Sign Up Now!

Sign up and get personalized intelligence briefing delivered daily.


Sign Up

Articles related to "aws"


What is Amazon EC2 and how to use it?

  • In this article, I would go over the basics of Amazon Elastic Compute Cloud (EC2), a web service that allows one to launch instances, which offer secure and resizable compute capacity in the cloud.
  • You can launch instances that are copies of AMIs running as virtual servers in the cloud.
  • These isolated locations have Local Zones where you place resources, such as compute and storage, in multiple locations closer to your end-users.
  • By using IAM with Amazon EC2, you have full control over the users performing tasks with Amazon EC2 and specific AWS resources.
  • Note: If you have never launched an instance of any type on AWS, you might receive an email from AWS Support asking you to initialize your account by creating an instance before they approve the limit increase.
  • Once AWS approves your GPU Limit Increase Request, you can start the process of launching your instance.

save | comments | report | share on


What is Amazon EC2 and how to use it?

  • In this article, I would go over the basics of Amazon Elastic Compute Cloud (EC2), a web service that allows one to launch instances, which offer secure and resizable compute capacity in the cloud.
  • You can launch instances that are copies of AMIs running as virtual servers in the cloud.
  • These isolated locations have Local Zones where you place resources, such as compute and storage, in multiple locations closer to your end-users.
  • By using IAM with Amazon EC2, you have full control over the users performing tasks with Amazon EC2 and specific AWS resources.
  • Note: If you have never launched an instance of any type on AWS, you might receive an email from AWS Support asking you to initialize your account by creating an instance before they approve the limit increase.
  • Once AWS approves your GPU Limit Increase Request, you can start the process of launching your instance.

save | comments | report | share on


AWS and Verizon Quickly Adding 5G Mobile Edge Computing Locations

  • AWS and Verizon are quickly expanding the number of locations where the former's computing infrastructure and the latter's 5G wireless network meet to provide edge computing services for next-generation ultra-low-latency mobile applications.
  • Wavelength embeds AWS compute and storage to Verizon's 5G mobile networks at the edge, providing access to cloud infrastructure over fewer network hops than possible when connecting to one of the big traditional AWS cloud availabilty regions.
  • In this case, the AWS edge computing infrastructure is deployed at Verizon's network switching facilities, called "Service Access Point" sites, which are located near its wireless towers.
  • Microsoft is taking a similar approach with one of its recently announced edge computing services, tightly integrating Azure cloud edge nodes with AT&T's network, at the carrier's edge.
  • Microsoft Azure is AWS's biggest rival in the cloud computing market.

save | comments | report | share on


Store and Access Time Series Data at Any Scale with Amazon Timestream – Now Generally Available | Amazon Web Services

  • Queries automatically access and combine recent and historical data across tiers without the need to specify the storage location, and support time series-specific functionalities to help you identify trends and patterns in data in near real time.
  • Timestream integrates with popular services for data collection, visualization, and machine learning, making it easy to use with existing and new applications.
  • You can visualize data stored in Timestream from Amazon QuickSight, and use Amazon SageMaker to apply machine learning algorithms to time series data, for example for anomaly detection.
  • You can use Timestream fine-grained AWS Identity and Access Management (IAM) permissions to easily ingest or query data from an AWS Lambda function.
  • I add a new GrafanaDemo table to my database, and use another sample application to continuously ingest data.

save | comments | report | share on


Setting up Amazon SageMaker Environment On Your Local Machine

  • Amazon SageMaker is beyond just managed Jupyter notebooks, it is a fully managed service that enables you to build, train, optimize and deploy machine learning models.
  • SageMaker Python SDK — This is a high level API in Python that abstracts the code to build, train and deploy machine learning models.
  • In most cases you will use this service-level APIs for things such creating resources for automations or interacting with other AWS services that are not supported by the SageMaker Python SDK.
  • You write the code to build your model as you normally would but instead of a SageMake Notebook Instance (or a SageMaker Studio Notebook), you do this one your local machine running Jupyter or from your IDE.
  • Since you are running this from your local machine using your AWS credentials as opposed to a notebook instance with an attached role, get_execution_role() will not work.

