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The Importance of Data Drift Detection that Data Scientists Do Not Know

Analytics Vidhya

This article was published as a part of the Data Science Blogathon What is Model Monitoring and why is it required? Machine learning creates static models from historical data. There might be changes in the data distribution in production, thus causing […].

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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. You have to understand data, how to extract value from them and how to monitor model performances.

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Data Science Tutorial using Python

Viso.ai

Data science is a multidisciplinary field that relies on scientific methods, statistics, and Artificial Intelligence (AI) algorithms to extract knowledgable and meaningful insights from data. At its core, data science is all about discovering useful patterns in data and presenting them to tell a story or make informed decisions.

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Top MLOps Tools Guide: Weights & Biases, Comet and More

Unite.AI

This is not ideal because data distribution is prone to change in the real world which results in degradation in the model’s predictive power, this is what you call data drift. There is only one way to identify the data drift, by continuously monitoring your models in production. What is Weights & Biases?

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How are AI Projects Different

Towards AI

Michael Dziedzic on Unsplash I am often asked by prospective clients to explain the artificial intelligence (AI) software process, and I have recently been asked by managers with extensive software development and data science experience who wanted to implement MLOps. Join thousands of data leaders on the AI newsletter.

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MLOps Landscape in 2023: Top Tools and Platforms

The MLOps Blog

For example, if your team is proficient in Python and R, you may want an MLOps tool that supports open data formats like Parquet, JSON, CSV, etc., DataRobot MLOps facilitates collaboration between data scientists, data engineers, and IT operations, ensuring smooth integration of models into the production environment.

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Create SageMaker Pipelines for training, consuming and monitoring your batch use cases

AWS Machine Learning Blog

If the model performs acceptably according to the evaluation criteria, the pipeline continues with a step to baseline the data using a built-in SageMaker Pipelines step. For the data drift Model Monitor type, the baselining step uses a SageMaker managed container image to generate statistics and constraints based on your training data.