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Building Generative AI and ML solutions faster with AI apps from AWS partners using Amazon SageMaker

AWS Machine Learning Blog

SageMaker AI makes sure that sensitive data stays completely within each customer’s SageMaker environment and will never be shared with a third party. It also empowers data scientists and ML engineers to do more with their models by collaborating seamlessly with their colleagues in data and analytics teams.

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Modernizing data science lifecycle management with AWS and Wipro

AWS Machine Learning Blog

The suite of services can be used to support the complete model lifecycle including monitoring and retraining ML models. Collaboration – Data scientists each worked on their own local Jupyter notebooks to create and train ML models. They lacked an effective method for sharing and collaborating with other data scientists.

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MLOps Landscape in 2023: Top Tools and Platforms

The MLOps Blog

Some popular end-to-end MLOps platforms in 2023 Amazon SageMaker Amazon SageMaker provides a unified interface for data preprocessing, model training, and experimentation, allowing data scientists to collaborate and share code easily. It provides a high-level API that makes it easy to define and execute data science workflows.

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How Kakao Games automates lifetime value prediction from game data using Amazon SageMaker and AWS Glue

AWS Machine Learning Blog

Challenges In this section, we discuss challenges around various data sources, data drift caused by internal or external events, and solution reusability. For example, Amazon Forecast supports related time series data like weather, prices, economic indicators, or promotions to reflect internal and external related events.

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Promote pipelines in a multi-environment setup using Amazon SageMaker Model Registry, HashiCorp Terraform, GitHub, and Jenkins CI/CD

AWS Machine Learning Blog

Building out a machine learning operations (MLOps) platform in the rapidly evolving landscape of artificial intelligence (AI) and machine learning (ML) for organizations is essential for seamlessly bridging the gap between data science experimentation and deployment while meeting the requirements around model performance, security, and compliance.

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How to Practice Data-Centric AI and Have AI Improve its Own Dataset

ODSC - Open Data Science

Machine learning models are only as good as the data they are trained on. Even with the most advanced neural network architectures, if the training data is flawed, the model will suffer. Data issues like label errors, outliers, duplicates, data drift, and low-quality examples significantly hamper model performance.

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How United Airlines built a cost-efficient Optical Character Recognition active learning pipeline

AWS Machine Learning Blog

This workflow will be foundational to our unstructured data-based machine learning applications as it will enable us to minimize human labeling effort, deliver strong model performance quickly, and adapt to data drift.” – Jon Nelson, Senior Manager of Data Science and Machine Learning at United Airlines.