Machine learning inference basically entails
deploying a software application into a production environment
, as the ML model is typically just software code that implements a mathematical algorithm. That algorithm makes calculations based on the characteristics of the data, known as “features” in the ML vernacular.
How does SageMaker run your inference image?
How SageMaker Runs Your Inference Image. SageMaker overrides default CMD statements in a container by specifying the
serve argument
after the image name. The serve argument overrides arguments that you provide with the CMD command in the Dockerfile.
What is inference code in AWS?
To use your own inference code with a persistent endpoint to get one prediction at a time, use SageMaker hosting services. … To use your own inference code to get predictions for an entire dataset, use SageMaker batch transform.
How does SageMaker inference work?
Within an inference pipeline model, SageMaker
handles invocations as a sequence of HTTP requests
. The first container in the pipeline handles the initial request, then the intermediate response is sent as a request to the second container, and so on, for each container in the pipeline.
What is opt ML?
The output
/opt/ml/model/ is
the directory where you write the model that your algorithm generates
. Your model can be in any format that you want. It can be a single file or a whole directory tree. … /opt/ml/output is a directory where the algorithm can write a file failure that describes why the job failed.
What is a SageMaker model?
Amazon SageMaker is
a fully managed machine learning service
. With SageMaker, data scientists and developers can quickly and easily build and train machine learning models, and then directly deploy them into a production-ready hosted environment.
What is a SageMaker endpoint?
This is where an Amazon SageMaker endpoint steps in – an Amazon SageMaker endpoint is
a fully managed service that allows you to make real-time inferences via a REST API
.
Does SageMaker use ECS?
Train and host Scikit-Learn models in Amazon SageMaker by building a Scikit Docker container. … In the overview, we’ll discuss how Amazon SageMaker runs Docker images that have been loaded from Amazon
Elastic Container Service
(ECS) for training and hosting models.
How does SageMaker run your training image?
SageMaker overrides any default CMD statement in a container by specifying
the train argument after the image name
. The train argument also overrides arguments that you provide using CMD in the Dockerfile.
What is a SageMaker estimator?
The Amazon SageMaker Python SDK provides
framework estimators and generic estimators to train your model
while orchestrating the machine learning (ML) lifecycle accessing the SageMaker features for training and the AWS infrastructures, such as Amazon Elastic Container Registry (Amazon ECR), Amazon Elastic Compute Cloud …
Are SageMaker endpoints public?
Endpoints are scoped to an individual account, and
are not public
. The URL does not contain the account ID, but Amazon SageMaker determines the account ID from the authentication token that is supplied by the caller.
How does SageMaker achieve scalability?
Amazon SageMaker supports
automatic scaling
(autoscaling) for your hosted models. Autoscaling dynamically adjusts the number of instances provisioned for a model in response to changes in your workload. When the workload increases, autoscaling brings more instances online.
Do SageMaker endpoints cost money?
Using SageMaker Studio is free
, you only pay for the AWS services that you use within Studio. You can use many services within SageMaker Studio at no additional charge, including: SageMaker Pipelines to automate and manage automated ML workflows.
What is a SageMaker image?
A SageMaker image is
a file that identifies the kernels, language packages, and other dependencies required to run a Jupyter notebook in Amazon SageMaker Studio
. These images are used to create an environment that you then run the Jupyter notebooks from. Amazon SageMaker provides many built-in images for you to use.
Where is opt ML?
SageMaker makes the hyperparameters in a CreateTrainingJob request available in the Docker container in the
/opt/ml/input
/config/hyperparameters.
What is SageMaker local mode?
The Amazon SageMaker local mode
allows you to switch seamlessly between local and distributed
, managed training by simply changing one line of code. Everything else works the same.
