What the service can cover
- Data science services and use-case feasibility
- Predictive analytics and machine learning services
- Generative AI, AI/ML solutions and LLM integration
- Evaluation, monitoring and responsible deployment
At BM Infotrade, we are leaders in the data science field, offering solutions that help organizations unlock the full potential of their data.
Data Science and AI Solutions help organisations evaluating predictive analytics, machine learning, generative AI and LLM integration. The service can include data science services and use-case feasibility, predictive analytics and machine learning services, generative AI, AI/ML solutions and LLM integration and evaluation, monitoring and responsible deployment and is designed to deliver an AI or analytics capability with a defined business use case, evaluation method and accountable operating process.
BM Infotrade follows a requirement-led process: define the decision, validate data, establish a baseline, develop and evaluate models, deploy controls and monitor performance. Architecture, timelines and commercials depend on documented workload, user, risk, location and compliance requirements. Organisations should compare providers on data fitness, evaluation metrics, privacy, explainability, integration effort, monitoring and human oversight rather than a generic product bundle.
Define the decision, validate data, establish a baseline, develop and evaluate models, deploy controls and monitor performance.
Data fitness, evaluation metrics, privacy, explainability, integration effort, monitoring and human oversight.
Data Science and AI Solutions can include data science services and use-case feasibility, predictive analytics and machine learning services, generative AI, AI/ML solutions and LLM integration and evaluation, monitoring and responsible deployment. The final scope should follow the organisation’s workloads, risks, users, compliance obligations and operating model rather than a fixed product bundle.
BM Infotrade uses a staged approach: define the decision, validate data, establish a baseline, develop and evaluate models, deploy controls and monitor performance. Scope, responsibilities, assumptions and acceptance criteria should be agreed before implementation.
Evaluate providers on data fitness, evaluation metrics, privacy, explainability, integration effort, monitoring and human oversight. Ask for a requirement-specific architecture, implementation plan, named responsibilities and a support model before selecting a solution.
Standards and product documentation: Microsoft Fabric documentation NIST AI Risk Management Framework Digital Personal Data Protection Act, 2023