save | comments | report | share on


Setting up Amazon SageMaker Environment On Your Local Machine

  • Amazon SageMaker is beyond just managed Jupyter notebooks, it is a fully managed service that enables you to build, train, optimize and deploy machine learning models.
  • SageMaker Python SDK — This is a high level API in Python that abstracts the code to build, train and deploy machine learning models.
  • In most cases you will use this service-level APIs for things such creating resources for automations or interacting with other AWS services that are not supported by the SageMaker Python SDK.
  • You write the code to build your model as you normally would but instead of a SageMake Notebook Instance (or a SageMaker Studio Notebook), you do this one your local machine running Jupyter or from your IDE.
  • Since you are running this from your local machine using your AWS credentials as opposed to a notebook instance with an attached role, get_execution_role() will not work.

save | comments | report | share on


Setting up Amazon SageMaker Environment On Your Local Machine

  • Amazon SageMaker is beyond just managed Jupyter notebooks, it is a fully managed service that enables you to build, train, optimize and deploy machine learning models.
  • SageMaker Python SDK — This is a high level API in Python that abstracts the code to build, train and deploy machine learning models.
  • In most cases you will use this service-level APIs for things such creating resources for automations or interacting with other AWS services that are not supported by the SageMaker Python SDK.
  • You write the code to build your model as you normally would but instead of a SageMake Notebook Instance (or a SageMaker Studio Notebook), you do this one your local machine running Jupyter or from your IDE.
  • Since you are running this from your local machine using your AWS credentials as opposed to a notebook instance with an attached role, get_execution_role() will not work.

save | comments | report | share on


Setting up Amazon SageMaker Environment On Your Local Machine

  • Amazon SageMaker is beyond just managed Jupyter notebooks, it is a fully managed service that enables you to build, train, optimize and deploy machine learning models.
  • SageMaker Python SDK — This is a high level API in Python that abstracts the code to build, train and deploy machine learning models.
  • In most cases you will use this service-level APIs for things such creating resources for automations or interacting with other AWS services that are not supported by the SageMaker Python SDK.
  • You write the code to build your model as you normally would but instead of a SageMake Notebook Instance (or a SageMaker Studio Notebook), you do this one your local machine running Jupyter or from your IDE.
  • Since you are running this from your local machine using your AWS credentials as opposed to a notebook instance with an attached role, get_execution_role() will not work.

save | comments | report | share on


Setting up Amazon SageMaker Environment On Your Local Machine

  • Amazon SageMaker is beyond just managed Jupyter notebooks, it is a fully managed service that enables you to build, train, optimize and deploy machine learning models.
  • SageMaker Python SDK — This is a high level API in Python that abstracts the code to build, train and deploy machine learning models.
  • In most cases you will use this service-level APIs for things such creating resources for automations or interacting with other AWS services that are not supported by the SageMaker Python SDK.
  • You write the code to build your model as you normally would but instead of a SageMake Notebook Instance (or a SageMaker Studio Notebook), you do this one your local machine running Jupyter or from your IDE.
  • Since you are running this from your local machine using your AWS credentials as opposed to a notebook instance with an attached role, get_execution_role() will not work.

save | comments | report | share on


Setting up Amazon SageMaker Environment On Your Local Machine

  • Amazon SageMaker is beyond just managed Jupyter notebooks, it is a fully managed service that enables you to build, train, optimize and deploy machine learning models.
  • SageMaker Python SDK — This is a high level API in Python that abstracts the code to build, train and deploy machine learning models.
  • In most cases you will use this service-level APIs for things such creating resources for automations or interacting with other AWS services that are not supported by the SageMaker Python SDK.
  • You write the code to build your model as you normally would but instead of a SageMake Notebook Instance (or a SageMaker Studio Notebook), you do this one your local machine running Jupyter or from your IDE.
  • Since you are running this from your local machine using your AWS credentials as opposed to a notebook instance with an attached role, get_execution_role() will not work.

save | comments | report | share on