Remove Auto-classification Remove Data Quality Remove Explainability
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MLOps Landscape in 2023: Top Tools and Platforms

The MLOps Blog

This includes features for model explainability, fairness assessment, privacy preservation, and compliance tracking. Amazon SageMaker Ground Truth SageMaker Ground Truth is a fully managed data labeling service designed to help you efficiently label and annotate your training data with high-quality annotations.

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Multimodal Large Language Models

The MLOps Blog

An output could be, e.g., a text, a classification (like “dog” for an image), or an image. It can perform visual dialogue, visual explanation, visual question answering, image captioning, math equations, OCR, and zero-shot image classification with and without descriptions. Basic structure of a multimodal LLM.

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Prioritizing employee well-being: An innovative approach with generative AI and Amazon SageMaker Canvas

AWS Machine Learning Blog

In a single visual interface, you can complete each step of a data preparation workflow: data selection, cleansing, exploration, visualization, and processing. Custom Spark commands can also expand the over 300 built-in data transformations. Other analyses are also available to help you visualize and understand your data.

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Is your model good? A deep dive into Amazon SageMaker Canvas advanced metrics

AWS Machine Learning Blog

It also enables you to evaluate the models using advanced metrics as if you were a data scientist. In this post, we show how a business analyst can evaluate and understand a classification churn model created with SageMaker Canvas using the Advanced metrics tab.

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How Memorial Sloan Kettering Cancer Center (MSKCC) used Snorkel Flow to scale clinical trial screening

Snorkel AI

Scaling clinical trial screening with document classification Memorial Sloan Kettering Cancer Center, the world’s oldest and largest private cancer center, provides care to increase the quality of life of more than 150,000 cancer patients annually. Watch this and many other sessions on-demand at future.snorkel.ai.

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Top 5 Challenges faced by Data Scientists

Pickl AI

Furthermore, it ensures that data is consistent while effectively increasing the readability of the data’s algorithm. Data Cleaning is an essential part of the Data Pre-processing task, which improves the data quality, allowing efficient decision-making.

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Building and Deploying CV Models: Lessons Learned From Computer Vision Engineer

The MLOps Blog

Regularization techniques: experiment with weight decay, dropout, and data augmentation to improve model generalization. Managing data quality and quantity : managing data quality and quantity is crucial for training reliable CV models. Libraries like imgaug , albumentations , and torchvision.