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5 Things to Consider when Building Machine Learning Projects

5 things to consider when building ML projects

The landscape of artificial intelligence and machine learning has been rapidly evolving over the past few years. Computers teaching themselves to recognize patterns and predict the future feels like a detail in almost every Ray Bradbury novel explicitly stated or not. It feels mystical, inaccessible, and far-fetched. However, machine learning technology is already part of our day-to-day lives. Though the technical aspects of machine learning technology may be impossible to grasp, the most crucial steps that managers must take to successfully introduce machine learning technology is far less technical and easy to understand. Most people know that the key to any successful project is to first define a problem statement. However, a more ML-specific rule is that all projects require a long process for preprocessing data. This is a less technical process than writing code but will impact the outcome and success of your ML project dramatically. Here are a few things to keep in mind to deliver impactful results before, during, and after creating machine learning projects:

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Table of Contents

Define a Specific Problem

What are your organization’s business goals? Before committing to machine learning adoption, you should define a problem that can be solved using machine learning technology, and establish what the end goal is. The success criteria of an ML project should always be how it impacts and improves your business, and not necessarily the accuracy and precision of the model.

It is important to always consider:

  1. What problem am I addressing?
  2. Can ML technology solve this problem?
  3. What decisions will I make based on the predictions my ML model generates that will positively influence my business?

If you have clear answers to all these questions, then moving forward with ML development is feasible and likely necessary, otherwise, reassess your ML needs and try to frame a problem that can be solved using an AI or ML project.

We cannot stress enough that the final output must have some business use. For example, a model that predicts the likelihood of clicking certain videos should be able to allow a system to pre-fetch the videos most likely to be clicked. A model that predicts whether or not there is a kitten in an image may not be so useful for a finance company.

Things to consider when building a machine learning project

Be prepared to prepare your data

Data preparation is the most unique and important step in creating a machine learning project. The final predictions will ultimately depend on the quality of the initial input data, so this first step is crucial. Preliminary data analysis allows project managers not only to understand any trends that they might see once the predictions start, but will also help you visualize how class-balanced your dataset is, whether or not it has missing values that will generate bias, or if there are any outliers in the data that may influence predictions.

If you are unable to find correlation in your data, and only find trivial patterns, then machine learning may not be able to deliver the expected results. Distinct patterns and potential causation in your data will lead to more accurate outputs from your model.

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Understand iteration is inevitable

Iteration is one of the cornerstones of a successful ML project and is vital on many levels. As the leader of a project, it is important to accept that your first model will not be accurate or precise, even though that may be a concept scares most people. Machine learning is experimental in nature, so starting small and iterating towards your goal will lead to higher-value predictions in the long-run. Iterating provides confidence at every stage so as you eliminate factors that degrade the quality of your model's performance, you can progressively make more and more informed decisions with respect to the data that you input in your model.

Storing the model code, hyper-parameters, and result metrics of every experiment and tracking the changes in variables and data is an essential part of the process because you must be able to discuss results, find the root causes of issues, and reproduce experiments, and decipher whether or not there were any issues

Monitor your models

Once machine learning models are put into production and actively making predictions, reciprocity is inevitable, unpredictable, and important to monitor. Reciprocity in machine learning models come completely change outputs, so keeping track of how accurate and precise your models are is essential in creating a project that will not become overrun with data that renders the model useless. When machine-learned models are put into production and integrated with products, reciprocity can be unpredictable, which makes it tough to predict and test all possible situations. It’s critical for product teams to invest time in exploring what their machine learning systems are doing and how they can be improved.

Model monitoring is extremely important because it ensures that the model is working as per expectations, i.e. the model is providing predictions accurately and fairly in all scenarios. It also helps detect the scope for quick improvements and enhancements of the model, so keeping track of your model’s performance can improve the ultimate business goal as well as avoid any problems down the road.

Can you scale?

One of the most important steps is putting your model into production and using it. Isn’t that the reason you decided to create a machine learning model? Investing in a sophisticated ML pipeline eliminates the need to build and rebuild models because they cannot handle the growing volume of data or demands from your team. Ensuring that your pipeline is scalable in every way is essential for a successful project.

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