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9 data governance strategies that will unlock the potential of your business data

IBM Journey to AI blog

Everything is data—digital messages, emails, customer information, contracts, presentations, sensor data—virtually anything humans interact with can be converted into data, analyzed for insights or transformed into a product. Managing this level of oversight requires adept handling of large volumes of data.

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Multimodal Large Language Models

The MLOps Blog

TL;DR Multimodal Large Language Models (MLLMs) process data from different modalities like text, audio, image, and video. Compared to text-only models, MLLMs achieve richer contextual understanding and can integrate information across modalities, unlocking new areas of application. Why do we need multimodal LLMs?

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MLOps Landscape in 2023: Top Tools and Platforms

The MLOps Blog

Can you debug system information? Data quality control: Robust dataset labeling and annotation tools incorporate quality control mechanisms such as inter-annotator agreement analysis, review workflows, and data validation checks to ensure the accuracy and reliability of annotations. Can you compare images?

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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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Centralize model governance with SageMaker Model Registry Resource Access Manager sharing

AWS Machine Learning Blog

It includes processes for monitoring model performance, managing risks, ensuring data quality, and maintaining transparency and accountability throughout the model’s lifecycle. It’s a binary classification problem where the goal is to predict whether a customer is a credit risk. region_name ram_client = boto3.client('ram')

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How Vericast optimized feature engineering using Amazon SageMaker Processing

AWS Machine Learning Blog

Each business problem is different, each dataset is different, data volumes vary wildly from client to client, and data quality and often cardinality of a certain column (in the case of structured data) might play a significant role in the complexity of the feature engineering process.

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Accelerate time to business insights with the Amazon SageMaker Data Wrangler direct connection to Snowflake

AWS Machine Learning Blog

Data scientists should have the following prerequisites Access to Amazon SageMaker , an instance of Amazon SageMaker Studio , and a user for SageMaker Studio. For more information about prerequisites, see Get Started with Data Wrangler. You can use the report to help you clean and process your data. Choose Create.