rapidly deployed across additional agencies, sectors and future use cases.

Lessons learned

For public sector organisations, AI innovation and governance are deeply interconnected. By designing governance, security and privacy into the platform from the beginning, organisations create an operating model that can be confidently reused across new use cases, agencies and sectors.