The MLOps Blog
JANUARY 23, 2023
Lived through the DevOps revolution. Founded neptune.ai , a modular MLOps component for ML metadata store , aka “experiment tracker + model registry”. If you’d like a TLDR, here it is: MLOps is an extension of DevOps. There will be only one type of ML metadata store (model-first), not three. Ok, let me explain.
AWS Machine Learning Blog
DECEMBER 19, 2023
Machine learning operations (MLOps) applies DevOps principles to ML systems. Just like DevOps combines development and operations for software engineering, MLOps combines ML engineering and IT operations. Conclusion In summary, MLOps is critical for any organization that aims to deploy ML models in production systems at scale.
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The MLOps Blog
APRIL 17, 2023
Building a tool for managing experiments can help your data scientists; 1 Keep track of experiments across different projects, 2 Save experiment-related metadata, 3 Reproduce and compare results over time, 4 Share results with teammates, 5 Or push experiment outputs to downstream systems.
The MLOps Blog
JUNE 27, 2023
This includes features for model explainability, fairness assessment, privacy preservation, and compliance tracking. When thinking about a tool for metadata storage and management, you should consider: General business-related items : Pricing model, security, and support. Is it fast and reliable enough for your workflow?
AWS Machine Learning Blog
JUNE 27, 2023
That is where Provectus , an AWS Premier Consulting Partner with competencies in Machine Learning, Data & Analytics, and DevOps, stepped in. They needed a cloud platform and a strategic partner with proven expertise in delivering production-ready AI/ML solutions, to quickly bring EarthSnap to the market.
AWS Machine Learning Blog
OCTOBER 20, 2023
The functional architecture with different capabilities is implemented using a number of AWS services, including AWS Organizations , SageMaker, AWS DevOps services, and a data lake. The architecture maps the different capabilities of the ML platform to AWS accounts.
The MLOps Blog
APRIL 5, 2023
Source Model packaging is a process that involves packaging model artifacts, dependencies, configuration files, and metadata into a single format for effortless distribution, installation, and reuse. These teams may include but are not limited to data scientists, software developers, machine learning engineers, and DevOps engineers.
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