From Static Predictions to Intelligent Automation: An MLOps Transformation by CACI and Snowflake
A leading media company* operating multiple radio brands had successfully deployed Machine Learning (ML) models to optimise ad selection and predict customer churn. Run on scheduled batch processes producing static outputs, the LM models were manually monitored and maintained. As the models became more business-critical, the client wanted to move from time-intensive manual oversight to a more automated, scalable approach for managing and retraining them.
Leveraging the client’s existing use of the data platform Snowflake, CACI designed a Machine Learning Ops (MLOps) architecture that enables continuous ML improvement through automated testing, version control, and human-gated deployment workflows. Delivered as a scalable blueprint, the solution is being trialled on the ad optimisation model as a proof of concept, with the potential to be applied across the client’s wider ML estate.
Industry
Media & publishing
Partner
Snowflake
Challenge
While the client’s ML models delivered value initially, scaling, reliably maintaining and improving them became increasingly complex. The in-house team had the technical capability but not the operational headroom to design a solution and faced issues that collectively slowed innovation, increased operational costs, exposed the business to risk and limited the company’s ability to respond to fast-changing business needs.
Lack of observability
No real-time visibility into model performance or data quality, meant the team couldn’t detect issues early and struggled to answer fundamental questions like “Is our model still working correctly?” or “Has our data changed significantly?”, creating uncertainty.
Infrastructure scalability constraints
On-premises virtual machines struggled under growing workloads, causing regular failures and downtime. The team required reliable, scalable hosting infrastructure that could provide them with autonomy over its deployment.
Manual deployment risks
Data scientists developed improved model versions but deploying them to production was high-risk in the absence of systematic testing or comparison frameworks. Each update felt like a leap of faith.
Inflexible batch processing
Scheduled batch jobs could not meet urgent business needs, such as real-time campaign optimisation or reacting to breaking news events.
No testing framework
Without automated quality assurance, silent data drift and undetected model degradation posed serious risks to business outcomes.
Solution
To address this, the client engaged CACI to design a MLOps (Machine Learning Operations) architectural blueprint – a structured framework of practices and tools to automate and streamline ML workflows – and support a proof-of-concept (POC).
Working closely with the in-house data science team, CACI mapped operational requirements, tested theories and validated approaches. The result: a robust MLOps architecture built on four core pillars:
- Observation – Four-tier monitoring for data quality, performance, drift, and infrastructure health. Threshold-based alerts linked to business KPIs trigger proactive responses like investigation, enhanced monitoring, or retraining.
- Reproducibility – Full version control across datasets, features, models, and configurations. Each model traceable to its training data and transformations, enabling fast troubleshooting and clear audit trails.
- Automation with oversight – CI/CD pipelines standardise testing, deployment, and model serving. Quality gates enforce performance thresholds, APIs enable real-time predictions, and monitoring informs retraining – while humans make the final call.
- Continuity – Challenger model versions run in parallel using shadow scoring, A/B testing, or seasonal rotation. A centralised serving layer manages selection, logging, and complexity, allowing better models to be adopted without disrupting stability.
Designed on the client’s Snowflake data platform, the blueprint leverages our strategic dataTech and cloud partnership. It powers data ingestion, feature engineering, versioning, model serving, and observability.
A central metadata store governs configurations and guardrails, with automated checks at every stage. Models are validated and served on-demand or scheduled, with outputs logged to downstream systems. Snowflake’s native monitoring tracks freshness, validity, and custom rules – establishing a pathway to scalable, governed automation.
Results
The client is now using the MLOps blueprint to implement the proof of concept on their ad-serving model, with the intention to scale the approach across their wider ML estate. Early feedback shows the in-house team confident the design will reduce manual effort, improve reliability, and accelerate innovation.
The blueprint provides a clear automation framework to move from reactive model maintenance to proactive, evidence-based improvement – allowing them to test and deploy improved models with faster-time to value, greater confidence and less risk.
Real-time model serving – previously out of reach due to infrastructure and process constraints – is now within reach. The challenger model framework enabling safe experimentation, while the metadata-driven design ensures flexibility as business needs evolve: all with improved auditability and compliance via full traceability.
Crucially, the architecture supports trust-building: alerts and retraining triggers are reviewed by humans before any automated action is taken. Over time, as confidence grows, the client can choose to enable full automation on their own terms.
The blueprint not just enables a powerful technical upgrade from a mostly manual ML implementation, but also the strategic and operational step change needed to move from: “Is our model working?” to “How can we make our models work even better?” A mindset shift laying the foundation for scalable, future-ready ML deployment can deliver business value over time.
*This case study describes a proof-of-concept architecture design and implementation support engagement. The client organisation is not identified to maintain confidentiality.
