Auto-complete Definition ML Engineer 
AWS Machine Learning Blog
SEPTEMBER 4, 2024
Amazon SageMaker provides capabilities to remove the undifferentiated heavy lifting of building and deploying ML models. SageMaker simplifies the process of managing dependencies, container images, auto scaling, and monitoring. They often work with DevOps engineers to operate those pipelines. amazonaws.com/sagemaker-xgboost:1.7-1",
AWS Machine Learning Blog
JUNE 13, 2023
This post is co-written with Jad Chamoun, Director of Engineering at Forethought Technologies, Inc. and Salina Wu, Senior ML Engineer at Forethought Technologies, Inc. In addition, deployments are now as simple as calling Boto3 SageMaker APIs and attaching the proper auto scaling policies. 2xlarge instances.
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TheSequence
MAY 24, 2023
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? In addition we labelled a PII dataset for code to train a PII detector.
AWS Machine Learning Blog
SEPTEMBER 18, 2023
ML engineers must handle parallelization, scheduling, faults, and retries manually, requiring complex infrastructure code. In this post, we discuss the benefits of using Ray and Amazon SageMaker for distributed ML, and provide a step-by-step guide on how to use these frameworks to build and deploy a scalable ML workflow.
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. Under Advanced Project Options , for Definition , select Pipeline script from SCM. Choose Create key. Choose Save.
The MLOps Blog
JANUARY 23, 2023
Machine Learning Operations (MLOps): Overview, Definition, and Architecture” By Dominik Kreuzberger, Niklas Kühl, Sebastian Hirschl Great stuff. If you haven’t read it yet, definitely do so. How about the ML engineer? MLOps engineer today is either an ML engineer (building ML-specific software) or a DevOps engineer.
The MLOps Blog
JULY 18, 2023
You need to have a structured definition around what you’re trying to do so your data annotators can label information for you. In our early days, we definitely landed on the notion that there are really two critical pieces to all meeting notes. Machines don’t deal well with ambiguity. Now, we’re not perfect.
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