AWS Machine Learning Blog
JANUARY 5, 2024
The AWS portfolio of ML services includes a robust set of services that you can use to accelerate the development, training, and deployment of machine learning applications. The suite of services can be used to support the complete model lifecycle including monitoring and retraining ML models.
Heartbeat
SEPTEMBER 6, 2023
Comet Comet is a machine learning platform built to help data scientists and ML engineers track, compare, and optimize machine learning experiments. Image by Author If you want to end the experiment, you can use the end method of the Experiment object to mark the experiment as complete. #
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TheSequence
MAY 24, 2023
Could you explain the data curation and training process required for building such a model? data or auto-generated files). cell outputs) for code completion in Jupyter notebooks (see this Jupyter plugin ). Were there any research breakthroughs in StarCoder, or would you say it was more of a crafty ML engineering effort?
AWS Machine Learning Blog
NOVEMBER 9, 2023
Create a KMS key in the dev account and give access to the prod account Complete the following steps to create a KMS key in the dev account: On the AWS KMS console, choose Customer managed keys in the navigation pane. Choose Create key. For Key type , select Symmetric. For Script Path , enter Jenkinsfile. Choose Save.
The MLOps Blog
JANUARY 23, 2023
Ok, let me explain. I believe the team will look something like this: Software delivery reliability: DevOps engineers and SREs ( DevOps vs SRE here ) ML-specific software: software engineers and data scientists Non-ML-specific software: software engineers Product: product people and subject matter experts Wait, where is the MLOps engineer?
DataRobot Blog
JANUARY 10, 2023
DataRobot Notebooks is a fully hosted and managed notebooks platform with auto-scaling compute capabilities so you can focus more on the data science and less on low-level infrastructure management. Auto-scale compute. In the DataRobot left sidebar, there is a table of contents auto-generated from the hierarchy of Markdown cells.
The MLOps Blog
JUNE 27, 2023
Model governance and compliance : They should address model governance and compliance requirements, so you can implement ethical considerations, privacy safeguards, and regulatory compliance into your ML solutions. This includes features for model explainability, fairness assessment, privacy preservation, and compliance tracking.
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