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Continuous Delivery for Machine Learning
Why is bringing machine learning code into production hard? Machine Learning applications are becoming popular in all industries, however, the process for developing, deploying, and continuously improving them is more complex compared to more traditional software, such as a web service or a mobile application. They are subject to... Read more
What is Pruning in Machine Learning?
Pruning is an older concept in the deep learning field, dating back to Yann LeCun’s 1990 paper Optimal Brain Damage. It has recently gained a lot of renewed interest, becoming an increasingly important tool for data scientists. The ability to deploy significantly smaller and faster models has driven most of... Read more
Reinforcement Learning with Ray RLlib
Why Reinforcement Learning? In reinforcement learning (RL), an agent tries to maximize a reward while interacting with an environment. The agent observes the state of the environment, takes an action and observes the reward received (if any) and the new state. Then the agent takes the next action, and... Read more
Best Practices for Dealing with Concept Drift
You trained a machine learning model, validated its performance across several metrics which are looking good, you put it in production, and then something unforeseen happened (a pandemic like COVID-19 arrived) and the model predictions have gone crazy. Wondering what happened? You fell victim to a phenomenon called concept drift.... Read more
Using Unsupervised Learning on Satellite Images to Identify Climate Anomalies
This work is a part of Omdena’s AI project with the United Nations High Commissioner for Refugees. The objective was to predict forced displacements and violent conflicts as a result of climate change and natural disasters in Somalia. We used unsupervised learning techniques on satellite images for capturing sudden environmental changes... Read more
Generating Images with Just Noise using GANs
GAN stands for Generative Adversarial Network, which is essentially applying game theory and putting a couple of artificial neural networks to compete with each other while they are trained at the same time. One network tries to generate the image and the other tries to detect if it is... Read more
Modeling Regression Trees
Decision Trees (DTs) are probably one of the most popular Machine Learning algorithms. In my post “The Complete Guide to Decision Trees”, I describe DTs in detail: their real-life applications, different DT types and algorithms, and their pros and cons. I’ve detailed how to program Classification Trees, and now it’s... Read more
Modeling Classification Trees
Decision trees (DTs) are one of the most popular algorithms in machine learning: they are easy to visualize, highly interpretable, super flexible, and can be applied to both classification and regression problems. DTs predict the value of a target variable by learning simple decision rules inferred from the data... Read more
Enhancing Discovery in Data Science Through Novelty in Machine Learning
Note: Kirk will present two training sessions at the ODSC Europe 2020 Virtual Conference. One will focus on “Solving the Data Scientist’s Dilemma: the Cold-Start Problem with 10+ Machine Learning Examples” and the other will look at “Atypical Applications of Typical Machine Learning Algorithms.” I have always appreciated the... Read more
Machine Learning: Active Failures and Latent Conditions
Machine learning and AI applications are advancing in increasingly critical domains such as medicine, aviation, banking, finances, and more.  These applications not only are shaping the way in which industries are operating, but also how people are interacting and using their platforms/technologies. That said, it is of fundamental importance... Read more