▮ Labeling Most ML models in production today adopt models with supervised learning. This means they all need data to learn to do a task, and there will rarely be a situation where label data is overwhelmingly abundant. Here are…
▮ After Deployment Previously, I shared how to evaluate your model offline; before production. So for this post, I’d like to share several model evaluation methods after deployment. Blog: Model Offline Evaluation Reference: Designing Machine Learning Systems ▮ Shadow Deployment…
▮ After Deployment After deploying our ML models we want to continually update them to be able to adapt whenever the data distribution shifts. This is why being able to “continually learn” by setting up an infrastructure in a way…
▮ ML-Specific Metrics Here are the main 4 ML-specific metrics to monitor after you’ve deployed your model. Fig.1 – The 4 Metrics Accuracy-related Metrics You should always log and track any type of user feedback. If you’re at the phase…
▮ What causes ML Systems to Fail? Here are the main 2 reasons why a machine learning system fails. Reference: Designing Machine Learning Systems ▮ Software System Failures In 2020, two Ml engineers at Google looked through 96 cases of…
▮ Evaluating your model How do you know whether your ML model is any good? Lacking a clear understanding of how to evaluate your model may not immediately mean that your ml project is going to fail, but it makes…
▮ Deployment Myths For machine learning engineers who have never deployed a model before, deployment itself can be quite intimidating and it may be hard for them to estimate the time and effort required. Here are several mindsets beginners should…
▮ Visualizing Data All machine-learning-related engineers inevitably have to deal with data. That means there will always be a situation where these engineers have to use those data to create proposals and communicate with their clients. Being able to visualize…
▮ THE 6 TIPS There are many things to consider when selecting a Machine learning Model besides performance metrics such as required data quantity, time to train the model, inference speed, etc. If we compare specific ML algorithms in this…
▮ 4 Mainstream Methods Originally, model compression’s main objective was to fit models to edge devices. However, in most cases, if you compress your model it would speed up inference as well. So lately, this method is also used when…