1) The document discusses testing machine learning and AI applications. It covers topics like what AI and ML are, how technology is shifting towards them, and where quality assurance fits in.
2) Hands-on activities are described for creating and testing a basic wine-beer classifier and an image classifier using mobile networks.
3) Challenges of testing AI and ML applications include generating training and test data, knowing thresholds, and dealing with data quality issues like overfitting and underfitting. Metrics like accuracy, precision, recall, and error rates are discussed.
Delivered talk on Performance Testing : Cloud Deployments by Shreyas Chaudhari and Manish Hemnani at ThoughtWorks, Pune on 16th March, 2019 in VodQA Pune 2019.
Alexander Andelkovic. Comaqa Spring 2018. Using Artificial Intelligence to Te...COMAQA.BY
Candy Crush Saga is one of the biggest mobile games today with more than 1000 levels of difficulty - and users continue to ask for more. When building new content, it is extremely important to make sure that the level of difficulty is balanced and that the user does not experience crashes or problems through some unforeseen level of play. Alexander Andelkovic shows you how King is training artificial intelligence (AI) programs (bots) to test its games by mimicking human interactions. Join Alex as he discusses how King is taking testing to the next level by employing Monte Carlo Tree Search, automatic heuristic construction, and NeuroEvolution of Augmenting Topologies (NEAT) to train bots to test and evaluate difficulty levels. He discusses ways to extend and use AI bots to predict game success rates and conduct automatic performance testing. Alex explains how this AI approach can be generalized to test other applications. Learn how AI can help you with testing that's getting very difficult to master with traditional testing techniques.
ATAGTR2017 Differentiation using Testing Tools and Automation in the BFS COTS...Agile Testing Alliance
The presentation on Differentiation using Testing Tools and Automation in the BFS COTS Product World was done during #ATAGTR2017, one of the largest global testing conference. All copyright belongs to the author.
Author and presenter : Vrushal Palyekar
Delivered talk on Performance Testing : Cloud Deployments by Shreyas Chaudhari and Manish Hemnani at ThoughtWorks, Pune on 16th March, 2019 in VodQA Pune 2019.
Alexander Andelkovic. Comaqa Spring 2018. Using Artificial Intelligence to Te...COMAQA.BY
Candy Crush Saga is one of the biggest mobile games today with more than 1000 levels of difficulty - and users continue to ask for more. When building new content, it is extremely important to make sure that the level of difficulty is balanced and that the user does not experience crashes or problems through some unforeseen level of play. Alexander Andelkovic shows you how King is training artificial intelligence (AI) programs (bots) to test its games by mimicking human interactions. Join Alex as he discusses how King is taking testing to the next level by employing Monte Carlo Tree Search, automatic heuristic construction, and NeuroEvolution of Augmenting Topologies (NEAT) to train bots to test and evaluate difficulty levels. He discusses ways to extend and use AI bots to predict game success rates and conduct automatic performance testing. Alex explains how this AI approach can be generalized to test other applications. Learn how AI can help you with testing that's getting very difficult to master with traditional testing techniques.
ATAGTR2017 Differentiation using Testing Tools and Automation in the BFS COTS...Agile Testing Alliance
The presentation on Differentiation using Testing Tools and Automation in the BFS COTS Product World was done during #ATAGTR2017, one of the largest global testing conference. All copyright belongs to the author.
Author and presenter : Vrushal Palyekar
ATAGTR2017 CDC Tests - Integration Tests cant be made simpler than this!Agile Testing Alliance
The presentation on CDC Tests - Integration Tests cant be made simpler than this! was done during #ATAGTR2017, one of the largest global testing conference. All copyright belongs to the author.
Author and presenter : Ramya Authappan
The last two decades have been all about SaaS, with advantages that cannot be overstated. Except SaaS isn’t always an option, nor is it always the right choice: businesses in tightly regulated industries, or where information security is paramount, for example, will not - often can not - consider any software that isn’t under their control. For many software enterprises, this leads to the dreaded inevitability of on-premise deployment.
Fortunately, the situation today is dramatically different to a scant few years ago, let alone a decade or two: the same technologies that enable SaaS have also radically transformed on-prem deployment. Modern tools like Docker, Consul, ELK and Kubernetes - to name a few - can be leveraged to completely transform the experience for both customers and vendors. In this talk we’ll contrast the challenges and advantages of SaaS and on-prem, see how things have evolved in recent history, and see how modern on-prem deployment can be, if not pleasurable, at least relatively painless.
Do you need the how-to for cloud best practices? AWS Well-Architected is actionable guidance for architecting systems in the cloud based on AWS’s unparalleled 14 years of public cloud experience with many of the world’s most amazing companies.
AWS Well-Architected helps cloud architects build secure, high-performing, resilient, and efficient cloud infrastructure for their applications and workloads. And, it empowers business results like reduced costs or security and compliance risk, improved reliability of core systems for better customer experiences, and much more.
During this live session, you’ll learn:
What AWS Well-Architected is and how to use it.
Amazing customer results from AWS Well-Architected.
What’s new — including a deep dive on Cloud Financial Management: Functional Ownership, Finance and Technology Partnership, Cloud Budgets and Forecasts, Cost-Aware Processes, Cost-Aware Culture, Quantifying Business Value Delivered Through Cost Optimization.
This session is most appropriate for CCoE lead, cloud infrastructure management, DevOps engineering management, DevOps and cloud practice leads, chief architects, solutions architects.
Real Testing Scenario Strategy - The Role of Exploratory TestingAdam Sandman
• Where to use exploratory testing
• Tools you can use (capture tools like bugreplay), tracking tools like SpiraTest’s new exploratory mode, etc.
• How to fit it into your sprint plan and best practices to working with developers to identify and fix issues found in exploratory testing.
