QA in Production

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Don't let your tests slow you down. Don't find out about bugs from your users. Rouan will provide a short introduction to how investing in the right production monitoring can help you find the issues that really affect your bottom line and speed up the way you build and release software.

QA in Production
@rouanw

Most bugs are surprises

Opportunities to learn

Monitoring what’s critical
System metrics
Usage metrics

Gathering monitoring data
Existing APIs
Log forwarding
Structured logging
Statistics

Responding to data
Alerts and thresholds
Dashboards

Tests are for what you already know

The wrong tests can slow you down

How fast can you ship a fix?

Finding balance

QA in Production
@rouanw

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A great deal of attention in medical devices has shifted towards cybersecurity with the ratification of section 524B of the FD&C act. This new law enables the FDA to enforce cybersecurity controls in any medical device that is capable of networked communications or that has software. In this webinar we will recap the process for managing vulnerabilities, identify categories of vulnerabilities and solutions and more.

The Real-World Challenges of Medical Device Cybersecurity- Mitigating Vulnera...

The Real-World Challenges of Medical Device Cybersecurity- Mitigating Vulnera...

The Real-World Challenges of Medical Device Cybersecurity- Mitigating Vulnera...

How To Use Server-Side Rendering with Nuxt.js

How To Use Server-Side Rendering with Nuxt.js

How To Use Server-Side Rendering with Nuxt.js

In an era where security concerns are paramount, the integration of artificial intelligence (AI) into CCTV cameras has revolutionized surveillance capabilities. One of the most significant advancements is the ability to achieve real-time threat detection, enabling immediate responses to potential security breaches. This blog explores how AI is reshaping surveillance through real-time threat detection and the implications of this technology.

Optimizing AI for immediate response in Smart CCTV

Optimizing AI for immediate response in Smart CCTV

Optimizing AI for immediate response in Smart CCTV

SyndBuddy AI 2k Review 2024: Revolutionizing Content Syndication with AI

SyndBuddy AI 2k Review 2024: Revolutionizing Content Syndication with AI

SyndBuddy AI 2k Review 2024: Revolutionizing Content Syndication with AI

ABDERRAOUF MEHENNI

Software Quality Assurance Interview Questions

Software Quality Assurance Interview Questions

Software Quality Assurance Interview Questions

Data spaces in distributed environments should be allowed to evolve in agile ways providing data space owners with large flexibility about which data they store. Agility and heterogeneity, however, jeopardize data exchanges because representations may build on varying ontologies and data consumers may not rely on the semantic correctness of their queries in the context of semantically heterogeneous, evolving data spaces. Graph data spaces are one example of a powerful model for representing and querying data whose semantics may change over time. To assert and enforce conditions on individual graph data spaces, shape languages (e.g SHACL) have been developed. We investigate the question of how querying and programming can be guarded by reasoning over SHACL constraints in a distributed setting and we sketch a picture of how a future landscape based on semantically heterogeneous data spaces might look like.

Shapes for Sharing between Graph Data Spaces - and Epistemic Querying of RDF-...

Shapes for Sharing between Graph Data Spaces - and Epistemic Querying of RDF-...

Shapes for Sharing between Graph Data Spaces - and Epistemic Querying of RDF-...

Foundation models are machine learning models which are easily capable of performing variable tasks on large and huge datasets. FMs have managed to get a lot of attention due to this feature of handling large datasets. It can do text generation, video editing to protein folding and robotics. In case we believe that FMs can help the hospitals and patients in any way, we need to perform some important evaluations, tests to test these assumptions. In this review, we take a walk through Fms and their evaluation regimes assumed clinical value. To clarify on this topic, we reviewed no less than 80 clinical FMs built from the EMR data. We added all the models trained on structured and unstructured data. We are referring to this combination of structured and unstructured EMR data or clinical data.

Reassessing the Bedrock of Clinical Function Models: An Examination of Large ...

Reassessing the Bedrock of Clinical Function Models: An Examination of Large ...

