Our resources
Our data experts are happy to share their knowledge with the data community. You'll find the articles they've written on this page. These should help you to leverage the potential of your data. Would you like to know more? Please don't hesitate to reach out.
Whitepaper
Blog:
Essentials of an MLOps platform Part 2: Infrastructure
Machine learning has become an essential tool for businesses to optimize and automate processes. The term MLOps has already been around for some time now, but that does not mean that it is easy to imp…
Blog:
Essentials of an MLOps platform Part 2: Infrastructure
Machine learning has become an essential tool for businesses to optimize and automate processes. The term MLOps has already been around for some time now, but that does not mean that it is easy to imp…
Blog:
The essentials of an MLOps platform Part 1: Architecture
When building Machine Learning products, an important aspect is getting the foundation right. You'll need a scalable and solid architecture where these products can be easily deployed and maintained.
Blog:
Detecting Data Drift with Machine Learning
Data changes over time. This is often unpredictable and unannounced. These changes cause a model based on old data to be inconsistent with new data.
Whitepaper
Blog:
Should the energy crisis affect the developer agenda?
Everyone is talking about gas prices increasing, inflation, and the global effects of the current energy crisis.
Blog:
Should the energy crisis affect the developer agenda?
Everyone is talking about gas prices increasing, inflation, and the global effects of the current energy crisis.
Blog:
Explainable AI: Understanding the Decisions of a Convolutional Neural Network
How can we define the quality of a convolutional neural network (CNN)? A model’s performance at a given task is often measured by some (simple) predefined metrics.
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Testing Strategies for incomplete functionality
When working with code that is not fully functional yet, standard testing approaches in which inputs and expected outputs are defined, might not be sufficient.
Whitepaper
Blog:
Building a data platform: gotchas and best practices
Over the course of many different projects, we have built data platforms on every major cloud provider. While doing that, we learned about often overlooked issues and gotchas.
Blog:
Building a data platform: gotchas and best practices
Over the course of many different projects, we have built data platforms on every major cloud provider. While doing that, we learned about often overlooked issues and gotchas.
Blog:
Helping the customer: a start to NBA platforms
You’ve heard talk of Next-Best-Action (NBA) platforms and how great they are at improving customer engagement, like the airline that achieved an 800% uplift in satisfaction and a 60% reduction in chur…
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Advantages of Serverless Services for Servitization Teams
Imagine: you are working for a company that produces tires. For many years now, your company has been collecting sensor data from the previously sold tires.
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Two steps towards a modern data platform
When building Data Science products, an important aspect is getting your data available and ready to use. You’ll need a platform to bring data together and serve it across the company.
Blog:
Two steps towards a modern data platform
When building Data Science products, an important aspect is getting your data available and ready to use. You’ll need a platform to bring data together and serve it across the company.
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Staying in control of your IAM model
tldr; staying in control of your IAM model is hard, especially when it evolves over time. That is why we built and open sourced Mia.
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Staying in control of your IAM model
tldr; staying in control of your IAM model is hard, especially when it evolves over time. That is why we built and open sourced Mia.
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From DevOps to MLOps
DevOps has become an accepted practice in software engineering teams. We see a similar trend starting in the data science and data engineering domain, named MLOps.
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Understanding inverse propensity weighting
Today I’d like to explain the underlying concepts of Inverse Propensity Weighting (IPW). As a Machine Learning Engineer, it’s important to know how models and algorithms work.
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Data science is boring
TL;DR
Data scientists should become MLOps Engineer or an Analytics Translator.…
Blog:
Data science is boring
TL;DR
Data scientists should become MLOps Engineer or an Analytics Translator.…
Blog:
Automating Software Versioning on Kubernetes
The Kubernetes community is growing rapidly with many cool new open source projects popping up all the time.
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Architecting a workable, yet secure data exploration environment on the Google Cloud Platform
Having worked for multiple major organisations in the Netherlands, there's always this recurring topic: how to give data scientists as much freedom as possible for exploring data, while minimising the…
Blog:
Architecting a workable, yet secure data exploration environment on the Google Cloud Platform
Having worked for multiple major organisations in the Netherlands, there's always this recurring topic: how to give data scientists as much freedom as possible for exploring data, while minimising the…
Blog:
Kinesis Data Analytics SQL: a cautionary review
You use AWS. You need to perform real-time feature processing all while maintaining state. Could Amazon Kinesis Data Analytics be a solution to your problem? What about concerns such as testability, c…
Podcast:
Scaling ML capabilities in large organizations
A podcast on machine learning platforms: what are their features, which complexities arise around them and how to bring a machine learning model to production.
