AI/ML Development with Secure, Automated, and Scalable Solutions

AI/ML development is the process of building predictive or decision-support systems using enterprise data. It includes data readiness, model evaluation, deployment, integration, and monitoring so the solution performs reliably in production.

You need a clear target outcome, historical examples, and relevant input features. If data is limited, a pilot can start with narrow scope and a defined plan for improving data quality and labeling.

Metrics depend on the use case and can include precision/recall, F1, AUC, MAE/MAPE, false positive rates, and latency. We also track business KPIs such as time saved, reduced errors, or improved conversion.

Deployment is typically done as APIs, batch pipelines, or edge components. The choice depends on latency needs, data availability, and how the prediction must fit into business workflows.

Yes. Regulated use cases typically require stronger audit logs, access controls, documentation, and escalation workflows for high-risk decisions.

AI/ML typically focuses on prediction and scoring such as forecasting, classification, and anomaly detection. GenAI focuses on generating content or responses; enterprise GenAI often needs grounding and governance to remain accurate.

Model choice depends on data volume, accuracy requirements, latency needs, interpretability, and operational constraints. The goal is the simplest model that meets KPI targets and is maintainable over time.

Model drift happens when real-world data changes and model performance declines. Drift is managed through monitoring, periodic evaluation, retraining triggers, and controlled releases.

Yes. Integrations often push scores, predictions, or alerts into enterprise tools so teams can act on them inside existing workflows.

Timeline depends on data readiness and integration scope. A focused pilot for one workflow can be delivered faster than a full rollout that includes governance, monitoring, and cross-team adoption.