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Is machine learning hard or easy to apply? You have probably heard about Machine Learning and how it changes how companies of all varieties deal with specific situations. If you are a business owner, you may think this is what you’ve been waiting for all this time, but is it? 

What is Machine Learning? 

Machine learning is a branch of artificial intelligence that enables systems to learn and improve from experience without programming. With machine learning, performing operations such as classification, clustering, regression, and pattern identification is possible.

Since it is a trending technology, more and more companies are interested in using machine learning in their projects or being advised to use it to implement solutions in their businesses.

But before diving into machine learning, let us try to answer this question: Do you actually need machine learning? Here are some things to consider:

Resource-consuming projects.

The first thing to grasp is that developing a machine-learning project is challenging. Many companies start a project without the necessary risk analysis and end up with an incomplete or useless project.

They are simply wasting their time and money. According to a Gartner report, even with the companies that are already experienced with AI, only 53% of machine learning projects make it from prototype to production.

Incomplete projects go up to 90% when it comes down to the companies that are newly being introduced to AI. Knowing the things you will need is vital before stepping into the world of machine learning.

 Is machine learning hard or easy to apply: Check if you have what you need to answer

One of the biggest mistakes many companies make is underestimating the effort that goes into an ML project. Without the necessary engineering and resources, they end up burning through their budget without being able to complete the project.

We know trending technologies always look more appealing, but they might lose their glitter briefly.
Take your time while building the structure of your dataset and collecting your data. Before you start, you need to specify your expectations from the model and the decisions you want it to make.

Depending on that, you need to confirm that your data is enough to satisfy those needs. Make sure that you pre-processed the information correctly and that you have trustable data.

As always: quality beats quantity.

Don’t forget that the quality of the data should remain. So having an individual or a team to ensure the dataset still satisfies the needs as you keep adding more data into it makes sense.

This brings us to another thing to consider before diving into machine learning; it is an iterative process! Without getting into it, you can never know what your model exactly needs. You need to make continuous changes and interventions as you move forward with the process.

Also, re-training can be necessary frequently. So acknowledging the process and the required workforce is also essential during decision-making. Fancy solutions do not always bring the best results.

Ask yourself the question, do you even need ML? If you can define simple rules and computations to determine your target, be it a label or a value, why even bring machine learning into the process?

Consider the scale of your data. Even without a rule set, making the decisions manually could be more efficient and cost less than diving into an ML project.

So if you can’t define a rule set, multiple factors affect the model’s decision, or you have a large scale of data, it would make sense to use machine learning for decision-making. Otherwise, you could be expending valuable resources on something your business may not even need.

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