Reassessing the Bedrock of Clinical Function Models: An Examination of Large ...

harshavardhanraghave

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Unlocking the Future of AI Agents with Large Language Models

Unlocking the Future of AI Agents with Large Language Models

Unlocking the Future of AI Agents with Large Language Models

Learn the Fundamentals of XCUITest Framework_ A Beginner's Guide.pdf

Learn the Fundamentals of XCUITest Framework_ A Beginner's Guide.pdf

Learn the Fundamentals of XCUITest Framework_ A Beginner's Guide.pdf

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Tech Tuesday-Harness the Power of Effective Resource Planning with OnePlan’s ...

Tech Tuesday-Harness the Power of Effective Resource Planning with OnePlan’s ...

Tech Tuesday-Harness the Power of Effective Resource Planning with OnePlan’s ...

A Secure and Reliable Document Management System is Essential.docx

A Secure and Reliable Document Management System is Essential.docx

A Secure and Reliable Document Management System is Essential.docx

The Ultimate Test Automation Guide_ Best Practices and Tips.pdf

The Ultimate Test Automation Guide_ Best Practices and Tips.pdf

The Ultimate Test Automation Guide_ Best Practices and Tips.pdf

TECUNIQUE: Success Stories: IT Service provider

TECUNIQUE: Success Stories: IT Service provider

TECUNIQUE: Success Stories: IT Service provider

Diamond Application Development Crafting Solutions with Precision

Diamond Application Development Crafting Solutions with Precision

Diamond Application Development Crafting Solutions with Precision

Right Money Management App For Your Financial Goals

Right Money Management App For Your Financial Goals

Right Money Management App For Your Financial Goals

Unveiling the Tech Salsa of LAMs with Janus in Real-Time Applications

Unveiling the Tech Salsa of LAMs with Janus in Real-Time Applications

Unveiling the Tech Salsa of LAMs with Janus in Real-Time Applications

Steps To Getting Up And Running Quickly With MyTimeClock Employee Scheduling ...

Steps To Getting Up And Running Quickly With MyTimeClock Employee Scheduling ...

Steps To Getting Up And Running Quickly With MyTimeClock Employee Scheduling ...

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The Real-World Challenges of Medical Device Cybersecurity- Mitigating Vulnera...

The Real-World Challenges of Medical Device Cybersecurity- Mitigating Vulnera...

The Real-World Challenges of Medical Device Cybersecurity- Mitigating Vulnera...

How To Use Server-Side Rendering with Nuxt.js

How To Use Server-Side Rendering with Nuxt.js

How To Use Server-Side Rendering with Nuxt.js

Optimizing AI for immediate response in Smart CCTV

Optimizing AI for immediate response in Smart CCTV

Optimizing AI for immediate response in Smart CCTV

SyndBuddy AI 2k Review 2024: Revolutionizing Content Syndication with AI

SyndBuddy AI 2k Review 2024: Revolutionizing Content Syndication with AI

SyndBuddy AI 2k Review 2024: Revolutionizing Content Syndication with AI

Software Quality Assurance Interview Questions

Software Quality Assurance Interview Questions

Software Quality Assurance Interview Questions

Shapes for Sharing between Graph Data Spaces - and Epistemic Querying of RDF-...

Shapes for Sharing between Graph Data Spaces - and Epistemic Querying of RDF-...

Shapes for Sharing between Graph Data Spaces - and Epistemic Querying of RDF-...

Reassessing the Bedrock of Clinical Function Models: An Examination of Large ...

Reassessing the Bedrock of Clinical Function Models: An Examination of Large ...

Reassessing the Bedrock of Clinical Function Models: An Examination of Large ...

QA in Production

1. QA in Production @rouanw

2. Most bugs are surprises

3. Opportunities to learn

4. Monitoring what’s critical System metrics Usage metrics

5. Gathering monitoring data Existing APIs Log forwarding Structured logging Statistics

6. Responding to data Alerts and thresholds Dashboards

7. Tests are for what you already know

8. The wrong tests can slow you down

9. How fast can you ship a fix?

10. Finding balance

11. QA in Production @rouanw