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Incorporated EvidentlyAI to detect data drift in ML pipelines. Leveraged Python scripts to analyze dataset changes, generate HTML reports, and send email alerts, ensuring proactive model monitoring. Enhanced project reliability and data integrity.
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This repository showcases PySpark integration with OCI Data Flow for efficient data processing. Harness the power of Apache Spark seamlessly on Oracle Cloud Infrastructure.
Exploring an agriculture dataset involves loading data, analyzing production, and visualizing patterns. K-means clustering identifies similar regions. Insights from this analysis inform decisions on sustainable farming and resource allocation.
Skin disease detection using ResNet-50 is an advanced and powerful application of deep learning in the field of dermatology and medical image analysis. ResNet-50, a deep convolutional neural network architecture, serves as the backbone for this task.
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Saving models in Oracle using ADS involves preparing model artifacts with metadata, dependencies, and sample data, then storing them in the Model Catalog. This ensures reproducibility and accessibility for deployment and further use within the Oracle ADS environment.
Learn to deploy machine learning models effortlessly with SageMaker! From data preprocessing to model training and deployment, SageMaker simplifies the entire ML workflow, offering scalability, flexibility, and ease of use. Dive into efficient model building with just a fraction of the effort.