Datadog: From a single product to a growing platform by Alexis Lê-Quôc, CTOTheFamily
By Alexis (https://twitter.com/alq), CTO at Datadog (https://www.datadoghq.com)
Alexis built Datadog's whole infrastructure and team from scratch as a co-founder. From a very small & dedicated team with no experience, he learned step by step to build a complete product ️
He shared with us his experience as a co-founder and CTO building a cloud giant in New York. How do you keep learning, how do you interact with customers & your market to drive your product development, and how do you monitor it all to make you company evolve will be the main topics of his talk.
A differnt Type of Supermarket DeliveryThoughtworks
In a world of seamless deployments to auto-scaling cloud environments, a ThoughtWorks team found itself in a very different place - trying to deploy a RESTful pricing API to every one of a UK supermarket’s 40,000 tills in a reliable, repeatable fashion.
SeleniumCamp 2020 - Shift Right and ObservabilityMarcus Merrell
SeleniumCamp 2020 presentation about the rise of observability, risk management, and weaving testing concepts throughout the SDLC. Examples of using analytics to increase confidence in your release cycle
This talk will focus on Techniques, metrics and different tests (code, models, infra and features/data) that help the developers of machine learning systems to achieve CD.
Your Testing Is Flawed: Introducing A New Open Source Tool For Accurate Kuber...StormForge .io
Complimentary Live Webinar
Sponsored by StormForge
Analyzing the performance and behavior of applications run on Kubernetes is often challenging, making the need to optimize prior to production something that you must have. However, a problem has reared its head in the form of a question: How do you get an accurate measurement of application performance or other behavior without accurate testing or an accurate representation of how it will run in production? In this webinar, we will present and discuss a new fully Open Source tool for creating the needed tests with which to accurately measure your applications. We hope you will join us to learn more about this tool, and find out how you can help contribute.
This webinar is sponsored by StormForge and hosted by The Linux Foundation.
Speaker
Noah Abrahams, Open Source Advocate
Noah is an Open Source Advocate for StormForge, merging Open Source Strategy with Developer Advocacy. He has been involved in cloud for over 12 years, has been contributing to the Kubernetes ecosystem for 5 years, and has been up and down the business stack from DevOps and Architecture to Sales, Enablement, and Education. You will find him running meetups in Las Vegas and attending conferences, once those are both happening again.
Continuous Behavior - BDD in Continuous Delivery (CoDers Who Test, Gothenburg...Gáspár Nagy
Abstract: This session is about my experience with BDD in a Continuous Delivery model. Many teams use Behavior Driven Development (BDD) for automated UI testing. Although UI testing might be an important element of your verification pipeline, it is slow, brittle and costly. But in the era of modern software development we would like to have fast feedback and quick reactions. A continuous delivery / deployment (CD) model can support this well. But what does it look like with BDD then? Can BDD help implementing CD?
This session is an attempt to define the role of BDD in CD. What works, what does not? Continuous delivery should mean continuous quality for the delivery team. What kind of quality criteria do we want to target with CD and how is it supported by BDD?
We are sinking: Hitting the testing iceberg (CukenFest London, 2018)Gáspár Nagy
At the session I am going to show you the concept of the testing iceberg through a concrete example. We will see how the "undersea" tests can support the ones on the tip, focusing on business requirements. We will see how you can move tests between the different levels so that finally we get more insight how the testing pyramid can be used in your own projects.
Can i service this from my raspberry piThoughtworks
Infrastructure-related skills are essential for developers in cross-functional teams who build microservices for the cloud. Becoming proficient in infrastructure development is not just about understanding the hardware and software components on top of which applications run in the cloud. It's also about being able to use the tools that provide virtual access to this infrastructure and enable us to provision, configure, monitor it, and deploy applications to it. In this talk Gesa shares how building a Kubernetes cluster of Raspberry Pis and serving applications from it can help in acquiring fundamental infrastructure skills.
If operations is a classic big data problem, cloud Operations is a *huge* data problem. We all understand the volume of logs, alerts and metrics generated by SaaS applications, and the increasing complexity of hybrid infrastructure, requires you to step up your monitoring strategy and just like any other big data problem – it only makes sense to leverage AI to achieve the observability imperative.
Taking into consideration the sheer volume of IT monitoring data that you have to deal with each and every day as DevOps, IT or SRE- leveraging traditional, reactive monitoring tools and approaches wont cut it much longer. Infusing AI is not about magically identifying and automatically solving all your problems, but given the criticality of delivering a phenomenal user experience for SaaS- you can leverage machine learning models to provide you with insights-rather than data- to not only effectively detect abnormal behaviors but also to predict potential issues, map them to associated services and help you intelligently prioritize preventive, troubleshooting and remediation efforts.
Building and operating a global cloud infrastructure at a large scale is a complex task with hundreds of ever-evolving service components. I am happy to share with you some real-world examples of how AI is leveraged at Azure Marketplace and Linkedin scale to monitor wisely, predict capacity and save costs so you can think how you can take it home, and apply it in your production environments.
Connecting the clouds, A TrueLime StoryJeroen Fürst
In this business case study, Jeroen shares his journey on how various microservices have been integrated, resulting in a rich and lightweight fresh new company blog.
With latest technologies evolving like Machine Learning, QA's must know the right strategy to test applications as AI apps like Personal Assistants, Smart Cars have direct impact in our life.
