AIOps with Azure Metrics Advisor 
AIOps with Azure Metrics Advisor  AIOps is a term introduced by Gartner in 2016, referring to AI for IT Operations. It combines big data and machine learning to automate IT operations processes, including event correlation, anomaly detection and causality determination. As more companies adopt digital transformation initiatives,... Read more
4 Techniques To Deal With Missing Data in Datasets
Missing data is a problem for every data scientist as we may not be able to carry out the analysis we desire or not run a certain model. In this article, I will discuss simple methods that deal with missing values. However, to preface, there is... Read more
Overcoming the Social Biases in Natural Language Processing Systems
Editor’s note: Danushka Bollegala is a speaker for ODSC Europe 2022. Be sure to check out his talk, Social Biases in Text Representations and their Mitigation, there! How would you feel if the final decision on your job application was made by a natural language processing... Read more
The AI Ethics Boom: 150 Ethical AI Startups and Industry Trends
The demand for ethical AI services (including terms like “explainable AI” or “responsible AI”) has skyrocketed, in part due to some of the troubling practices employed by large technology companies. Everyday media is full of news of privacy breaches, algorithmic biases, and AI oversights. In the... Read more
7 More Methods For Better Machine Learning
Many companies are now utilizing data science and machine learning, but there’s still a lot of room for improvement in terms of ROI. A 2021 VentureBeat analysis suggests that 87% of AI models never make it to a production environment and an MIT Sloan Management Review article found that... Read more
Practical Quantization in PyTorch
Quantization is a cheap and easy way to make your DNN run faster and with lower memory requirements. PyTorch offers a few different approaches to quantize your model. In this blog post, we’ll lay a (quick) foundation of quantization in deep learning, and then take a... Read more
5 Anomaly Detection Algorithms every Data Scientist should know
A real-world dataset often contains anomalies or outlier data points. The cause of anomalies may be data corruption or experimental or human errors. The presence of anomalies may impact the performance of the model, hence to train a robust data science model, the dataset should be... Read more
What Statistical Test Should I Do?
Being a teaching assistant in statistics for students with diverse backgrounds, I have the chance to see what is globally not well understood by students. I have realized that it is usually not a problem for students to do a specific statistical test when they are told which... Read more
Answering Causal Questions in AI
Two of the main techniques used in order to try to discover causal relationships are Graphical Methods (such as Knowledge Graphs and Bayesian Belief Networks) and Explainable AI. These two methods form in fact the basis of the Association level in the Causality Hierarchy (Figure 1),... Read more
Making Deep Learning Climate-Friendly
Deep learning is bringing many benefits to the world: solving the 50-year-old protein folding problem, detecting cancer, and improving the power grid. While there is so much that deep learning is powering, we also need to consider the costs. In the quest for more accurate and... Read more