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Dealing with abrupt market changes in your analysis - a brief tutorial on time series change point detection
The Covid-19 crisis has an extraordinary effect on global economic activity. After this crisis it will remain important to take this period into account when training machine learning models on histor…
Blog:
Dealing with abrupt market changes in your analysis - a brief tutorial on time series change point detection
The Covid-19 crisis has an extraordinary effect on global economic activity. After this crisis it will remain important to take this period into account when training machine learning models on histor…
Blog:
Writing functional DSLs for business domains
In functional programming, a domain specific language (DSL) is a set of functions that can be composed to solve a specific problem.
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Improving the security of Data Science containers - Using Docker's seccomp profiles and Linux capabilities features
No one wants to be the person who exposed sensitive information through their container and caused a hefty GDPR fine, right? What then should data scientists do to improve the security of their contai…
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How to grow as a data science professional - introducing the Skill Stack
Professionals need to grow and develop their skills to advance in their career. That’s not different for a data scientist. There are various skills, all contributing to your impact on the project.
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How to grow as a data science professional - introducing the Skill Stack
Professionals need to grow and develop their skills to advance in their career. That’s not different for a data scientist. There are various skills, all contributing to your impact on the project.
Blog:
Machine learning models on AWS with the Rendezvous architecture
tl;dr Testing and updating machine learning models can be done safely and systematically using the Rendezvous architecture.
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AWS Lambda: Comparing Golang and Python
Serverless functions are great for lightweight cloud architecture and rapid provisioning. However, sometimes serverless introduces additional complexity to the deployment process.
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Hosting workshops on AWS using ECS, EC2 and Terraform
During workshops, I often see participants wrestle with software installation before they can get started.
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Hosting workshops on AWS using ECS, EC2 and Terraform
During workshops, I often see participants wrestle with software installation before they can get started.
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Preventing churn like a bandit - with uplift modeling, causal inference, and Thompson sampling
Blog:
Preventing churn like a bandit - with uplift modeling, causal inference, and Thompson sampling
The real goal is to prevent churn, not to predict churn. Thus, we predict the effect of treatments. The transformed outcome technique is helpful.
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A review of Netflix's Metaflow
tl;dr Metaflow is a framework that alleviates several infrastructure-related pains data scientists experience in their projects.
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On machine learning team composition
Getting machine learning off the ground requires many skills and capabilities. Some of these skills are related, some are not.
Blog:
On machine learning team composition
Getting machine learning off the ground requires many skills and capabilities. Some of these skills are related, some are not.
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For effective treatment of churn, don't predict churn
In the business to consumer market, there are two strategies to grow market share: gaining new customers, and retaining existing customers. The latter challenge is referred to as preventing churn.
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Advanced Pandas: Optimize speed and memory
Nowadays the Python data analysis library Pandas is widely used across the world. It started mostly as a data exploration and experimentation tool but is slowly transitioning to be used in a productio…
Blog:
You don't have enough Analytics Translators, here's why that's a problem
I often get asked the question ‘Why do AI projects fail?’ As a data science consultant, I’ve seen a variety of organizations struggle to make AI work for them.
Blog:
You don't have enough Analytics Translators, here's why that's a problem
I often get asked the question ‘Why do AI projects fail?’ As a data science consultant, I’ve seen a variety of organizations struggle to make AI work for them.
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From predictive to prescriptive analytics - the benefit of causal diagrams
Suppose you work at as a data scientist at a dating site. Recently more and more customers are closing their accounts (a.k.a. churning).
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Cost comparison of deep learning hardware: Google TPUv2 vs Nvidia Tesla V100
Google offered us a chance to test their new TPUv2 devices for free on Google Cloud as part of the TensorFlow Research Cloud program.
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Integrating Pandas and scikit-learn with Pipelines
Scikit-learn and Pandas are both great tools for explorative data science. Both require a bit of practice to get the hang of.
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Integrating Pandas and scikit-learn with Pipelines
Scikit-learn and Pandas are both great tools for explorative data science. Both require a bit of practice to get the hang of.
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Machine learning for predictive maintenance: where to start?
Think about all the machines you use during a year, all of them, from a toaster every morning to an airplane every summer holiday. Now imagine that, from now on, one of them would fail every day.