Siloed Data Science & Engineering
Data scientists handed off models to engineers manually, causing delays and misconfigurations.
Streamlining Machine Learning Operations
Turning machine learning prototypes into production-ready, business-impacting solutions remains one of the biggest challenges for modern enterprises. A leading retail analytics company approached Spundan to build a robust MLOps framework to operationalize its data science models and automate the entire ML lifecycle — from training to deployment and continuous monitoring.
The client's data science teams built complex recommendation engines and demand forecasting models to optimize inventory, pricing, and promotions. However, deploying models into production was slow, error-prone, and lacked version control — creating inconsistencies and limiting the business impact of their AI initiatives.
Before implementing MLOps, the company faced multiple pain points:
Siloed Data Science & Engineering
Data scientists handed off models to engineers manually, causing delays and misconfigurations.
No Model Versioning
Models were overwritten or deployed inconsistently, making rollbacks difficult when predictions failed.
Manual Deployment & Scaling
Model deployment required manual packaging and lacked automation to scale across multiple environments.
No Monitoring or Feedback Loops
There was no reliable way to track model performance drift or automatically retrain models on fresh data.
Spundan's MLOps specialists designed and deployed a modern, scalable MLOps pipeline aligned with cloud-native best practices and the client's existing cloud infrastructure on AWS and Azure.
Key solution components included:Automated Pipelines
Built CI/CD pipelines for ML workflows using Kubeflow Pipelines and Jenkins, automating data ingestion, model training, validation, packaging, and deployment.
Model Versioning & Registry
Integrated MLflow to version control all models, store metadata, and enable reproducible experiments.
Containerized Model Serving
Packaged models into Docker containers and deployed them as microservices on Kubernetes for easy scaling and consistent environments.
Monitoring & Drift Detection
Implemented automated monitoring tools to track prediction accuracy, detect data or concept drift, and trigger retraining workflows when performance dropped.
Self-Service ML Platform
Created reusable templates and notebooks, enabling data scientists to run experiments, push models to staging, and deploy approved models with minimal ops overhead.
Governance & Compliance
Added audit trails and automated logs to ensure the entire ML lifecycle was trackable and compliant with data privacy regulations.
The transformation was delivered in four phases over six months:
Assessment & Architecture
Mapped out current workflows, defined target MLOps architecture, and aligned with data governance requirements.
Prototype Pipelines
Built pilot pipelines for high-impact models to validate the automation framework.
Full Rollout
Expanded MLOps pipelines to cover all key ML models in the client's ecosystem.
Enablement & Handover
Provided training, reusable templates, and documentation for seamless adoption by the data science and engineering teams.
"With Spundan's MLOps framework, we went from months-long manual model rollouts to automated pipelines that ship, monitor, and retrain our models continuously — driving real business impact from AI.”
— Head of Data Science, Retail Analytics Client
80% faster model deployment, reducing rollout time from weeks to days.
Automated retraining pipelines, ensuring models remain accurate and relevant as data evolves.
Consistent, reproducible experiments with robust model versioning and metadata tracking.
Reduced operational burden, freeing data scientists to focus on building better models instead of deployment logistics.
Standardization is Key
Reusable templates and pipelines reduced manual errors and ensured consistency across teams.
Monitoring Closes the Loop
Continuous monitoring and drift detection helped keep models accurate and trustworthy.
Collaboration is Critical
Clear workflows bridged the gap between data science and operations, delivering real production value.
Through its MLOps transformation, this retail analytics company turned AI prototypes into reliable, scalable production services — maximizing ROI on its data science investments and unlocking faster business value.
Ready to operationalize your machine learning workflows? Talk to Spundan's MLOps experts today
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