Auto-complete ML Software Development 
AWS Machine Learning Blog
NOVEMBER 26, 2024
8B model With the setup complete, you can now deploy the model using a Kubernetes deployment. Complete the following steps: Check the deployment status: kubectl get deployments This will show you the desired, current, and up-to-date number of replicas. AWS_REGION.amazonaws.com/${ECR_REPO_NAME}:latest Deploy the Meta Llama 3.1-8B
AWS Machine Learning Blog
NOVEMBER 14, 2024
We recently announced the general availability of cross-account sharing of Amazon SageMaker Model Registry using AWS Resource Access Manager (AWS RAM) , making it easier to securely share and discover machine learning (ML) models across your AWS accounts.
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
AWS Machine Learning Blog
MARCH 25, 2025
As organizations increasingly deploy foundation models (FMs) and other machine learning (ML) models to production, they face challenges related to resource utilization, cost-efficiency, and maintaining high availability during updates. Now another two free GPU slots are available.
AWS Machine Learning Blog
JUNE 7, 2023
You can now retrain machine learning (ML) models and automate batch prediction workflows with updated datasets in Amazon SageMaker Canvas , thereby making it easier to constantly learn and improve the model performance and drive efficiency. An ML model’s effectiveness depends on the quality and relevance of the data it’s trained on.
AWS Machine Learning Blog
NOVEMBER 30, 2023
Amazon SageMaker is a fully managed service that enables developers and data scientists to quickly and easily build, train, and deploy machine learning (ML) models at scale. For more information, refer to Package and deploy classical ML and LLMs easily with Amazon SageMaker, part 1: PySDK Improvements.
Marktechpost
NOVEMBER 7, 2023
Software developers, however, are more interested in creating libraries that may be used to solve whole problem domains than they are in finishing the current work at hand. Figure 1: The LILO learning loop overview. (Al) Al) Using a dual-system search methodology, LILO creates programs from task descriptions written in plain language.
AWS Machine Learning Blog
FEBRUARY 19, 2025
Rather than using probabilistic approaches such as traditional machine learning (ML), Automated Reasoning tools rely on mathematical logic to definitively verify compliance with policies and provide certainty (under given assumptions) about what a system will or wont do. However, its important to understand its limitations.
Expert insights. Personalized for you.
94 
Let's personalize your content