Summary of AWS blogs for the week of Monday Apr 24

In the week of Mon Apr 24 2023 AWS published 83 blog posts – here is an overview of what happened.

Topics Covered

AWS DevOps Blog

Utilizing DevSecOps for Faster Application Builds with Amazon CodeGuru Reviewer and Bitbucket Pipelines

Integrating Security Controls into CI/CD Workflows with DevSecOps

DevSecOps is a set of best practices that combine security controls into continuous integration and delivery (CI/CD) workflows. The first step in this process is utilizing Static Application Security Testing (SAST) tools to search for potential security vulnerabilities before code is executed for the first time. Catching vulnerable code early in the development process can help prevent costly security breaches later.

Using Amazon CodeGuru Reviewer and Bitbucket Pipelines to Implement DevSecOps

Amazon CodeGuru Reviewer is an integrated service that supports code-level security testing. It uses machine learning to identify security and performance vulnerabilities in code. It can be used with Bitbucket Pipelines, a cloud-based CI/CD tool. This combination of services makes it easy to implement DevSecOps and ensure that code is secure before it is deployed.

Accelerating Application Builds with Amazon CodeWhisperer

Amazon CodeWhisperer is a powerful generative AI tool that helps users build applications faster by automating common coding tasks. By incorporating CodeWhisperer into their workflow, developers can drastically reduce their development time and produce better results. However, effectively using CodeWhisperer requires a beginner’s mindset and willingness to adopt new methods.

How Cloud2 Can Help

At Cloud2, we specialize in providing professional and managed services for AWS. Our team of AWS experts is experienced in working with Amazon CodeGuru Reviewer and Bitbucket Pipelines to build secure, reliable applications quickly. We can also provide guidance on how to use Amazon CodeWhisperer to accelerate application builds. Contact us today to learn more about how we can help you get the most out of DevSecOps and Amazon’s generative AI tools.

Read the full blog posts from AWS

Official Machine Learning Blog of Amazon Web Services

Recent Advances In Machine Learning To Improve Multi-Hop Reasoning And Extend Functionality Of AWS Trainium

Large language models (LLMs) are making tremendous progress in natural language understanding, but they are prone to generating confident but nonsensical explanations, posing a significant obstacle to establishing trust with users. This post introduces a method to incorporate human feedback on incorrect reasoning chains for multi-hop reasoning to improve performance. Additionally, the post covers how to extend the functionality of AWS Trainium with custom operators.

Incorporating Human Feedback for Improved Multi-Hop Reasoning

Incorporating human feedback on the incorrect reasoning chains can bridge the gap between LLMs and providing consistent and trust-worthy results. To do this, a framework is needed that can take in feedback from a human evaluator and use it to update a language model. By using feedback from a human evaluator, the model is able to adjust its weights and improve its performance. In addition, the framework must account for the fact that human feedback may be noisy, as some feedback may be incorrect or incomplete. To account for this, the framework must be able to identify false feedback and discard it.

This process enables the model to learn from rich human feedback, becoming more accurate and reliable. The improved performance can then be used to provide more accurate and trustworthy explanations.

Extending The Functionality Of AWS Trainium With Custom Operators

Deep learning is constantly evolving, and practitioners are continuously creating new models and ways to speed up existing models. Custom operators are one of the methods used to extend the functionality of existing ML frameworks such as PyTorch. An operator is a function that defines how a model should perform a certain operation, such as an activation function or a convolution operation. By using custom operators, developers can add their own custom logic to a model, allowing them to create more powerful and accurate models.

Using custom operators can significantly improve the performance of a model, and is becoming increasingly popular in the ML space. With AWS Trainium, developers can add custom operators to their models without having to write code. With AWS Trainium, developers can also use pre-trained models and leverage the insights they provide to improve the performance of their own models.

Delivering Your First ML Use Case In 8–12 Weeks

Many executives believe that ML can be applied to any business decision, however, only half of ML projects make it to production. To help move ML journey from pilot to production, Amazon offers support to implement the first ML use case. The post covers how to implement the use case with Amazon SageMaker and also provides a timeline of 8–12 weeks.

The implementation process involves four steps: data preparation, training, inference, and deployment. During the data preparation stage, the dataset needs to be cleaned, formatted, and split into training and testing sets. During the training stage, the ML model should be trained on the training dataset. The inference stage is used to evaluate the performance of the model on the test dataset. And the deployment stage is used to send the model to production.

Running Local Machine Learning Code As Amazon SageMaker Training Jobs With Minimal Code Changes

The Amazon SageMaker Python SDK enables data scientists to run their ML code on Amazon SageMaker training jobs with minimal changes. This helps data scientists to quickly deploy their models to production. The process involves adding a few lines of code to their existing code and making the necessary changes to support the SageMaker environment.

This feature allows data scientists to take advantage of the SageMaker environment without having to rewrite their code. By doing this, they can quickly move their ML projects from development to production.

Performing Intelligent Search Across Emails In Your Google Workspace Using The Gmail Connector For Amazon Kendra

Google Workspace is a set of productivity and collaboration tools including Gmail for Business, Google Drive, Google Docs, Google Sheets, and more. Emails contain a wealth of information, which can be difficult to access and use. The Gmail connector for Amazon Kendra is a feature that makes it easy to search through emails and make use of the information they contain.

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