● Machine Learning
Machine learning turns your historical data into predictive power. 971 Technologies starts with clear business goals and KPIs, then builds a robust pipeline: data quality checks, feature engineering, and model selection with rigorous validation to ensure accuracy and reliability.
We deliver production‑ready ML with explainability and governance—SHAP/LIME for insights, bias and drift monitoring, and CI/CD for models. Deployments fit your stack: batch scoring, real‑time APIs (REST/gRPC), or edge/embedded inference. Post‑launch, we monitor performance and costs, retrain models on fresh data, and iterate based on A/B tests and real‑world results.
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What ML problems do you solve?
971 Technologies builds models for forecasting, churn/propensity, anomaly and fraud detection, recommendations, NLP/document processing, and predictive maintenance—across web, mobile, and enterprise systems.
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How much data do we need?
More helps, but it’s not mandatory. We start with what you have, use transfer learning and augmentation where useful, and prove value with a pilot before scaling.
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How do you ensure accuracy and explainability?
We use robust validation (train/val/test splits, cross‑validation), track offline and online metrics, and provide explainability with SHAP/LIME plus clear dashboards for stakeholders.
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Where do models run—in cloud or on‑prem?
Both. We deploy on AWS/Azure/GCP (SageMaker, Vertex AI, Azure ML) or on‑prem/k8s, exposing batch jobs or low‑latency APIs. Edge and on‑device options are available for select use cases.
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How long to reach production?
Typical MVPs take 3–6 weeks depending on scope and integrations. 971 Technologies also provides ongoing monitoring, retraining, and MLOps support to keep models accurate over time.
