Introduction
Machine Learning (ML), at its core, is an algorithm that can learn from data. Leading companies in the market are using Machine Learning to understand and monitor consumer behaviour, business processes, and keep track of the competition. As such, it has many applications in many fields.
It can be used in healthcare to diagnose diseases and personalise medicine. In eCommerce, ML can be used in predicting customers who are likely to buy or leave, dynamic buying behaviour, etc. But the more important question is: what are the challenges of deploying a Machine Learning model?
Problem definition
- A clearly defined problem is one of the most important factors for ensuring the successful implementation of machine learning. It will help you:
- Understand what exactly your company needs to do, and why that’s so important.
- Think about who you can involve in the project and how they can help.
- Keep track of your progress on a daily basis with a structured approach to solving it (and keep everyone else motivated).
Data Volume
Data volume is a challenge because it takes a lot of data to train a model, and it can take a long time to train each model. This means that businesses need to have the ability to store, process and analyse large amounts of raw data for their machine learning models to improve over time. Another way that companies can tackle this challenge is by using cloud computing platforms like Amazon Web Services (AWS), Microsoft Azure, etc which allow users to scale up or down as needed based on the current size of their datasets. Cloud-based services also make it easier for developers and businesses alike because they don’t have to worry about purchasing hardware or software licenses upfront—they simply pay for what they use when they use it!
Data Quality
Data can be messy and hard to understand. It’s often incomplete, inaccurate or inconsistent, and sometimes even biased. The data you’re using to train your machine learning models has to be clean, accurate and consistent. If the data is messy or poorly formatted, it can result in poor model performance when applied to real-world problems.
It’s not uncommon for organizations to have large volumes of historical data that are difficult to understand and analyse because they contain incomplete documentation or lack metadata. In addition, many organizations also have inaccurate or inconsistent data due to human error during entry as well as inconsistent business processes across departments and regions. These issues can make it hard for an organization’s analysts and developers who may not be familiar with the specific domain being modelled by a machine learning algorithm.”
Bias in training data
One of the biggest challenges facing machine learning adoption is bias in training data. The more you understand about what bias is and how it can be detected, fixed, and prevented, the easier it will be to implement a solution that works for your organization. Inherent biases are often overlooked or hidden and can lead to false conclusions and cause harm to users who rely on your product. Common examples include:
Biases based on race, gender, disability, or age (e.g., if you’re targeting ads for babysitters)
Biases that result from overfitting (i.e., when a model performs well on training data but doesn’t generalize well)
When machine learning is fed inaccurate information about who does what job or which candidates would make good employees (or worse yet—who should be hired), then your company will end up with an employee base that doesn’t reflect reality at all.
A common way of detecting bias is through an evaluation metric called AUC (area under curve). AUC shows how well your model performs at predicting outcomes. If there’s a problem with your data set—for example, if it’s biased towards certain groups—you might see lower AUC scores than expected for that model performance level. Once you’ve detected a potential issue with your data set’s accuracy/generalization capabilities due to inherent biases such as overfitting, fixing it usually involves removing relevant features from the dataset until they no longer have any effect on accuracy scores or increasing sample size until performance improves enough so that any remaining issues are negligible compared against other factors influencing overall system performance (such as distributional characteristics like range limits).
Lack of reproducibility
Reproducibility is the ability to reproduce results. It’s a key aspect of science and has been the center of many debates over the past decade or so. Reproducibility is also one of the biggest challenges for machine learning adoption, but it’s not something that you can solve by yourself.
The problem isn’t just about reproducing individual experiments: it’s about reproducing entire lines of research and studies in general. When researchers publish their findings, they have to provide enough information for others to be able to reproduce them (that’s why all papers contain detailed descriptions). This means that if someone sees a study that interests them, they should be able to use those same methods and run their own experiment based on those findings without having any trouble re-creating what was done before.
If you’re interested in getting into machine learning research for fun or even professionally then chances are high that this will become an issue at some point down the line when someone wants you to share your code with them or asks how exactly did you get those results?
Machine learning has a high failure rate.
According to Gartner 2021, 85% of Machine Learning projects fail. One of the main reasons is getting a data science team in place that can help implement and maintain your machine learning solution. If you have an issue with implementing or maintaining your ML solution, feel free to reach out and our team will be happy to help!
Conclusion
Machine learning is a powerful tool, but it’s important to remember that it is just one of many. Data scientists still need to know their way around a spreadsheet and have business acumen in order to be effective. Machine learning can help us find insights in data, but we still need humans at the helm when it comes time to make decisions based on those insights.