▮ 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
The Challenges While training large models helps improve state-of-the-art performance, deploying such cumbersome models fail to meet performance benchmarks at the time of inference on real-world test data. Knowledge distillation helps overcome these challenges by “distilling” the knowledge in a…
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…
Process Developing ML systems are iterative and in most cases a never-ending process. Here is a simplified representation of what that iterative process looks like.