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Airbnb Researchers Develop Chronon: A Framework for Developing Production-Grade Features for Machine Learning Models

Marktechpost

In the ever-evolving landscape of machine learning, feature management has emerged as a key pain point for ML Engineers at Airbnb. Airbnb recognized the need for a solution that could streamline feature data management, provide real-time updates, and ensure consistency between training and production environments.

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How Earth.com and Provectus implemented their MLOps Infrastructure with Amazon SageMaker

AWS Machine Learning Blog

Earth.com didn’t have an in-house ML engineering team, which made it hard to add new datasets featuring new species, release and improve new models, and scale their disjointed ML system. We initiated a series of enhancements to deliver managed MLOps platform and augment ML engineering.

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Orchestrate Ray-based machine learning workflows using Amazon SageMaker

AWS Machine Learning Blog

Data scientists have to address challenges like data partitioning, load balancing, fault tolerance, and scalability. ML engineers must handle parallelization, scheduling, faults, and retries manually, requiring complex infrastructure code. Ingest the prepared data into the feature group by using the Boto3 SDK.

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How to Build Machine Learning Systems With a Feature Store

The MLOps Blog

We’ll see how this architecture applies to different classes of ML systems, discuss MLOps and testing aspects, and look at some example implementations. Understanding machine learning pipelines Machine learning (ML) pipelines are a key component of ML systems. But what is an ML pipeline? All of them are written in Python.

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Governing the ML lifecycle at scale, Part 4: Scaling MLOps with security and governance controls

AWS Machine Learning Blog

Usually, there is one lead data scientist for a data science group in a business unit, such as marketing. Data scientists Perform data analysis, model development, model evaluation, and registering the models in a model registry. ML engineers Develop model deployment pipelines and control the model deployment processes.

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Up Your Machine Learning Game With These ODSC East 2024 Sessions

ODSC - Open Data Science

By the end of this session, you’ll have a practical blueprint to efficiently harness feature stores within ML workflows. Using Graphs for Large Feature Engineering Pipelines Wes Madrigal | ML Engineer | Mad Consulting Feature engineering from raw entity-level data is complex, but there are ways to reduce that complexity.

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MLOps Landscape in 2023: Top Tools and Platforms

The MLOps Blog

Knowledge and skills in the organization Evaluate the level of expertise and experience of your ML team and choose a tool that matches their skill set and learning curve. 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.,