How To Overcome The Three Barriers To Machine Learning

The process can be overwhelming and can stir confusion and doubt about its efficiency. This is due to the challenges businesses encounter along the way, which slow down their goals in growing the business.

Some barriers can be reduced through a small, well-defined pilot; others may show that machine learning is not the right approach for the problem. A useful outcome can be deciding to keep a simpler process.

These are the three most common obstacles to machine learning your business may encounter and how you can overcome them.

Inefficient Data and poor security

One of the first barriers to Machine learning in businesses that use try to adopt these technologies is that they often often choose to save vast amounts of data in different storages, making the process longer and more time-consuming.

The extraction of data is another thing to consider. Data security is crucial in machine learning projects, given that breach is possible, specifically for sensitive data containing personal information.

Data encryption may be done before the model comes in, allowing a more challenging data access process.

To overcome this hurdle, the separation of sensitive data is needed. This option also minimizes the risks in security.

Lesser sensitive data should be more accessible by reliable team members so that when it is needed, it can be used as soon as possible without waiting time.

Accessibility to data also makes processes faster and more convenient when it comes to development, testing, and collaborating with others.

Additionally, understand that machine learning models are only sometimes straight out of perfection. It still has flaws, makes mistakes, and is always open for changes as you go through your projects.

Balance access with controls appropriate to the data, including permissions, secure storage, and review of how data move through the workflow. These measures reduce risk; they do not eliminate the possibility of a breach.

Lack of robust test and experimental phases

Our second of the three barriers to machine learning is that businesses need to understand that machine learning technology must undergo testing and experimentation just like in every other system.

Evaluation needs representative data and a clearly defined target. Document missing values, label quality, duplicates, and noisy measurements, then test how those issues affect the baseline. Perfect data are not a prerequisite, but unexplained data problems can invalidate results.

As artificial intelligence may not always be flawless from the get-go, testing allows comparison to the accurate data.

Start with offline evaluation that reflects the intended use. If the evidence supports a trial, consider shadow predictions or a controlled rollout with monitoring and a fallback; do not assume direct production use is the first test.

Companies may prefer something other than this option, especially for those new to digitalization. For large scale projects, it can be difficult to apply such trials right to the real process because of fear of shaking the status quo.

A solution for this concern is to strengthen the department where experimentation phases occur. A strong support system that can serve as a backup or safety net should be in place for each test, specifically if it involves large-scale machine learning technologies.

It is essential to build independent sandboxes, allowing various teams to experiment on their own using different approaches.

Using various solutions and methods, each experiment is tested up to its maximum, with results showing what it can offer. All results from each test will reveal what deserves to go up in the next phase or what needs to be considered for final use.

For example, suppose you are to adopt a machine vision AI for your company. In that case, it is necessary that tests include visual inspection, positioning, defect detection, identification, sorting, and tracking using different methodologies.

The best result will likely be your ultimate choice, with chances of getting improved, revised, or scrapped later.

Inability of business processes to adapt to changes

Gone are the days when firm business processes were the norm in digital transformation. Companies that likely succeed in machine learning welcome new data with flexibility and openness to what comes next.

Machine learning demands continuous evolution in business processes, starting with businesses’ capacity to adapt to rapid changes, evolving skill sets, and diverse growth opportunities.

Also included in the need for flexibility are data governance, data integration, and data exploration, all of which require crucial supervision and monitoring. These processes ensure all data are evaluated and all scopes are checked, tested, and assessed.

Size infrastructure to the workload. Many pilots can run on a standard CPU and simple storage; accelerators, distributed systems, or streaming infrastructure should follow measured requirements.

Before investing in infrastructure, estimate data volume, training time, prediction latency, operating cost, and maintenance effort. Extra capacity alone does not make the project useful.

Define who owns the model, who can approve changes, and how users can handle uncertain or failed predictions. The system should fit the organization’s operating requirements.

One thing’s for sure, machine learning does not promise quick success.

With machine learning, businesses are required to adopt a culture of agility and tenacity. By being agile, businesses learn from failed tests and move on quickly to study the new ones.

This solution lets companies deal with failure fast, recognize the opportunities for learning, and veer away from spending too much capital on processes that no longer work.

It is a waste of time in machine learning and in adopting artificial intelligence in general if companies allow themselves to be dragged by failures from its past projects.

By quickly acting after failure, companies reach the goal of building a solid machine learning strategy ideal to withstand changes.

Deep learning is a subset of machine learning, not the other way around. Many business problems can be addressed with simpler methods; our comparison guide explains the distinction.

You may encounter hurdles as you develop your machine learning projects, and they are inevitable.

While it has imperfect models, algorithms trained on data sets, and many inaccuracies that come with it, it does not mean that they are unreliable. You can learn a thing or two from each model and create a better version for your next.

These challenges to machine learning may seem overwhelming for some businesses, but overcoming them opens more opportunities for them to grow.

Next step: write a one-page pilot plan with a baseline, validation strategy, success metric, owner, and stop condition. Check it against our cross-validation guidance before interpreting a promising score.

Summary on the main barriers to Machine Learning

In this post we have covered some of the main barriers to machine learning.

Despite their perceived difficulty, these obstacles can be tackled by clearly aligning the companies goals towards becoming more data driven, incorporating security standards, and driving change management and an innovation mentality within the organization.

We hope that this article on the obstacles to machine learning was useful, have a wonderful day, and as always, thank you for reading How to Learn Machine Learning!