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Advocacy and Innovation

Azure Machine Learning Service for Kubernetes Architects: Deploy Your First Model on AKS with AZ CLI v2

Joseph Masengesho provides a step-by-step tutorial on how to deploy your first ML model on AKS. In a previous post, I provided a lengthy write-up about my understanding of using Kubernetes as a compute target in Azure ML from a Kubernetes architect’s perspective. In this post, I will offer a step-by-step tutorial that teaches you how ...

Machine Learning – Lessons from our POC

Using multiple algorithms and tuning the algorithms to find the optimum value for each parameter also improves the accuracy of the model. However, it is not necessary that higher accuracy models always give the accurate results, as sometimes, the improvement in model’s accuracy can be due to over-fitting too.

AI, ML & Data Science – Explained

Where would you find all three (AI, ML and DS) at work? The most common place today is in autonomous driving vehicles. All three disciplines work together to help train an algorithm to recognize obstacles (MS), then to provide real-time actions (AI) to the vehicle, all based on large amounts of information that data science (DS) can analyze.

Exploring Feature Weights using R and Azure Machine Learning Studio

I find that machine learning experiment’s results are always interesting and somewhat unexpected in certain cases. On this comparison, the feature ranking results of PFI are often different from the feature selection statistics that are utilized before a model is created. This is useful in many cases, especially when training “black-box” models where it is difficult to explain how the model characterizes the relationship between the features and the target variable.