Top 7 Machine Learning Challenges for Implementation in Businesses

Studies show that your business can experience 40% productivity improvement by using Artificial Intelligence and Machine Learning. It can help you to reorganize your data in such a way that you get value out of every data point that you record.

Machine Learning is an invaluable technology that more than 50% of businesses are already exploring or planning to adopt. It is a key player in the digital transformation of your organization.

However, while implementing Machine Learning, your business is likely to look at the positive side of things. There are multiple Machine Learning challenges that you may forget even exist.

Solving these Machine Learning problems is crucial to the success of your entire digital transformation initiative. You don't want to get stuck in management struggles or half-hearted Machine Learning projects that yield no result.

In this article, we will highlight the 7 Machine Learning challenges that your business can face while implementing. You will also learn how to find quick solutions to these problems in Machine Learning projects.

7 Machine Learning challenges your business might face

If you are struggling to begin your journey even with simple Machine Learning projects, you are not alone. Only an exaggerated explanation of the positives of Machine Learning can make you feel like.

Here are 7 Machine Learning challenges that we will address so that you can get a better perspective on its implementation. You can even decide whether it's the right technology for you or not.

1. Time-consuming deployment
   • Some enterprises say that it takes them around a year to completely implement Machine Learning ideas in their enterprise.
   • While these lead times are undesirable, even simple Machine Learning projects can take months to implement. The reason is simple - Machine Learning is a relatively young technology, and you might not be able to figure out its full potential for your organization.
   • You may want to indulge in the old school hit-and-trial, which is more time-consuming. A solution to this Machine Learning problem would be to deploy it at a really small scale and check for its feasibility with other functions.
2. Overestimating result delivery
   • You might face the challenge of thinking that your Machine and Deep Learning projects will deliver results much better than you expect. Machine Learning, being what it is, is expected to provide outcomes quickly and precisely.
   • However, you'll often see that such is not the case. Machine Learning and Deep Learning requires working with vast amounts of data, and it could fail in haste.
   • The best Machine Learning problems and solutions require time and resources as the technology practically learns everything from data.
3. Unavailability of data
   • While your business might know how to work with data using simple Machine Learning projects, the unavailability of data can be a significant challenge. Data of a hundred components is not a real value-contributor for any Machine Learning model.
   • On the other hand, once you understand that data is the key, you don't know what type of data you want. While Machine Learning works like a breeze with unstructured data, you may want to start with structured data for achieving visible results for the first time.
4. Issues with data security
   • Now one of the biggest Machine Learning challenges today is data security. Even though you collect tons of data, security is one issue that you will always be concerned with.
   • Machine Learning models cannot inherently differentiate between sensitive and insensitive data. Confidential data stored on risky servers can falter the entire Machine Learning project.
   • You must start by encrypting data and storing it in servers from where Machine Learning models can safely access it. Confidential data must only be supervised through superior decision makers.
5. Scaling challenges
   • A study by Algorithmia shows that 58% of organizations with employees over 10,000 using Machine Learning face challenges in scaling the initiative. Most of the scaling Machine Learning problems arise due to hardware issues, modularity, or data unavailability.
   • Even today, most companies work with traditional systems that have different storage for different types of data. It makes scaling difficult because Machine Learning doesn't work like it.
   • You need to have a centralized data hub for your Machine Learning projects to access data from a single source. It makes data processing simpler for Machine Learning models.
6. Lack of Machine Learning experts
   • While developers are now quickly onboard the Machine Learning journey, a lack of skilled and developed ML experts still remains one of the biggest Machine Learning challenges. You might not be able to find developers who can fulfil your requirements.
   • Even today, the skills needed to understand complex Machine Learning algorithms are limited. And without the right ML experts, you can face several challenges in implementation.You can look for collaborating with other organizations who have expert Machine Learning developers. In this way, you can access the experienced developers necessary for implementation.
7. Expensive deployment
   • Possibly the biggest Machine Learning problem is the expensive deployment in the enterprise. Machine Learning implementation requires data scientists, project managers, and developers with a high degree of technical expertise.
   • It is costly to hire all these people as a lack of talent (challenge 6) makes it difficult to find skilled experts at an affordable price. On the other hand, since Machine Learning projects work with massive amounts of data, there are extra infrastructure requirements for deployment.
   • Without the proper infrastructure, testing becomes difficult. Without testing, proper implementation is a major Machine Learning challenge. To solve this problem, you need to consult with firms that can provide ML experts and services all at once. It would not cost less but would relatively lower your expenses on implementation.

Conclusion: The benefits surpass Machine Learning challenges

If you have the right team and the timing is right, you can overcome all the challenges in Machine Learning implementation. These challenges only prepare you to face the next complexities that are bound to occur in your Machine Learning model.

You don't have to be afraid of these Machine Learning problems. With a certain amount of patience, you can see noticeable results like increase in productivity, efficiency, and employee jobs satisfaction.

When it comes to Machine Learning, therefore, you should follow the "move fast and break things" approach. It's the same approach that followed. It allows you to understand all the Machine Learning challenges at an initial stage and then reiterate the models to your biggest advantage.

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