AWS Machine Learning Blog

JANUARY 5, 2024

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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The MLOps Blog

JUNE 27, 2023

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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AWS Machine Learning Blog

MARCH 1, 2023

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.

Automation 101

Automation ETL Data Drift ML 101

AWS Machine Learning Blog

NOVEMBER 9, 2023

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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ODSC - Open Data Science

OCTOBER 11, 2023

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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AWS Machine Learning Blog

SEPTEMBER 21, 2023

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.

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Topbots

JUNE 29, 2023

Figure 1: Representation of the Text2SQL flow As our world is getting more global and dynamic, businesses are more and more dependent on data for making informed, objective and timely decisions. However, as of now, unleashing the full potential of organisational data is often a privilege of a handful of data scientists and analysts.

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Algorithm Auto-complete Data Scientist Auto-classification 52