Evaluation of the client’s existing PV environment with respect to intelligent automation, knowledge processes, data management practices and technical capacities.
Tailored strategy and roadmap for more automated and AI-driven/-assisted PV processes including data acquisition (monitoring, intake), case processing or signal detection.
Transition by initializing and driving relevant Proofs of Concepts, implementation projects and corporate change initiatives
Monitoring of relevant data sources - literature, social media, mail inboxes etc.
Exploratory Data Analysis, data/text preparation and pre-processing
Entity recognition and pre-coding (MedDRA PTs, products), AERs and duplicate detection/classification, seriousness suggestion, case summarization, results evaluation
Simulations, visualizations, modeling temporal relations and causality assessment
Augmented AI patterns (AI vs. human-assisted) and related explainability and validation compliance
Tuning models (hyper-parameters) and architectures for regression, classification, deep learning (NLP) and others
Productionalizing PV IT operations towards scalable architectures, faster turnaround (CI/CD), continuous improvements and Machine Learning PV IT operations (MLOps covering train/test/validation/production pipelines, etc.)
Active awareness of relevant community activities - open-source models and libraries, no-code SaaS tools, regulatory AI PV guidelines and actions, ethical and green AI, and more.
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