Data Science & Data Engineering blogs
Thanks for reading. Here you will find a huge range of information in text, audio and video on topics such as Data Science, Data Engineering, Machine Learning Engineering, DataOps and much more. The show notes for “Data Science in Production” are also collated here.
Wondering how to create the best marketing strategy to reach out to different customer groups? Do you know which groups of customers are most likely to buy your product? Clustering your customers into segments based on profiles, behaviours and buying patterns is the answer. This article provides a great way to jump-start your clustering project using pre-built code designed by Databricks.
Read MoreDo you know what the 10 most commonly useful clustering algorithms are? If you wondering what they are, then this article is for you.
Read MoreThis article uses an easy and simple example to explain what clustering is and how it is being used in business to solve problems.
Read MoreDo you want to get started with computer vision but do not really know where to start? Or would you like to quickly get an idea as to which state-of-the-art (SoTA) computer vision model is best for your task? If you are, then this article will give you a good starting point to jumpstart your computer vision journey.
Read MoreAutomated Machine Learning, or AutoML, has gained a lot of popularity. Recently, cloud providers have introduced the capability of AutoML for computer vision tasks. In this article, we explore the concept of AutoML for vision and see how computer vision is revolutionising many industries.
Read More10 ways AI & Machine learning can be used to drive revenue in Insurance & Brokers.
Read MoreDo you know exactly how many of your customers are leaving? Do you know why your customers are leaving? Are you able to predict which customers will leave the service? This article will show you how to predict churn using Azure Synapse Analytics and Azure Machine Learning.
Read MoreFeature stores are rapidly gaining popularity in the machine learning environment. Find out what feature stores are all about and the benefits they offer when implemented in a machine learning pipeline.
Read MoreWhat do AI, ML and DL mean? How do they relate to each other? This article aims to explain the terms and the differences AI, ML and DL using an incredibly simple example.
Read MoreIn this blog, I want to take you through three different approaches that you can use to overcome the problem of outlier identification and in how you can resolve them in Spark 3.0.
Read MoreIf you look at the multitude of different ways that you can build a machine learning model today, you would be right to feel overwhelmed. You can build models in R, Python, ML.net etc. Once you have decided which language to build in, the range of different frameworks only adds to the problem. ONNX simplifies the process.
Read MoreWe’re thrilled to announce that both of our directors have been renewed as Microsoft Most Valuable Professionals - they would both like to share a quick message to the community!
Read MoreWhat is Microsoft BrainWave? Deep Learning Deployment at Scale on FPGAs.
Read More2019: Containers, K8s, DataOps and Applied AI. Where should you focus your time in 2019? Well here is a short list of areas you might want to invest in.
Read MoreWhy you should attend the #GlobalAIBootcamp
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