Model Deployment Jobs

deploy a dual-model pipeline on AWS. Scope of Work Dual-Model Deployment: Deploy the Pillar-0 (Atlas architecture) for multi-finding classification across Chest, Abdomen, and Brain. Integrate Sybil-1.5 for specialized future lung cancer risk prediction (1–6 year horizon). Inference Pipeline & Report Generation: deploy pipeline that takes zipped DICOM files, performs 3D volumetric reconstruction, and runs concurrent inference. script (attached). This script shows how to read the (Zip file), stack of the medical pictures (DICOMs) into a 3D block, and run both the Pillar-0 model (for findings) and Sybil-1.5 (for cancer risk) -This tells AWS which specialized tools to install to read 3D medical images AWS Cloud Architecture: Host models on AWS SageMaker (GPU instances like m...

Project Brief I want to build Version 2 of my existing mobile Web3 wallet app. V2 should include a UI revamp, additional features, and end-to-end deployment support. Current status: Existing app is live and codebase is available. Backend services/APIs are already deployed. Goal is to create a cleaner, more modern V2 without disrupting current production users. Tech stack: React Native with Expo. Expo Router. NativeWind/Tailwind. TanStack Query + Context. Existing wallet/transaction integrations. What I want in Version 2: Full UI redesign across key flows (modern, consistent, polished). New features I will provide in a detailed list. Better UX for onboarding, wallet actions, and transaction flows. Refactor frontend structure for maintainability and scalability. Keep backend compatibilit...

I have a clean, structured numerical dataset and need a supervised machine-learning model built, validated, and handed over with clear documentation. The goal is to predict future outcomes from past observations, so model accuracy and interpretability both matter. Here’s what I need from you: • A brief data-exploration notebook that highlights key correlations, missing-value handling, and basic visuals. • Feature engineering tailored to the data’s domain (scaling, encoding, derived metrics, etc.). • At least two supervised algorithms (for example, Gradient Boosting and Random Forest in scikit-learn, or an XGBoost/TensorFlow alternative) trained, cross-validated, and benchmarked. • A concise performance comparison using appropriate regression/classif...

I have a clean, structured numerical dataset and need a supervised machine-learning model built, validated, and handed over with clear documentation. The goal is to predict future outcomes from past observations, so model accuracy and interpretability both matter. Here’s what I need from you: • A brief data-exploration notebook that highlights key correlations, missing-value handling, and basic visuals. • Feature engineering tailored to the data’s domain (scaling, encoding, derived metrics, etc.). • At least two supervised algorithms (for example, Gradient Boosting and Random Forest in scikit-learn, or an XGBoost/TensorFlow alternative) trained, cross-validated, and benchmarked. • A concise performance comparison using appropriate regression/classif...