Reviewing progress in the machine learning certification journey
𝗦𝗽𝗲𝗰𝗶𝗮𝗹 𝗔𝗱𝗱𝗶𝘁𝗶𝗼𝗻 - Short tech talk on How to Network by Qingyue(Annie) Wang
C𝗼𝗻𝘁𝗲𝗻𝘁 𝗿𝗲𝘃𝗶𝗲𝘄 𝗼𝗻 AI and ML on Google Cloud by Margaret Maynard-Reid
𝗔 𝗳𝗼𝗰𝘂𝘀𝗲𝗱 𝗰𝗼𝗻𝘁𝗲𝗻𝘁 𝗿𝗲𝘃𝗶𝗲𝘄 𝗼𝗻 𝗠𝗟 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 𝗳𝗿𝗮𝗺𝗶𝗻𝗴, 𝗺𝗼𝗱𝗲𝗹 𝗲𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻, 𝗮𝗻𝗱 𝗳𝗮𝗶𝗿𝗻𝗲𝘀𝘀 by Sowndarya Venkateswaran.
A discussion on sample questions to aid certification exam preparation.
An interactive Q&A session to clarify doubts and questions.
Previewing next steps and topics, including course completions and material reviews.
ATAGTR2017 CDC Tests - Integration Tests cant be made simpler than this!Agile Testing Alliance
The presentation on CDC Tests - Integration Tests cant be made simpler than this! was done during #ATAGTR2017, one of the largest global testing conference. All copyright belongs to the author.
Author and presenter : Ramya Authappan
The last two decades have been all about SaaS, with advantages that cannot be overstated. Except SaaS isn’t always an option, nor is it always the right choice: businesses in tightly regulated industries, or where information security is paramount, for example, will not - often can not - consider any software that isn’t under their control. For many software enterprises, this leads to the dreaded inevitability of on-premise deployment.
Fortunately, the situation today is dramatically different to a scant few years ago, let alone a decade or two: the same technologies that enable SaaS have also radically transformed on-prem deployment. Modern tools like Docker, Consul, ELK and Kubernetes - to name a few - can be leveraged to completely transform the experience for both customers and vendors. In this talk we’ll contrast the challenges and advantages of SaaS and on-prem, see how things have evolved in recent history, and see how modern on-prem deployment can be, if not pleasurable, at least relatively painless.
Do you need the how-to for cloud best practices? AWS Well-Architected is actionable guidance for architecting systems in the cloud based on AWS’s unparalleled 14 years of public cloud experience with many of the world’s most amazing companies.
AWS Well-Architected helps cloud architects build secure, high-performing, resilient, and efficient cloud infrastructure for their applications and workloads. And, it empowers business results like reduced costs or security and compliance risk, improved reliability of core systems for better customer experiences, and much more.
During this live session, you’ll learn:
What AWS Well-Architected is and how to use it.
Amazing customer results from AWS Well-Architected.
What’s new — including a deep dive on Cloud Financial Management: Functional Ownership, Finance and Technology Partnership, Cloud Budgets and Forecasts, Cost-Aware Processes, Cost-Aware Culture, Quantifying Business Value Delivered Through Cost Optimization.
This session is most appropriate for CCoE lead, cloud infrastructure management, DevOps engineering management, DevOps and cloud practice leads, chief architects, solutions architects.
Real Testing Scenario Strategy - The Role of Exploratory TestingAdam Sandman
• Where to use exploratory testing
• Tools you can use (capture tools like bugreplay), tracking tools like SpiraTest’s new exploratory mode, etc.
• How to fit it into your sprint plan and best practices to working with developers to identify and fix issues found in exploratory testing.
Datadog: From a single product to a growing platform by Alexis Lê-Quôc, CTOTheFamily
By Alexis (https://twitter.com/alq), CTO at Datadog (https://www.datadoghq.com)
Alexis built Datadog's whole infrastructure and team from scratch as a co-founder. From a very small & dedicated team with no experience, he learned step by step to build a complete product ️
He shared with us his experience as a co-founder and CTO building a cloud giant in New York. How do you keep learning, how do you interact with customers & your market to drive your product development, and how do you monitor it all to make you company evolve will be the main topics of his talk.
A differnt Type of Supermarket DeliveryThoughtworks
In a world of seamless deployments to auto-scaling cloud environments, a ThoughtWorks team found itself in a very different place - trying to deploy a RESTful pricing API to every one of a UK supermarket’s 40,000 tills in a reliable, repeatable fashion.
SeleniumCamp 2020 - Shift Right and ObservabilityMarcus Merrell
SeleniumCamp 2020 presentation about the rise of observability, risk management, and weaving testing concepts throughout the SDLC. Examples of using analytics to increase confidence in your release cycle
This talk will focus on Techniques, metrics and different tests (code, models, infra and features/data) that help the developers of machine learning systems to achieve CD.
Your Testing Is Flawed: Introducing A New Open Source Tool For Accurate Kuber...StormForge .io
Complimentary Live Webinar
Sponsored by StormForge
Analyzing the performance and behavior of applications run on Kubernetes is often challenging, making the need to optimize prior to production something that you must have. However, a problem has reared its head in the form of a question: How do you get an accurate measurement of application performance or other behavior without accurate testing or an accurate representation of how it will run in production? In this webinar, we will present and discuss a new fully Open Source tool for creating the needed tests with which to accurately measure your applications. We hope you will join us to learn more about this tool, and find out how you can help contribute.
This webinar is sponsored by StormForge and hosted by The Linux Foundation.
Speaker
Noah Abrahams, Open Source Advocate
Noah is an Open Source Advocate for StormForge, merging Open Source Strategy with Developer Advocacy. He has been involved in cloud for over 12 years, has been contributing to the Kubernetes ecosystem for 5 years, and has been up and down the business stack from DevOps and Architecture to Sales, Enablement, and Education. You will find him running meetups in Las Vegas and attending conferences, once those are both happening again.
