How to Make Sense of the Reinforcement Learning Agents? What and Why I Log During Training and Debug
Based on simply watching how an agent acts in the environment, it is hard to tell anything about why it behaves this way and how it works internally. That’s why it is crucial to establish metrics that tell WHY the agent performs in a certain way.... Read more
Pruning for Success
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... Read more
Dealing with the Incompleteness of Machine Learning
The prospect of automating every aspect of human life is exciting. Imagine humans permanently living a life of leisure and machine learning robot labor picking up the slack! Even though this sounds like a recipe for lazy and depressed humans, we can still be useful to... Read more
ML Inference on Edge Devices with ONNX Runtime Using Azure DevOps
This article discusses ML inference on edge. AI applications are designed to perform tasks that emulate human intelligence to make predictions that help us make better decisions for the scenario. This drives operational efficiency when the machine executes the task without worrying about fatigue or safety.... Read more
Machine Learning Model Development with DALEX and Neptune
Machine learning model development is hard, especially in the real world. Typically, you need to: understand the business problem, gather the data, explore it, set up a proper validation scheme, implement models and tune parameters, deploy them in a way that makes sense for the business,... Read more
Implementing Content-Based Image Retrieval with Siamese Networks in PyTorch
Image retrieval is the task of finding images related to a given query. With content-based image retrieval, we refer to the task of finding images containing some attributes which are not in the image metadata, but present in its visual content. In this post we: –... Read more
Optimizing ML Serving with Asynchronous Architectures
When AI architects think about ML Serving, they focus primarily on speeding up the inference function in the Serving layer. Worried about performance, they optimize towards overcapacity, leading to an expense end-to-end solution. When the solution is deployed, the cost of serving alarms those responsible for... Read more
How Bayesian Machine Learning Works
Bayesian methods assist several machine learning algorithms in extracting crucial information from small data sets and handling missing data. They play an important role in a vast range of areas from game development to drug discovery. Bayesian methods enable the estimation of uncertainty in predictions which... Read more
Could Your Machine Learning Model Survive the Crisis: Monitoring, Diagnosis, and Mitigation Part 2
This is the second part of my blog posts on machine learning monitoring. In the first part, we listed the four questions we are trying to address in a machine learning model monitoring setup. We discussed the first two on how to detect functionality degradation of... Read more
The Ultimate Free Machine Learning Development Stack
Keeping up with data science’s intense pace of innovation is difficult for all of us. This problem is only compounded with the loss of a job, which comes with the loss of business problems to apply data science and machine learning to, the enterprise development tools... Read more