In the world of machine learning, data takes centre stage. It’s often said that data is the key to success. In this blog post, we emphasise the significance of data, especially when building a comment classification model. We will delve into how data quality, quantity, and biases significantly influence machine learning model performance. Additionally, we’ll explore techniques like undersampling as […]
Data quality matters when building and refining a Classification Model
Build a semantic search tool using FAISS
This post provides an overview of implementing semantic search. Why? Because too often, we notice testers skip testing more complex features like autocomplete. This might be ok in most applications. But in domain specific applications, testing autocomplete capabilities of the product is important. Since testers can benefit from understanding implementation details, in this post, we will look at how autocomplete […]
Robustness Testing of Machine Learning Models
In the world of machine learning, assessing a model’s performance under real-world conditions is important to ensure its reliability and robustness. Real-world data is usually not perfect, it may contain messy data or data with noise, outliers, and variations. During model training, these types of data could be limited, and the model may not have received sufficient training to handle […]
Context-based question answering using LLM
Companies are going to want to query their own internal documents – especially with the rise of LLMs and improvements in AI. Qxf2 has already heard of several CEOs that want to use AI/ML models to glean insights from internal knowledge stores. What does this mean for a tester? Well, you can expect to test such systems in the coming […]
Baseline Model Comparison for Performance Evaluation
Machine learning models evolve. As a tester, how do we know the newer version of the model is better? How do we know that the model did not get worse in other areas? The most intuitive approach would be to design a ‘good’ labelled dataset and then calculate the evaluation score like the F1 score for the model under test. […]
Testers, practice testing a machine learning algorithm!
Testers, you now have an app you can use to practice testing a machine learning algorithm. We have written and hosted a simple sentence classifier for testers to practice testing a machine learning algorithm. Play with it and let us know what fun bugs you discover. Hint: Testing this app is fun because there are several bugs that are not […]
Identify tech words from a text
As part of the pairing project activity at Qxf2 – pick a project that can produce a meaningful output in 5 hours and work on it collaborating with your team mates, I picked this project to identify the tech keywords from a text using NLTK module. This post covers the steps I followed to find the tech related words in […]
Scraping websites using Octoparse (no programming!)
Did you know you can scrape data from webpages without writing a single line of code? In this post, we will talk about a tool called Octoparse. We used Octoparse to scrape data from a list of URLs, without any coding at all. Data is valuable and it’s not always easy to get the correct data from the web sources […]
Organize and edit your test data with Quilt
We recently stumbled upon Quilt, a data package manager that wants to fill the role of ‘Github for data’. We have enjoyed using it so far. We think it can be useful for testers to manage their test data using Quilt. In this post, we will take an example dataset and show you step by step guide on how to […]
Quilt – a Data Package Manager
We have been testing data-rich applications for a long time. And like any experienced tester, we realize how difficult it is to create, maintain and update data every time the data model changes. So we were excited to come across Quilt, a data package manager, via Hacker News. We were thrilled that it integrated well with our favorite programming language […]