3 Common Problems Machine Learning Engineers Face

The volume of data is greater than it’s ever been. 

Back in 2010 the total worldwide volume of data that was created, captured, copied and consumed hit around 2 zettabytes (roughly 2 billion terabytes).

As we move deeper into 2022, that number has risen to over 90 zettabytes

Which is a gargantuan amount of data.

Now, in this high-stakes race to generate as much data as possible, how do you decide what information is worth your time and attention? 

Machine Learning Might Just Be The Answer

The power behind machine learning (ML) lies in its ability to use algorithms that analyse large sets of data and draw correlations that even our most intellectual savants simply cannot.

With billions of parameters in algorithms like GPT-3 for text creation; or trillion-parameter switch transformers that reduce computational costs and carbon footprint, there’s no real comparison to be made.

And it’s becoming obvious that through the tremendous investment and growth in the ML sector, businesses are seeing more and more opportunities coming out of ML. 

Whether it’s task automation, better predictions, new innovation, or improving their operations. 

But, as one of the most important tools that businesses have in their arsenal to deal with the big data explosion, ML is not without its challenges.

  • Businesses may not have enough clean, accessible data to train their machine learning models effectively. 
  • Even if businesses have enough accessible data, they may not have the infrastructure or talent in place to deploy and test those models.

As ML becomes more commonplace, so do the challenges that come with it.

Here are three of the most common problems machine learning engineers face:

  • Dealing with large amounts of data;
  • Ensuring model accuracy;
  • Explainability.

1. Dealing With Large Amounts of Data

One of the biggest challenges ML engineers come to face is dealing with large amounts of (often chaotic) data.

Without that data being clean and accessible, businesses run at massive losses and data scientists struggle with deploying effective models.

According to global stats on Big Data technologies, poor data quality costs businesses worldwide anywhere between £7.3 million and £10.7 million every year. Which reveals just how large the big data problem actually is.

But how badly does this data problem affect ML models?

  • Gartner predicted a shocking 85% of ML projects failing – right through to 2022.
  • And research by Datagen reveals that 99% of computer vision teams have faced at least one ML project cancellation due to insufficient training data.

2. Ensuring Model Accuracy

Another common challenge machine learning engineers face is ensuring that their models are accurate. 

ML is only as good as the data it’s trained on, so if there are any errors in the data, the machine learning model will likely be inaccurate. To add to that, ML models can overfit on training data, which means they may perform well on the training data but not so well on new data.

When an algorithm becomes overfitted, it cannot perform accurately against unseen or new data. Overfitting occurs when a statistical model fits exactly against its training set, which can be problematic as there’s too much memorisation of irrelevant information in these models. 

Therefore, they become unable to generalise well enough from one occasion or context into another.

3. Explainability

While some ML models may be accurate, if the ML engineer or data scientist can’t explain how it works, it may bring up some issues.

Which can negatively affect business profits and is often indicative of a disconnect between data scientists and business teams.

In some cases, it’s important for a machine learning model to be interpretable so that business users can understand why the model is making the predictions it is. In other cases, explainability is important for regulatory reasons. 

ML engineers need to be aware of the explainability requirements for their models and choose appropriate ML algorithms, accordingly.

According to Neptune.ai, explainability is defined as 

“Explainability in machine learning means that you can explain what happens in your model from input to output. It makes models transparent and solves the black box problem. Explainable AI (XAI) is the more formal way to describe this and… means methods that help human experts understand solutions developed by AI.”

Ensuring model explainability helps improve:

  • Accountability,
  • Compliance,
  • Performance and
  • Overall Control.

Expand Your Knowledge On ML 

These are just a few of the challenges that machine learning engineers face. 

As machine learning becomes more widespread, we can expect to see more challenges emerge from the woodwork.

If you’re interested in finding out more about ML and its applications, check out our blog! Also be sure to get yourself a free copy of our ML Feature Engineering eBook.

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