Continuous Behavior - BDD in Continuous Delivery (CoDers Who Test, Gothenburg...Gáspár Nagy
Abstract: This session is about my experience with BDD in a Continuous Delivery model. Many teams use Behavior Driven Development (BDD) for automated UI testing. Although UI testing might be an important element of your verification pipeline, it is slow, brittle and costly. But in the era of modern software development we would like to have fast feedback and quick reactions. A continuous delivery / deployment (CD) model can support this well. But what does it look like with BDD then? Can BDD help implementing CD?
This session is an attempt to define the role of BDD in CD. What works, what does not? Continuous delivery should mean continuous quality for the delivery team. What kind of quality criteria do we want to target with CD and how is it supported by BDD?
We are sinking: Hitting the testing iceberg (CukenFest London, 2018)Gáspár Nagy
At the session I am going to show you the concept of the testing iceberg through a concrete example. We will see how the "undersea" tests can support the ones on the tip, focusing on business requirements. We will see how you can move tests between the different levels so that finally we get more insight how the testing pyramid can be used in your own projects.
Can i service this from my raspberry piThoughtworks
Infrastructure-related skills are essential for developers in cross-functional teams who build microservices for the cloud. Becoming proficient in infrastructure development is not just about understanding the hardware and software components on top of which applications run in the cloud. It's also about being able to use the tools that provide virtual access to this infrastructure and enable us to provision, configure, monitor it, and deploy applications to it. In this talk Gesa shares how building a Kubernetes cluster of Raspberry Pis and serving applications from it can help in acquiring fundamental infrastructure skills.
If operations is a classic big data problem, cloud Operations is a *huge* data problem. We all understand the volume of logs, alerts and metrics generated by SaaS applications, and the increasing complexity of hybrid infrastructure, requires you to step up your monitoring strategy and just like any other big data problem – it only makes sense to leverage AI to achieve the observability imperative.
Taking into consideration the sheer volume of IT monitoring data that you have to deal with each and every day as DevOps, IT or SRE- leveraging traditional, reactive monitoring tools and approaches wont cut it much longer. Infusing AI is not about magically identifying and automatically solving all your problems, but given the criticality of delivering a phenomenal user experience for SaaS- you can leverage machine learning models to provide you with insights-rather than data- to not only effectively detect abnormal behaviors but also to predict potential issues, map them to associated services and help you intelligently prioritize preventive, troubleshooting and remediation efforts.
Building and operating a global cloud infrastructure at a large scale is a complex task with hundreds of ever-evolving service components. I am happy to share with you some real-world examples of how AI is leveraged at Azure Marketplace and Linkedin scale to monitor wisely, predict capacity and save costs so you can think how you can take it home, and apply it in your production environments.
Connecting the clouds, A TrueLime StoryJeroen Fürst
In this business case study, Jeroen shares his journey on how various microservices have been integrated, resulting in a rich and lightweight fresh new company blog.
With latest technologies evolving like Machine Learning, QA's must know the right strategy to test applications as AI apps like Personal Assistants, Smart Cars have direct impact in our life.
Reviewing progress in the machine learning certification journey
𝗦𝗽𝗲𝗰𝗶𝗮𝗹 𝗔𝗱𝗱𝗶𝘁𝗶𝗼𝗻 - Short tech talk on How to Network by Qingyue(Annie) Wang
C𝗼𝗻𝘁𝗲𝗻𝘁 𝗿𝗲𝘃𝗶𝗲𝘄 𝗼𝗻 AI and ML on Google Cloud by Margaret Maynard-Reid
𝗔 𝗳𝗼𝗰𝘂𝘀𝗲𝗱 𝗰𝗼𝗻𝘁𝗲𝗻𝘁 𝗿𝗲𝘃𝗶𝗲𝘄 𝗼𝗻 𝗠𝗟 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 𝗳𝗿𝗮𝗺𝗶𝗻𝗴, 𝗺𝗼𝗱𝗲𝗹 𝗲𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻, 𝗮𝗻𝗱 𝗳𝗮𝗶𝗿𝗻𝗲𝘀𝘀 by Sowndarya Venkateswaran.
A discussion on sample questions to aid certification exam preparation.
An interactive Q&A session to clarify doubts and questions.
Previewing next steps and topics, including course completions and material reviews.
implementing_ai_for_improved_performance_testing_the_key_to_success.pdfsarah david
Experience a revolution in software testing with our AI-driven Performance Testing solutions at Cuneiform Consulting. In a world dominated by technological advancements, implementing AI is the key to unlocking unparalleled software performance. Boost your applications with speed, scalability, and responsiveness, ensuring a seamless user experience. Cuneiform Consulting leads the way in reshaping quality assurance, adhering to the predictions of the World Quality Report for AI's significant role in the next decade. Join us to stay ahead, save costs with constant AI-powered testing, and explore the boundless possibilities of AI/ML development services. Contact us now for a future-proof digital transformation!
Reproducibility and experiments management in Machine Learning Mikhail Rozhkov
Machine Learning becomes more and more common practice in many companies. ML teams size is growing and collaboration goes out of office and personal laptops. The complexity of ML projects leads to adopting distributed team collaboration, cloud based infrastructure and distributed machine learning. Well defined and manageable process for ML experiments becomes a central issue. Practices to apply automated pipelines, models and data set versioning helps to establish a good manageable process in project and provide reproducible results.
This speech helps to start with handling models and datasets versioning using open source tools: DVC, mlflow, Luigi, etc.
MLOps and Data Quality: Deploying Reliable ML Models in ProductionProvectus
Looking to build a robust machine learning infrastructure to streamline MLOps? Learn from Provectus experts how to ensure the success of your MLOps initiative by implementing Data QA components in your ML infrastructure.
For most organizations, the development of multiple machine learning models, their deployment and maintenance in production are relatively new tasks. Join Provectus as we explain how to build an end-to-end infrastructure for machine learning, with a focus on data quality and metadata management, to standardize and streamline machine learning life cycle management (MLOps).
