ML Models That Run in Production, Not Just Notebooks

We build machine learning models that run in production — not just in notebooks. From demand forecasting to anomaly detection, ML that delivers measurable business value.

Pythonscikit-learnXGBoostTensorFlow

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Deka Technology engineers working on machine learning projects

30+

ML models in production

25%

Average accuracy improvement

99.5%

Model uptime SLA

< 100ms

Inference latency

 
THE CHALLENGE

AI projects that never leave the lab

Most enterprise AI initiatives start with enthusiasm and end in a Jupyter notebook. Data scientists build impressive prototypes, but the models never make it to production. The gap between a working notebook and a reliable production system is vast: data pipelines, model serving, monitoring, retraining, versioning and integration with business processes.

The enterprises that benefit from ML are not the ones with the best algorithms — they are the ones with proper engineering around their models. ML engineering is software engineering applied to models.

 
OUR APPROACH

Define & Validate

We identify high-value ML use cases, validate data availability and feasibility, and define success metrics with business stakeholders.

Build & Train

Feature engineering, model development and rigorous validation. We build MLOps pipelines for automated training, versioning and deployment.

Deploy & Monitor

Production deployment with monitoring, drift detection and automated retraining. Models that stay accurate as data evolves.

 
CAPABILITIES

What we deliver

Demand Forecasting

Time-series models for inventory planning, sales prediction and resource allocation. Handling seasonality, promotions and external factors for accurate forecasts.

Anomaly Detection

Real-time anomaly detection for fraud, equipment failure and process deviations. Unsupervised and semi-supervised approaches that adapt to changing patterns.

Customer Analytics

Segmentation, churn prediction, lifetime value estimation and next-best-action models. Turning customer data into retention and growth strategies.

NLP & Text Analytics

Document classification, sentiment analysis, named entity recognition and intelligent search. Processing unstructured text at scale for business insights.

MLOps & Model Serving

End-to-end ML pipelines: feature stores, experiment tracking, model registries, A/B testing and monitoring. Models deployed as scalable API endpoints.

AI pilots stuck in POC or models that never reach production?

Talk to an ML engineer who deploys predictive models at enterprise scale.

Discuss your project →

30+

ML models in production

25%

Average accuracy improvement

99.5%

Model uptime SLA

< 100ms

Inference latency

 
PROJECT SPOTLIGHT

MACHINE LEARNING

FMCG Enterprise

CHALLENGE

Manual demand planning causing 15% overstock and frequent stockouts across 200+ SKUs. Planners relied on spreadsheets and intuition.

APPROACH

We built an ensemble forecasting model incorporating sales history, seasonality, promotions and external factors. Deployed as an API integrated with the planning system.

RESULT

Forecast accuracy improved by 25%. Overstock reduced by 40%. Planners now review and adjust AI suggestions rather than building forecasts from scratch.

25% better accuracy40% less overstock200+ SKUs

 
USE CASES

Where we apply it

Improve demand forecast accuracy by 25% or more
Identify at-risk customers before they churn
Flag fraudulent transactions in real time
Prevent equipment failures before they halt production
Route documents to the right team automatically

 
TECHNOLOGY

Technologies

Pythonscikit-learnXGBoostTensorFlowPyTorchMLflowAzure MLDatabricksFastAPIDocker

TECHNOLOGY PARTNERS

Microsoft

Cloudera

FAQ

Common questions about machine learning

Do we need a data science team to work with you? +

No. We provide end-to-end ML engineering — from data preparation to production deployment. If you have data scientists, we complement them with the engineering they need to get models into production.

How do you ensure models stay accurate over time? +

We implement drift detection monitoring that tracks data distribution and model performance continuously. When performance degrades, automated retraining pipelines kick in. Every model has a scheduled retraining cadence.

What is the typical ROI timeline for an ML project? +

A focused ML project (e.g. demand forecasting for a product category) delivers measurable results in 8-12 weeks. ROI depends on the use case, but we typically see 20-40% improvement in the target metric within the first quarter.

Can you integrate ML models with our existing systems? +

Yes. We deploy models as REST API endpoints that integrate with any system. For real-time use cases, we optimize for low latency. For batch use cases, we schedule predictions and push results directly to your databases or BI tools.

Discuss your specific setup →