Add AI to Your SDLC, presented by Applitools and Curiosity

I am an instructor of the MLOps workshop for some anonymous startup incubation program where the objectives are (1) to orchestrate and deploy updates to the application and the deep learning model in a unified way. (2) To design a DevOps pipeline to coordinate retrieving the latest best model from the model registry, packaging the web application, deploying the web application and inferencing web service.

The document discusses Amazon SageMaker, a fully managed machine learning service. It provides an overview of SageMaker's capabilities for preparing, building, training and deploying machine learning models. Key features highlighted include SageMaker Studio for an integrated development environment, Autopilot for automatic model creation, JumpStart for pre-built solutions, and Data Wrangler for preparing data. Use cases and demos are presented to illustrate how customers can use SageMaker's services and features to develop machine learning applications.

This document discusses continuous testing and provides an overview of key concepts. It defines continuous testing as executing automated tests as part of the software delivery pipeline to rapidly obtain feedback on release candidates. The benefits of continuous testing discussed include fast feedback, continuous visibility, and testing that matches different skill levels. It also covers topics like test authoring best practices, key performance indicators for continuous testing, and the potential future role of machine learning. Continuous testing is presented as a way to mature an organization's path toward DevOps practices.

"In this workshop, you practice running an environment with a test and production deployment pipeline. Along the way, we cover topics such as static code analysis, dynamic infrastructure review, and workflow types. You also learn how to update your process in response to security events. We write new AWS Lambda functions and incorporate them into the pipeline, and we consider capabilities such as AWS Systems Manager Parameter Store and AWS Secrets Manager.

AgileChennai.com AI-Powered Agile : Transforming How We Deliver Extraordinary Impact Gayathri Pandian General Manager, Thoughtworks & Kalarani Lakshmanan Engineering Director, Thoughtworks

The document discusses approaches for determining when to stop testing based on three dimensions of test coverage: system logic, device mix, and system tiers. It advocates targeting tests based on changes across the software development lifecycle to focus testing on at-risk areas. Maintaining a single source of truth and using coverage algorithms can help generate targeted automated tests across user interfaces, APIs and backends in a way that avoids wasteful over-testing or risky under-testing. The presentation concludes with a question and answer section and a call to action to learn more about test modeling and automation approaches.

Deliver Visually Perfect Digital Experiences across every screen with Test Automation powered by Visual AI

Model-based testing offers several advantages over traditional documentation-based approaches to quality assurance. It allows testers to create executable models that can automatically generate test cases and scripts, focusing testing on high-risk areas. When the software changes, the model can automatically update dependent tests. Using the same models across teams provides a central point of reference that brings business and development groups into alignment. Modeling catches design flaws earlier in the development process compared to traditional testing approaches.

This document introduces CloudOne as a company that provides managed continuous engineering services for companies developing Internet of Things (IoT) products and solutions. It discusses how CloudOne integrates infrastructure, tools, and support across continuous engineering and DevOps, enterprise asset management, analytics, and devices and applications. The document emphasizes that continuous engineering is a core capability needed for successful IoT strategies and outlines benefits of CloudOne's services such as enterprise-class security, ability to reuse existing tools and processes, and enabling global collaboration. Examples are given of major companies that use CloudOne's services for their IoT initiatives.

Deliver Visually Perfect Digital Experiences across every screen with Test Automation powered by Visual AI

Quality assurance (QA) is a strategic way of preventing mistakes and defects in developed products and avoiding problems when delivering products or services to customers. This defect prevention in quality assurance differs subtly from defect detection and rejection in quality control and has been referred to as a shift left since it focuses on quality earlier in the process

This document discusses quality assurance testing for progressive applications. It defines quality assurance as preventing defects through early testing. Progressive testing tests application modules incrementally in a top-down, bottom-up, or hybrid approach. A quality assurance checklist should include unit, regression, performance, security, and installation testing to validate the application and ensure long-term functionality. Comprehensive testing provides benefits like reduced costs, improved customer satisfaction, and increased profits.

Software quality reflects degree of excellence with which a product is developed and performs. At Software Quality Days Vienna 2020, Nagarro QA Experts, Rajni Singh and Khimanand Upreti discuss how well defined and structured requirements acts as foundation stones for ensuring success of any software development process. They also speak about the need for the development of a framework that would contribute in combining various AI techniques along with their drivers for requirements phase.

This presentation explores how busting software bugs does more than ensure the reliability and performance of your software—it helps ensure application security. Topics covered include: How AppSec processes are really quality processes How software bugs are really security vulnerabilities How to apply coding standards as part of a continuous testing process to prevent defects from affecting the safety, security, and reliability of your applications

This document discusses how automation can help reveal technical debt. It explains that technical debt is accumulated to deliver features faster but often goes unpaid. Automation provides a "safety net" to identify debt through continuous integration, code inspection, and trend analysis. Implementing automation unexpectedly uncovered unknown issues and improved understanding of problems like dependencies, testing gaps, and deployment challenges. Both direct and indirect benefits occur at the team and enterprise levels by standardizing processes and providing visibility into issues.