Agenda
- Data Quality and why it matters
- Challenges and solutions of Data Testing
- Challenges and solutions of Model Testing
- MLOps pipelines and why they matter
- How to expand validation pipelines for Data Quality
Tutorial for Machine Learning 101 (an all-day tutorial at Strata + Hadoop World, New York City, 2015)
The course is designed to introduce machine learning via real applications like building a recommender image analysis using deep learning.
In this talk we cover deployment of machine learning models.
Integrating AI in software quality in absence of a well-defined requirementsNagarro
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.
For a company like Blue Apron that is radically transforming the way we buy, prepare and eat meals, experimentation is mission critical for delivering a great customer experience. Blue Apron doesn’t just think about experimenting to improve short term conversion, they focus on ways to impact longer term metrics like retention, referrals, and lifetime value.
Join John Cline, engineering manager at Blue Apron, to learn how his team has built their experimentation program on Optimizely’s platform.
Attend this webinar to learn:
-How Blue Apron built their experimentation program on top of Optimizely Full Stack
-How developers play a critical role in experimentation
-The key considerations for developers when thinking about experimentation
Data Con LA 2022 - Why Data Quality vigilance requires an End-to-End, Automat...Data Con LA
Curtis ODell, Global Director Data Integrity at Tricentis
Join me to learn about a new end-to-end data testing approach designed for modern data pipelines that fills dangerous gaps left by traditional data management tools—one designed to handle structured and unstructured data from any source. You'll hear how you can use unique automation technology to reach up to 90 percent test coverage rates and deliver trustworthy analytical and operational data at scale. Several real world use cases from major banks/finance, insurance, health analytics, and Snowflake examples will be presented.
Key Learning Objective
1. Data journeys are complex and you have to ensure integrity of the data end to end across this journey from source to end reporting for compliance
2. Data Management tools do not test data, they profile and monitor at best, and leave serious gaps in your data testing coverage
3. Automation with integration to DevOps and DataOps' CI/CD processes are key to solving this.
4. How this approach has impact in your vertical
implementing_ai_for_improved_performance_testing_the_key_to_success.pptxsarah david
Experience a revolution in software testing with our AI-driven Performance Testing solutions at Cuneiform Consulting. In a world dominated by technological advancements, implementing AI is the key to unlocking unparalleled software performance. Boost your applications with speed, scalability, and responsiveness, ensuring a seamless user experience. Cuneiform Consulting leads the way in reshaping quality assurance, adhering to the predictions of the World Quality Report for AI's significant role in the next decade. Join us to stay ahead, save costs with constant AI-powered testing, and explore the boundless possibilities of AI/ML development services. Contact us now for a future-proof digital transformation!
Machine Learning has become a must to improve insight, quality and time to market. But it's also been called the 'high interest credit card of technical debt' with challenges in managing both how it's applied and how its results are consumed.
Learn statistics and expert opinions on the state of the market regarding data quality in 2023.
Learn about:
- statistics and expert opinions
- the key focus of data quality in 2023
- the Data Maturity Model
- DevOps for data and CI/CD pipelines
- data validation and ETL testing
- test automation
To Open Banking and Beyond: Developing APIs that are Resilient to every new I...Curiosity Software Ireland
Watch the live webinar on-demand here -
https://curiositysoftware.ie/resources/to-open-banking-and-beyond-api-testing-free-webinar/
With over 35 APIs involved in an average business transaction, API innovation is critical for every organisation. However, microservices and fast-changing components can quickly create overwhelming complexity for architects, developers, and testers. They produce complex arrays of API calls, often leading to QA bottlenecks – or, worse, business-critical systems that have been released with undetected flaws in their APIs.
APIs often also work with sensitive data, making it essential to remove risk from API releases. Otherwise, initiatives like Open Banking can turn from an opportunity to a compliance nightmare. The challenge is that the time available to test APIs is only becoming shorter, while the complexity of the systems is increasing.
API testing must become both more iterative and more granular. This webinar will show why enterprises across banking, retail, telecoms, and more are combining Model-Based Testing and API Test Automation to overcome API complexity. You will see how Test Modeller builds rigorous API tests automatically in-sprint, pushing them to API Fortress for execution. This approach enables QA teams to ensure that APIs deliver business value, building seamlessly on the skills and techniques they use today.
Key takeaways:
1. Organizations investing in APIs must maintain API resilience, reliability, performance, and security.
2. Companies can significantly decrease risk while accelerating releases by combining model-based testing with complete test data management.
3. Test Modeller enables model-based API test automation, using coverage algorithms to create functional and data-driven API tests.
4. Testers can reuse functional API tests in API Fortress as integration tests, load tests, and functional uptime monitors with unlimited deployment and no metered usage fees.
DataTalkClub Conference, Feb 12 2021
Creating a machine learning model is not an easy task.
Creating a useful machine learning model that gets into production and generates actual business value - is an even harder one.
There are many ways for an ML project or product to fail even when the data is there and the model technically performs well. From the wrong problem statement to lack of trust from stakeholders, in this talk I will discuss what issues to look out for, and how to avoid them.
Scale your Testing and Quality with Automation Engineering and ML - Carlos Ki...QA or the Highway
Many teams and organizations struggle to scale their quality and testing strategies once they reach tens of teams and hundreds of developers and services across their systems. Traditional strategies and techniques, like testing phases and code freezes, do not work at scale and quickly add friction, reduce productivity, and make testing and quality harder.
In this presentation, we will cover different ideas and strategies to make things like BDD and TDD easier to adopt at the beginning, how to include observability and operability in your definition of quality, and how leveraging ML/AI can augment your devs and testers and reduce risk while accelerating value.
