Things to Know About Machine Learning Model Monitoring
In the past 5 or 10 years, the terms AI and ML monitoring have been broadly used in every social and non-social media platform. Most of the companies, from manufacturing to banking to legacy companies that we come across in daily life: Google, Facebook, Amazon, and Netflix are examples that use ML algorithms and tools.
Voice recognition by Siri, movie suggestions on Netflix, the news feed on Facebook, and Product suggestions on Amazon is a few examples where ML Monitoring technology is applied. In general, Machine learning makes computers predict the outcomes without being programmed explicitly.
Table of Contents
What is ML Monitoring?
Machine Learning is a part of Artificial Intelligence that makes a machine make complex decisions and tasks similar to how a human being would work. Writing programs that can make computers suggest things such as new feeds or movie suggestions is very difficult.
Hence Machine Learning uses various tools to train the computer on how to program themselves and make decisions. It uses various data, text, images, and numerals that are gathered and prepared as training data that programmers use to train the machine. Based on this, the programmers choose the best ML monitoring model based on the job.
How to choose an ML model?
Depending on what is monitored, the functionality might change, but the below are essential factors considered before choosing an ML monitoring method:
- Ease of integration in deployment tools and models used for training
- Overheads
- Flexibility to see all the logs
- Easy monitoring of data, concept
- Alerting while there is an irrelevant input
In monitoring an ML model, the following are considered:
- How good the predictions are made
- How much of the CPU memory is consumed during the training
- Other hardware metrics and pipeline technology
- Should the machine be re-trained
- Input and output changes due to distribution
Types of Machine Learning
Based on the prediction to be made, programmers choose various algorithmic data. It could be any one of the following:
- Supervised Learning
A fixed set of input and output is given to teach and train the machine. Classification, regression and prediction occur when the machine finds the right pattern for the given output and input.
- Unsupervised Learning
The machine uses correlation regression with the available data to evaluate the parameters. A larger set of data is chosen and structured into various clusters. Dimension reduction is used to reduce the number of variables used since the data here is huge and undefined
- Reinforcement Learning
In reinforcement learning, a set of actions or rules are given along with the parameters and end value. The system looks for all the possibilities from past experiences and chooses the optimal solution.
Various tools help in monitoring the ML models throughout their life cycle. You can start with the production log, efficient data usage and storage, pipeline, and data anomalies integrated with a range of AI that can help improve workflow and communication.
Artificial Intelligence and Machine learning make daily lives easier and better. Constant evaluation with real-time data can notify the accuracy and precision of the monitoring model. Simple things such as watching movies, song selection and suggestions, news feed generation, etc., happen by gathering real-time data using the ML Monitoring Model.
