AI/ML (Amazon SageMaker, Rekognition, Textract, Comprehend)
Machine learning initiatives often stall due to infrastructure complexity, data wrangling, and lack of integrated ML tools. Building custom ML pipelines on-prem can be expensive and time-consuming.
Complex Model Lifecycle:
Data labeling, model training, hyperparameter tuning, deployment, and monitoring.
Unstructured Data:
Need for text extraction, image recognition, sentiment analysis, or language translation.
Limited Expertise:
Data scientists need a faster, more accessible environment for model experimentation.
Relevant AWS Solutions
Amazon SageMaker: End-to-end machine learning platform for model building, training, tuning, and deployment.
Amazon Rekognition: Deep learning-based image and video analysis.
Amazon Textract: OCR and text extraction from scanned documents.
Amazon Comprehend: NLP tasks such as sentiment, entity recognition, and language detection.
Detailed Use Case
A healthcare provider aims to build predictive models for patient readmission risk, while also scanning and extracting data from archived patient records for advanced analytics.
Data Preparation
Dhanyaayai leverages SageMaker Data Wrangler or custom Glue ETL scripts to clean and normalize patient data.
Model Development
Data scientists train a readmission risk model in SageMaker with built-in algorithms or custom Docker containers.
Document Digitization
Amazon Textract processes legacy records, extracting text fields for advanced NLP.
Entity Recognition
Amazon Comprehend identifies key clinical terms from extracted text, fueling deeper analytics or feed into readmission model features.
Why Choose Dhanyaayai
Key Benefits
Faster ML Experiments
SageMaker’s integrated environment accelerates data prep, training, and deployment.
Scalable Model Serving
Models auto-scale based on real-time demand.
Rich Unstructured Data
Tools like Textract, Comprehend, Rekognition open new data insights from images, documents, or text.
Improved Patient Outcomes
Predictive analytics fosters earlier interventions, reduced readmissions, and cost savings.
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