By the end, you will have some "low quality" indicators that you can use to identify patterns and practices that won\'t scale well. You will have new insights and ideas for how you can set up your teams and strategies for success long term, and you will see tangible, practical examples you can take to your team and company to start this transformation now.
Model Monitoring at Scale with Apache Spark and VertaDatabricks
For any organization whose core product or business depends on ML models (think Slack search, Twitter feed ranking, or Tesla Autopilot), ensuring that production ML models are performing with high efficacy is crucial. In fact, according to the McKinsey report on model risk, defective models have led to revenue losses of hundreds of millions of dollars in the financial sector alone. However, in spite of the significant harms of defective models, tools to detect and remedy model performance issues for production ML models are missing.
Based on our experience building ML debugging and robustness tools at MIT CSAIL and managing large-scale model inference services at Twitter, Nvidia, and now at Verta, we developed a generalized model monitoring framework that can monitor a wide variety of ML models, work unchanged in batch and real-time inference scenarios, and scale to millions of inference requests. In this talk, we focus on how this framework applies to monitoring ML inference workflows built on top of Apache Spark and Databricks. We describe how we can supplement the massively scalable data processing capabilities of these platforms with statistical processors to support the monitoring and debugging of ML models.
Learn how ML Monitoring is fundamentally different from application performance monitoring or data monitoring. Understand what model monitoring must achieve for batch and real-time model serving use cases. Then dig in with us as we focus on the batch prediction use case for model scoring and demonstrate how we can leverage the core Apache Spark engine to easily monitor model performance and identify errors in serving pipelines.
Over the last few years, Appium has become the automation tool of choice for mobile application UI testing. Advanced Appium Workshop is a hands on session, which will help people to write cross platform tests with gestures and locator-chaining using PageFactory. Workshop would also focus on iOS 10 using XCUITest backend with the latest appium java-client.Participants need to have knowledge of Appium architecture, ability to write basic test for android/iOS and knowledge of OOPS concepts.
As a part of the software industry, it is a basic necessity to create a secure application/product. Security testing is not only about hacking, and can be approached in a structured manner. This presentation will help you understand how to incorporate security in different phases and aspects of software development.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
2. AGENDA
Intro + Quick agenda walkthrough(brief talk)
a. What is AI/ML
b. How technology is shifting towards AI, ML
c. Where does a QA step in
d. Challenges while testing AI,ML application
Hands-ON Activity:
1. Create and Test a basic Beer-Wine Classifier
1. Create an Image Classifier ( via CLI )
a. Retrain a Mobile Net
b. Generate test data
c. Create Optimized graphs
d. Test you classifier
1. Dynamic Image Classifier via Android App - (OPTIONAL)
a. Retrain a Mobile Net
b. Generate test data
c. Create Optimized graphs
d. Test you classifier
2
5. “Machine learning is an application
of artificial intelligence (AI) that
provides systems the ability to
automatically learn and improve
from experience without being
explicitly programmed“
5
14. ● Label: Is what you're attempting to predict or forecast
● Features: are an individual measurable property OR the descriptive attributes
● Feature Vectors: A feature vector is a vector in which each dimension represent a certain
feature of an example
● Learning Rate: number of time data is reread in a model to perform accurate predictions.
● Hyperparameters : is a parameter whose value is set before the learning process begins to fine
tune performance such as coefficient of features for logistic regression model.
Frequent terms used in ML
1
4
18. Training data Vs Test data
● Training set— Data subset to train a model
● Test set— Data subset to test the trained model
You could imagine slicing the single data set as follows:
1
8
24. Testing the feature
● Test whether the value of features lies between the threshold values
● Test whether the feature importance changed with respect to previous QA run
● Test the feature unsuitability by testing RAM, usage, inference latency etc.
● Test/Review whether the generated feature violates the data compliance related issues
2
4
26. It depends on application type.
Examples :
● Decision tree → classification
● Random forest → categorization
● Naive bayes algorithm → classification
APIs of few libraries used to develop/test ML models
● Tensorflow
● Cloud Vision API
● Natural Language
● Google Speech
Some algorithmic models
2
6
36. Precision
Out of all the predictions predicted as beer , how many are correctly classified as beer ?
True Positive +False Positive
True Positive
3
6
37. Recall
Out of all the drinks labeled as beer , How many were correctly predicted ?
True Positive
True Positive +False Positive
3
7
38. Metrics used for Regression Model
● Root Mean Square Error : is a measure of accuracy, to compare forecasting errors of different
models for a particular dataset and not between datasets
● Mean Absolute Error : how much % error the model makes in its predictions.
● Entropy : is used as an impurity measure of the model.
3
8
40. Challenges in testing
● Fast machines and processors
● Generate training data
● Generate test Data
● Know the Threshold and test with new data
● Data Filtering/quality of data - Enhancing data, Prevent overfitting & underfitting
4
0
41. PREREQUISITES
Please complete all the following steps:
● Clone all the following repositories at local:
a. https://github.com/tarunmaini16/beer-wine-classifier
b. https://github.com/tarunmaini16/image-classifier
c. https://github.com/tarunmaini16/android-image-classifier
● Pull following docker images (optional):
a. https://cloud.docker.com/u/tarunmaini/repository/docker/tarunmaini/wine-beer-classification
b. https://cloud.docker.com/u/tarunmaini/repository/docker/tarunmaini/image-classifier
● Install Python at system and python plugin in IntelliJ
● Install Tensorflow via terminal $ pip install --upgrade “tensorflow==1.9*”
● Android Studio Setup [v3.1+]
● Android Device OR Virtual Emulator ( API Level = 27/28, Target = Android 8.1/9 )
● Bring your data Cables to connect mobile device
● ADB setup
41
Machine Learning is the field of study that gives computers the ability to learn without being explicitly programmed.” - Arthur Samuel, 1959
Machine Learning is the science of programming computers so they can “learn from data”
A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E. - Tom Mitchell, 1997
Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed
Machine learning is a form of AI that enables a system to learn from data rather than through explicit programming. However, machine learning is not a simple process. As the algorithms ingest training data, it is then possible to produce more precise models based on that data. A machine learning model is the output generated when you train your machine learning algorithm with data. After training, when you provide a model with an input, you will be given an output. For example, a predictive algorithm will create a predictive model. Then, when you provide the predictive model with data, you will receive a prediction based on the data that trained the model.
