Remove Data Drift Remove Data Scientist Remove Explainability
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Data Scientists in the Age of AI Agents and AutoML

Towards AI

Uncomfortable reality: In the era of large language models (LLMs) and AutoML, traditional skills like Python scripting, SQL, and building predictive models are no longer enough for data scientist to remain competitive in the market. Coding skills remain important, but the real value of data scientists today is shifting.

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AI Transparency and the Need for Open-Source Models

Unite.AI

along with the EU AI Act , support various principles such as accuracy, safety, non-discrimination, security, transparency, accountability, explainability, interpretability, and data privacy. Human element: Data scientists are vulnerable to perpetuating their own biases into models. Moreover, both the EU and the U.S.

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Modernizing data science lifecycle management with AWS and Wipro

AWS Machine Learning Blog

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. This has helped the data scientist team to create and test pipelines at a much faster pace.

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How Axfood enables accelerated machine learning throughout the organization using Amazon SageMaker

AWS Machine Learning Blog

Each product translates into an AWS CloudFormation template, which is deployed when a data scientist creates a new SageMaker project with our MLOps blueprint as the foundation. These are essential for monitoring data and model quality, as well as feature attributions. Alerts are raised whenever anomalies are detected.

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Monitoring Machine Learning Models in Production

Heartbeat

The primary goal of model monitoring is to ensure that the model remains effective and reliable in making predictions or decisions, even as the data or environment in which it operates evolves. Data drift refers to a change in the input data distribution that the model receives. The MLOps difference?

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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. The platform gives you a unified set of tools for enterprise‑grade solutions for everything you need to do with data, including building, deploying, sharing, and maintaining solutions that have to do with data.

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Machine Learning Project Checklist

DataRobot Blog

Machine learning and AI empower organizations to analyze data, discover insights, and drive decision making from troves of data. Data scientists need to understand the business problem and the project scope to assess feasibility, set expectations, define metrics, and design project blueprints. Monitor and observe results.