Machine learning lies at the heart of almost all data science projects today. Every sector is making significant usage of this technology. Robotic process automation and intelligent process automation are one of the best products of machine learning technologies when it comes to manufacturing, supply chain, distribution, and various other business functions that can be augmented through automation.
AI and machine learning research has moved forward faster than we had expected, however, some pitfalls are recurrent and common across all industries. We will talk about those mistakes that an average machine learning course may miss (not the good ones though).
1. Not paying attention to the data
Data fuels machine learning, hence, it deserves your keen attention. If you are not paying close attention to data you will miss certain important insights. Datasets that have similar descriptive numbers in terms of mean variance, regression line, etc, may have very different distributions and produce very dissimilar graphs.
Mistakes occur while choosing your data as well. For instance, if you consider the value of dependent variables it might be misleading in terms of the true relationship between the dependent and independent variables.
The way out
- You need to use exploratory data analysis to understand the data at hand.
- Make sure the data you use to train your models appropriately represents the real world data your model is likely to encounter.
- Check if there are errors in data or if any data is missing.
- Communicate with the data owners or creators to form a better understanding of the datasets.
2. Working with insufficient infrastructure
Developing and maintaining in house infrastructure for machine learning tasks can be quite a challenge for many companies. The lack of elasticity in terms of data storage capacity and the lack of computational power create the most difficult problems.
The storage and computational requirements for machine learning are dynamic, if you are operating at your limit, you will definitely face a pitfall.
Distributed computing, cloud storage, regular hardware updates, are some ways of avoiding these situations.
3. Implementation without strategy
It often occurs that a company deploys machine learning models without even being certain about their needs. There are certain questions you need to answer before integrating machine learning into your existing analytical processes.
Do you have the infrastructure to handle machine learning? Does machine learning have a good business case for your company? Is it really going to add value?
Machine learning deployment without answering these is set up for failure.
Once you have got a business case for machine learning you need to figure out what sort of models you may need. You might want to implement a model factory that automatically creates models for different segments of your business. Or, you may opt for an ensemble model.
4. Implementing algorithms from scratch
Sometimes you just land in a unique problem where it seems easier to build an algorithm from scratch than looking for it trying different existing models. But most of the time what you need is already out there.
When you create an algorithm from ground up, it may show certain issues like memory hogging, edgecase intolerance, slow functionality, or it may be outright wrong.
When you use an existing library you can stop worrying about a lot of these issues and get faster and more accurate results.
So search for past works. Talk to people. Search frequently. Try new methods. And you should be good.
5. Skipping failure analysis
Analyzing failures of different categories is an extremely important part of machine learning. If you do not perform failure analysis you will end up losing a lot of time and effort on things that produce very little results.
When you perform failure analysis and review the system level metrics regularly, you get a clear picture of what works and what does not. You identify the problems that need to be prioritized. This way you can maximize your efforts in areas that need the most attention.
It is always a game of efficiency, and the more you know about the shortcomings of your models, the better your chances of achieving optimum efficiency.
Summing it up
Machine learning will have pitfalls. We need to accept that and analyze the failures. Focus on finding solutions rather than trying to reinvent the wheel. Form a business case for yourself, focus on solving the problem, rather than developing technology for its own sake. And most importantly do not overlook the data. It is what matters the most.