Marktechpost

AUGUST 8, 2023

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.

Machine Learning 98

Machine Learning ML Engineer Data Ingestion ML 98

AWS Machine Learning Blog

JUNE 27, 2023

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.

DevOps 115

DevOps ML Machine Learning ML Engineer 115

AWS Machine Learning Blog

SEPTEMBER 18, 2023

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.

Machine Learning 130

Machine Learning ML Python Auto-complete 130

The MLOps Blog

JANUARY 26, 2024

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.

Machine Learning 72

Machine Learning Metadata ML Python 72

AWS Machine Learning Blog

FEBRUARY 7, 2025

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.

ML 63

ML Data Scientist ML Engineer Data Science 63

ODSC - Open Data Science

FEBRUARY 22, 2024

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.

Machine Learning 52

Machine Learning Data Science Python ML 52

The MLOps Blog

JUNE 27, 2023

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.,

Machine Learning 134

Machine Learning Metadata Data Scientist Data Quality 134