ML Projects Keep Failing… Here’s Why

AI has brought about some amazing advances in healthcare, tech, education, finance and more over the recent years. But it hasn’t been easy. Although warriors of innovation, Machine Learning (ML) engineers face many battles. Mostly with data.

With digital transformation becoming far more attainable, we’re seeing great promise as we head into a digital-driven future. Especially considering the fact that global investment in AI has seen it’s largest year-on-year growth in the last 20 years: 

A report by Tortoise Intelligence revealed that global investment in AI has increased to 115% since 2020. Reaching just over £57 billion in 2021 alone. 

That said, according to Statista, of the different solutions that AI offers, Machine Learning applications saw the most investment at £20 billion in 2019. 

The problem? 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.

So why do so many ML projects fail?

While there exists a number of reasons why these innovative projects fail, our focus will be given to the data problem.

The Data Problem

Clean, optimal datasets and the healthy flow of data are the rocket fuel to any AI/ML project. Whether it’s automation, prediction, or recognition, ML models need data (and lots of it) to operate at their best. 

Data is so important, in fact, that IBM has revealed that the U.S. economy loses around $3.1 trillion (£2.3 trillion) yearly due to bad data. To make matters worse, research by Experian found that the bottom line of 88% of American companies have felt the sting of bad data, with the average company losing out on 12% of its total revenue. 

No matter the size of an organisation, data comes to affect virtually every decision-making process and has a direct impact on your bottom line. 

And bad data can result in poor decision-making. Which could give rise to customer complaints, extra costs, security breaches and increased risk of public scrutiny.

Being that AI and ML are heavily dependent on clean, accessible data, the stats reveal just how important it is for data to be both optimal and reachable. 

And a big problem facing data scientists and ML engineers is unclean data.

Meaning that the data they have to deal with is often inaccurate, inconsistent or incomplete. So to get past these in-house problems, many ML engineers will use shared data from public datasets which can also often lead to major inaccuracies and failed models.

Data Engineers: ML’s Best Friend

To combat these mounting problems faced by ML engineers, data engineers are a go-to solution. They are complete masters at data cleansing (or data cleaning) and building data pipelines. 

They essentially use code and a variety of tools to clean, optimise and store your company’s data for ML engineers and data scientists to go buck-wild with.

Making use of data engineers or a data engineering solution would make a world of difference if you keep running into similar issues surrounding ML.

If you are facing hurdles in deploying your ML models, we can help you. If it’s dirty data that you’re dealing with, we’ve got a superb data engineering team to remove the grime

And if you need your ML models on point, then we have just the right thing for you… 

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Our MLOps Lead and Machine Learning expert has a knack for creating autonomous algorithms that learn (and do) all by themselves. A machine himself, Dr. Kafka designs, deploys and optimises models of the highest calibre to perform and adapt within any environment.

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