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Build a cross-account MLOps workflow using the Amazon SageMaker model registry

AWS Machine Learning

When designing production CI/CD pipelines, AWS recommends leveraging multiple accounts to isolate resources, contain security threats and simplify billing-and data science pipelines are no different. Some things to note in the preceding architecture: Accounts follow a principle of least privilege to follow security best practices.

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What Is Knowledge Engineering and Why Do I Need It for Chatbot Development?

Aspect

It also has to be engineered to fit different purposes and contexts. No, there are simple, static bots that can be developed with scripting tools. These bots allow for conversation branching and connection to structured data sources such as account balances. appeared first on Aspect Blogs.

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Everything You Need to Know About Auto Attendant

Hodusoft

In this blog post, we will discuss everything about auto attendants starting from what they are, how they work, the pros and cons of using auto attendants, how to set up an auto attendant, and how to include scripts. Pros of Using Auto Attendant Cons of Auto Attendant Auto Attendant Scripts – What to Record?

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Build Streamlit apps in Amazon SageMaker Studio

AWS Machine Learning

The code for this blog can be found in this GitHub repository. As long as a user has access to the AWS account, Studio domain ID, and user profile, they can access the link. The code for this blog can be found in this GitHub repository. This will serve as a starting point, and it can be generalized to demo any custom ML model.

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Modernizing data science lifecycle management with AWS and Wipro

AWS Machine Learning

Wipro further accelerated their ML model journey by implementing Wipro’s code accelerators and snippets to expedite feature engineering, model training, model deployment, and pipeline creation. Across accounts, automate deployment using export and import dataset, data source, and analysis API calls provided by QuickSight.

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How BigBasket improved AI-enabled checkout at their physical stores using Amazon SageMaker

AWS Machine Learning

Each worker then proceeds with the forward and backward pass defined in your training script on each GPU. In our entire partnership, AWS has set the bar on customer obsession and delivering results—working with us the whole way to realize promised benefits.” – Keshav Kumar, Head of Engineering at BigBasket.

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Build and train ML models using a data mesh architecture on AWS: Part 2

AWS Machine Learning

In part 1 , we addressed the data steward persona and showcased a data mesh setup with multiple AWS data producer and consumer accounts. The workflow consists of the following components: The producer data steward provides access in the central account to the database and table to the consumer account. Data exploration.

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