Machine Learning - Challenges companies face when deploying and using machine learning

Wiki Article

Anyone who still questions the value of machine learning should take note of former Hewlett-Packard president, Mark Hurd’s thoughts on the subject: “AI and its offshoot, machine learning, will be a foundational tool for creating social good as well as business success.” And anyone who thinks it’s just a fad that will fall out of fashion should think again: according to Hurd – who has a reputation for making accurate predictions about the tech landscape – all cloud applications will include AI by 2025.

Machine learning isn’t just the future; it’s already an omnipresent force responsible for driving numerous advancements and applications we use today – from recommendation systems like those on Netflix, YouTube and Spotify and search engines like Google and Baidu to email filters that sort out spam and banking software that detects unusual transactions.

What is machine learning?

According to IBM “machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.”

These algorithms use statistics to identify patterns in numbers, words, images, and clicks – basically any large datasets that can be digitally stored – before making predictions to improve the customer experience and provide real value to the end-user. Take Netflix, YouTube, and Spotify, for example: each platform is collating as much data about you as possible – such as what genres you prefer and what links you are clicking – and using machine learning to make an informed decision about what you might want next.

Machine learning: the challenges

The benefits of successfully deploying and using machine learning are compelling: facilitates informed split-second decision making, boosts focus and effectiveness of repetitive tasks, improves product and consumer personalisation – to name a few. However, common challenges often arise when attempting to establish a successful machine learning model that must be addressed before you can realise its true potential, including:

Lack of experience

Machine learning is a tool and like most tools, it’s most effective when used properly. To achieve this, you must establish an experienced in-house team, which involves more than just hiring data scientists. The full deployment of a new solution demands product managers, software engineers, data engineers, operational experts, staff engagement, and onboarding and training. Consequently, many organisations opt to buy an affordable off-the-shelf solution.

Motivation for use

There must be a clear-cut business case for machine learning; it should not be used just because it’s trendy. Instead of implementing machine learning for the sake of it, businesses need to identify problems it can solve and evaluate whether it’s the best solution. Unfortunately, companies often implement machine learning before identifying the use case. Adopting this short-sighted approach wastes time and money.

Change management

Change management represents a significant challenge when integrating machine learning into a business. This vital process includes getting buy-in from users, managing new processes, and coping with changing job duties. Without these foundations, the machine learning model will lack the stability needed to achieve its goals.

Unclean data

Successful machine learning is only as good as the data fed into it, which is why it needs fresh, updated data to deliver the most accurate outputs or predictions. Therefore, if training data – an extremely large dataset that is used to teach a machine learning model – contains lots of errors, outliers, and noise, it will make it impossible for the machine learning model to detect underlying patterns.

NobleProg: helping you make the most of machine learning

Having identified the potential challenges of deploying and using machine learning – and implemented measures to mitigate them – you will be well-placed to harness its potential to improve the customer experience and provide real value to the end-user. Effective training can set you on the right course to achieving this. NobleProg instructor-led machine learning training courses – whether online or onsite – demonstrate through hands-on practice how to apply machine learning techniques and tools for solving real-world problems in various industries.

Report this wiki page