Here we discuss Machine Learning Basics, the basic steps to get started with ML, setting up an ML pipeline.
First, let's talk about the key steps of machine learning. We call that the machine learning workflow. No matter which framework, library you use, generally, this workflow serves as a good roadmap. For example, generally, machine learning engineering will start with data cleaning, preprocessing. And ends with model deployment and monitoring.
Also includes Feature Selection, Feature Engineering, Data Preparation
Read more about Data Pre-Processing, Clean - Uniqtech topic page
This is the phase where the model learns. In Neural Networks, weights are learned.
In depth discussion on loss functions. How to best choose them. Coming soon
Or loading the model locally.
Track performance indicators over time. Prediction endpoint, API performance, testing etc. Availability, uptime. Determine if there's a need for continuous training - if data change over time.
The above high level illustration of Machine Learning workflow is similar to the workflows of major AI platforms such as Google Cloud, AWS. When studying and using new ML libraries, try to keep this framework in mind when organizing knowledge points. To summarize, machine learning is all about working with data, cleaning and formatting data into appropriate model inputs, create / select best models by trying different models and optimizing model parameters, in an iterative feedback process, or using an existing state-of-art architecture. Saving the optimal trained model, and use that for prediction. Monitor the performance of the model, re-train, maintain as needed. Optimize the model for prediction on the cloud, as an API endpoint, or on mobile devices. This a summary of common Machine Learning, Deep Learning tasks. Each stage of machine learning can be iteractive, and takes quite a bit of feedback and optimization improvement cycle.
PRO Coming soon...