▮ 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…
▮ 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…
Requirements The requirements for an ML system vary from use case to use case. However, most systems should have these 4 characteristics. Here is a brief overview. Reference Designing Machine Learning Systems
Return on investment in ML depends a lot on the maturity stage of adoption. Here is an overview of how long it takes for a company to bring a model to production by Algorithmia. Reference: Designing Machine Learning Systems
Identifying the Task How you frame an ML problem can significantly affect the difficulty of solving it. It’s important to appropriately identify the task type of your ML problem to avoid such situations. Let’s say we want to predict what…