Social Networking: FB automatically recognises faces suggests to tag a friend.
Banking / Finance: Fraud detection algorithms to classify fraudulent transactions are in place.
Mobile:
-Personal Assistants
-Voice to text
-Technology
Online Shopping: Recommendations of similar products
Search Engines: Google’s autocomplete suggestions for search
Medicine : Researches on using ML for disease diagnosis. - Google’s DeepMind Health
Machine learning has the potential to automate a large portion of skilled labor, but the degree to which this affects a workforce depends on the level of difficulty involved in the job.
Education :
1.)Algorithms can analyze test results, drastically reducing the time teachers spend in their leisure time on grading
2.)A student's attendance and academic history can help determine gaps in knowledge and learning disabilities.
Law:
J.P. Morgan, for example, uses a software program dubbed COIN (Control Intelligence) to review documents and previous cases in seconds that would otherwise take 360,000 hours.
Transportation :
1.)
Rolls Royce and Google have teamed up to design and launch the world's first self-driving ship by 2020.
2.)NASA having successfully launched and landed an autonomous space shuttle
Manual Labour :
driverless trucks operating in mining pits in Australia, operated remotely from a distant control center.(particular jobs that involve some element of danger or potential harm, such as work in factories and mining)
Healthcare:
Hospitals are currently using AI algorithms to more accurately detect tumors in radiology scans and analyze different moles for skin cancer, and machine learning is being adapted to accelerate research toward a cure for cancer.
Alexa:
voice-activated control of your smart-home (the dimming of lights, closing of blinds, locking of doors, etc., all at your command).
Supervised: Supervised learning identifies patterns in data given pre-determined features and labeled data.
Unsupervised: Unsupervised learning identifies patterns in data, which is particularly helpful for unlabeled and unstructured data.
Semi-supervised: A blend of supervised and unsupervised learning. Best in situations where there is some labeled data but not a lot.
Reinforcement: Reinforcement learning provides feedback to the algorithm as it trains; it is essentially experience-driven decision
Typical business uses of supervised learning include recognizing objects in images, predicting financial results, detecting fraud, and evaluating risk.
Unsupervised : Categorizing news, books, and other things, recommending items to customers.
Semi : detecting spam, classifying web-content, and analyzing speech
Color
Taste (differs with acidity /Alcoholic content)
https://semanti.ca/blog/?glossary-of-machine-learning-terms
A feature vector is a one dimensional matrix which is used to describe a feature of an image. It can be used to describe an entire image (Global feature) or a feature present at in a location in the image space (local feature)
Bias
The bias is an error from erroneous assumptions in the learning algorithm. High bias can cause an algorithm to miss the relevant relations between features and target outputs (underfitting)
Only thing in the process that has human intervention
Gathering data
Preparing that data
Choosing a model
Training
Evaluation
Hyperparameter tuning
Prediction.
Things to explain here:
The approach to Development and testing isn’t traditional here . But does that mean NO QA for ML applications ?
NO, the answer is being adaptive enough to learn how to test those predictions and as of now due to lack to knowledge Data Scientist develop + test the models that they create .
Which in long term will not work when applications scale.
So now , the problem at hand is
How do we test predictions ![ The challenge for QA ]
Gathering data
Preparing that data
Choosing a model
Training
Evaluation
Hyperparameter tuning
Prediction.
Training Set : 80% , Test Data : 20%Make sure that your test set meets the following two conditions:
Is large enough to yield statistically meaningful results.
Is representative of the data set as a whole. In other words, don't pick a test set with different characteristics than the training set.
Never train on test data. If you are seeing surprisingly good results on your evaluation metrics, it might be a sign that you are accidentally training on the test set. For example, high accuracy might indicate that test data has leaked into the training set.
For example, consider a model that predicts whether an email is spam, using the subject line, email body, and sender's email address as features. We apportion the data into training and test sets, with an 80-20 split. After training, the model achieves 99% precision on both the training set and the test set. We'd expect a lower precision on the test set, so we take another look at the data and discover that many of the examples in the test set are duplicates of examples in the training set (we neglected to scrub duplicate entries for the same spam email from our input database before splitting the data). We've inadvertently trained on some of our test data, and as a result, we're no longer accurately measuring how well our model generalizes to new data.
Data snooping bias:
Test set have to be created immediately after receiving the dataset. Otherwise as
humans we derive a pattern around all the data and there is a possibility of bias while
training the model, which is called as the ‘data snooping bias’.
A validation dataset is a dataset of examples used to tune the hyperparameters (i.e. the architecture) of a classifier. It is sometimes also called the development set or the "dev set". In artificial neural networks, a hyperparameter is, for example, the number of hidden units.[7][8] It, as well as the testing set (as mentioned above), should follow the same probability distribution as the training dataset
In the figure, "Tweak model" means adjusting anything about the model you can dream up—from changing the learning rate, to adding or removing features, to designing a completely new model from scratch. At the end of this workflow, you pick the model that does best on the test set.
Dividing the data set into two sets is a good idea, but not a panacea. You can greatly reduce your chances of overfitting by partitioning the data set into the three subsets shown in the following figure:
Use the validation set to evaluate results from the training set. Then, use the test set to double-check your evaluation after the model has "passed" the validation set. The following figure shows this new workflow:
In this improved workflow:
Pick the model that does best on the validation set.
