The possibilities of Machine Learning models are almost limitless. Ranging from predicting customer churn over determining the right discount to maintaining your machines using predictive maintenance. The outcome of a machine learning model is often straightforward: a zero or one in classification problems, a continuous number in case of a regression problem. However, companies might also be interested in understanding how ‘the number’ […]
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ChatGPT3: the future of natural language processing is here!
Are you looking for a way to improve your natural language processing systems? Look no further than ChatGPT3, the latest and most advanced language model from OpenAI. In this blog post, we will introduce ChatGPT3 and explain its capabilities and potential uses. Whether you’re a business owner, a developer, or simply someone who is interested […]
Architecture behind the Qatar World Cup 2022 model
We explain the steps we took to predict the winner of the Qatar World Cup 2022 in this blog. The concepts covered include the various types of source data, the machine learning model, and the architecture used to obtain our results. Furthermore, we will go over and interpret the results in greater depth. Source data […]
Connecting and Using MS Graph in Azure Data Factory
Companies are creating more and more data, on which they want to gain insights. One valuable source of data is data from within the company itself, from the companies’ structure. For this type of data, the MS Graph API is something to look at. The API provides a single endpoint to access all kinds of […]
Loading mechanisms – Part I
As there are huge amounts of data available within companies, data is also moved in increasing quantities from one data storage to another for multiple reasons. As copying data can come with a longer load time and higher costs, you want to make this process as efficient as possible. Luckily, there are multiple loading mechanisms […]
Feature Store
Everyone who has already come in touch with data science, has already heard of features used in such models. One aspect that can become quite challenging, is reusing features in a consistent way, across several team members, projects and in environments. In this article, I will explain the most commonly used way to resolve these […]
Kimball in a data lake? Come again?
Most companies are already familiar with data modelling (be it Kimball or any other modelling technique) and data warehousing with a classical ETL (Extract-Transform-Load) flow. In the age of big data, an increasing number of companies are moving towards a data lake using Spark to store massive amounts of data. However, we often see that […]
Pandas, Koalas and PySpark in Python
If you landed on this page to learn more about animals, I have to disappoint you. Pandas, Koalas and PySpark are all packages that serve a similar purpose in the programming language Python. Python has increasingly gained traction over the past years, as illustrated in the Stack Overflow trends. Originally designed as a general purpose […]
Transfer learning in Spark for image recognition
Transfer learning in Spark demystified in less than 3 minutes reading Businesses that want to classify a huge set of images in batch per day can do this by leveraging the parallel processing power of PySpark and the accuracy of models trained on a huge set of images using transfer learning. Let’s first explain the […]
How ALM streamlines BI projects: Azure DevOps
Application Lifecycle Management (ALM) refers to a (software) development process which has been setup in a governed and easy-to-manage way. ALM provides added value to the development team, project managers and the business users. While ‘ALM’ is mostly coined by pure software development projects (…written in 100% programming languages), BI projects (which are by nature […]
