Auto-complete DevOps Metadata 
AWS Machine Learning Blog
MARCH 10, 2025
Visit octus.com to learn how we deliver rigorously verified intelligence at speed and create a complete picture for professionals across the entire credit lifecycle. The use of multiple external cloud providers complicated DevOps, support, and budgeting. Follow Octus on LinkedIn and X.
AWS Machine Learning Blog
SEPTEMBER 4, 2024
DevOps engineers often use Kubernetes to manage and scale ML applications, but before an ML model is available, it must be trained and evaluated and, if the quality of the obtained model is satisfactory, uploaded to a model registry. SageMaker simplifies the process of managing dependencies, container images, auto scaling, and monitoring.
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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. Came to ML from software.
The MLOps Blog
JUNE 27, 2023
When thinking about a tool for metadata storage and management, you should consider: General business-related items : Pricing model, security, and support. Flexibility, speed, and accessibility : can you customize the metadata structure? Can you see the complete model lineage with data/models/experiments used downstream?
AWS Machine Learning Blog
JUNE 11, 2024
Launch the instance using Neuron DLAMI Complete the following steps: On the Amazon EC2 console, choose your desired AWS Region and choose Launch Instance. You can update your Auto Scaling groups to use new AMI IDs without needing to create new launch templates or new versions of launch templates each time an AMI ID changes.
AWS Machine Learning Blog
JULY 16, 2024
It manages the availability and scalability of the Kubernetes control plane, and it provides compute node auto scaling and lifecycle management support to help you run highly available container applications. Training Now that our data preparation is complete, we’re ready to train our model with the created dataset.
AWS Machine Learning Blog
JANUARY 9, 2025
This process is like assembling a jigsaw puzzle to form a complete picture of the malwares capabilities and intentions, with pieces constantly changing shape. The meticulous nature of this process, combined with the continuous need for scaling, has subsequently led to the development of the auto-evaluation capability.
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