Double-check that model against the test set.
Things to explain here:
The approach to Development and testing isn’t traditional here . But does that mean NO QA for ML applications ?
NO, the answer is being adaptive enough to learn how to test those predictions and as of now due to lack to knowledge Data Scientist develop + test the models that they create .
Which in long term will not work when applications scale.
So now, the problem at hand is
How do we test predictions ![ The challenge for QA ]
<Show this data to tarun-k>
Some of the ways of generating data are:
E.g In Linear Regression make_regression() takes several inputs as shown in the example above. The inputs configured above are the number of test data points generated n_samples the number of input features n_features and finally the noise level noise in the output date
* * what was this star for - divya?
In Clustering - make_blobs() from sklearn can be used to clustering data for any number of features n_features with corresponding labels
Underfitting:
A statistical model or a machine learning algorithm is said to have underfitting when it cannot capture the underlying trend of the data. (It’s just like trying to fit undersized pants!) Underfitting destroys the accuracy of our machine learning model. Its occurrence simply means that our model or the algorithm does not fit the data well enough. It usually happens when we have less data to build an accurate model and also when we try to build a linear model with a non-linear data. In such cases the rules of the machine learning model are too easy and flexible to be applied on such a minimal data and therefore the model will probably make a lot of wrong predictions. Underfitting can be avoided by using more data and also reducing the features by feature selection.
Overfitting:
A statistical model is said to be overfitted, when we train it with a lot of data (just like fitting ourselves in an oversized pants!). When a model gets trained with so much of data, it starts learning from the noise and inaccurate data entries in our data set. Then the model does not categorize the data correctly, because of too much of details and noise. The causes of overfitting are the non-parametric and non-linear methods because these types of machine learning algorithms have more freedom in building the model based on the dataset and therefore they can really build unrealistic models. A solution to avoid overfitting is using a linear algorithm if we have linear data or using the parameters like the maximal depth if we are using decision trees.
What do u think was involved in building this algo ?
Take is as your mind reads information after it has been fed similar information !
In this exercise, we will retrain a MobileNet. MobileNet is a a small efficient convolutional neural network. "Convolutional" just means that the same calculations are performed at each location in the image.
Tensorflow: is used for acquiring data, training models, serving predictions, and refining future results
Cloud Vision API provides a REST API to understand and extract information from an image. It uses powerful machine learning models to classify images into thousands of categories, detect faces, identify adult content, emotions, OCR support and more.
Natural Language API is used to identify parts of speech and to detect multiple types of entities like persons, monuments, etc. It can also perform sentiment analysis. It currently supports three languages: English, Spanish and Japanese
Speech API is used to translate audio files into text. It is able to identify over 80 languages and their variants, and can work with most audio files
Description
TensorFlow is an open-source software library for dataflow and differentiable programming across a range of tasks. It is a symbolic math library, and is also used for machine learning applications such as neural networks
TensorFlow can train and run deep neural networks for handwritten digit classification, image recognition, word embeddings, recurrent neural networks, sequence-to-sequence models for machine translation, natural language processing, and PDE (partial differential equation) based simulations. Best of all, TensorFlow supports production prediction at scale, with the same models used for training
TensorFlow allows developers to create dataflow graphs—structures that describe how data moves through a graph, or a series of processing nodes. Each node in the graph represents a mathematical operation, and each connection or edge between nodes is a multidimensional data array, or tensor.
-wher edoes it come from
Used for?
Wherwe using it* *
Decision trees can be applied to both classification & regression tasks.
For regression task, decision trees use the MSE instead of gini score.
Scikit uses CART Algorithm to grow decision trees.
Main issue with Decision trees is the sensitivity to change in training data
--------------Random Forest ------------------
Random forest is an ensemble of Decision trees.
Instead of searching for the best feature to split a node, it searches for the best feature among a random subset of features, thus introducing more randomness hence less bias.
Important quality of Random Forests is that they make it easy to measure the relative importance of a feature.
It takes the features which reduces impurity on average to grow trees.
------Naives Bayes-----------
Random forest is an ensemble of Decision trees.
Instead of searching for the best feature to split a node, it searches for the best feature among a random subset of features, thus introducing more randomness hence less bias.
Important quality of Random Forests is that they make it easy to measure the relative importance of a feature.
It takes the features which reduces impurity on average to grow trees.
Things to explain here:
The approach to Development and testing isn’t traditional here . But does that mean NO QA for ML applications ?
NO, the answer is being adaptive enough to learn how to test those predictions and as of now due to lack to knowledge Data Scientist develop + test the models that they create .
Which in long term will not work when applications scale.
So now , the problem at hand is
How do we test predictions ![ The challenge for QA ]
If you specify a small learning_rate, like 0.005, the training will take longer, but the overall precision might increase
For example,
'mobilenet_1.0_224' will pick a model that is 17 MB in size and takes 224
pixel input images, while 'mobilenet_0.25_128_quantized' will choose a much
less accurate, but smaller and faster network that's 920 KB on disk and
takes 128x128 images
Should we talk about F-beta score ?
When False positives are ok and False negatives are NOT ok → use precision .. like you cannot tell a sick person that he is healthy . But you may tell a person that healthy person is sick and needs re-test
When False negatives are OK but False positives are not ok .Then use recall .
Eg. If Important mail goes to spam is wrong .
Spam mail in inbox might be ok .
RMSE: In meteorology, to see how effectively a mathematical model predicts the behavior of the atmosphhere. This is type regression
So, we have data for which we are trying to achieve a prediction/output and we have to chose the best model/ algorithm to achieve accurate prediction . So , we evaluate the model using the different metrics