Machine Learning is the best method for data analysis. In addition, it automates the creation of analytical business models. That is why machine learning plays such an important role in business development. So, it is quite possible that your business will also need new ideas to implement Machine Learning solutions. However, problems can arise with the implementation of machine learning methods.
Companies need to fully understand how ML works and be aware of the latest developments in order to identify the challenges of applying machine learning.
We will list the problems that startups face when implementing Machine Learning methods and describe how to solve them.
1. Issues with Model Deployment
To identify model deployment issues when implementing ML methods, companies need industry experts who are well versed in the latest AI/ML technologies. Or hire Machine Learning developers to solve the incompatibility between the machine learning model and the business process, which directly affects the deployment of ML systems.
Machine learning models are typically built using languages such as Java, C, Python, R, and SQL. For example, there have been many improvements in Facebook’s ability to recognize faces or Amazon Alexa to understand specific voice commands. In particular, commercial companies must ensure that they can answer the following questions:
- Which ML model needs to be updated in your business profile?
- What data usage pattern applies to your business?
- What are the right development algorithms for your Machine Learning model?
To successfully introduce Machine Learning, enterprises must have a sound understanding of data flows, algorithms, and how they can be applied to various processes. For companies manufacturing machinery and equipment, Machine Learning provides a platform for predicting active measures and possible failures in the production department. To ensure a normal working condition, an exact algorithm must be followed.
2. Ethical issues in Machine Learning
Ethical issues in machine learning include issues related to the use of data.
There have been some cases of racial bias in Machine Learning programs that also unintentionally affect the adoption of ML technologies.
Similarly, when talking to a helpdesk employee, it is not easy for people to know if they are talking to a human or a machine. This alone makes ML technology difficult to implement.
A famous case of racism occurred two years ago when Google’s facial recognition software mislabeled two African-American boys and classified the teens as gorillas.
Google had to face a lot of criticism, and people began to wonder if it was possible to train artificial intelligence to be deliberately racist. The real reason for the misclassification is not racism, but an actual error in the system’s training set.
Developers need to make more transparent decisions in ML when it comes to ethics and user behavior patterns. Your technical team must collect sufficient ethical data to properly prepare Machine Learning applications. This is important because different situations need different ethical approaches and the system must be designed according to their goals and actions so that ML development technologies are useful and empower as many people as possible.
3. Problems related to data collection and storage
Collecting and storing data is a really big problem in applying ML. Take the example of a public health project that was designed to reduce the cost of treating patients with pneumonia. That is, they integrated machine learning to sort through patient records to determine who is at high risk of infection and therefore should be in the hospital, and who is at low risk of contracting pneumonia.
However, they cannot get the information they need from the data records, which means that machine learning will not be as useful, or perhaps even completely useless for them. So, what is the right way to deal with such problems?
It is true that the need for a proper data collection mechanism may be the most difficult to manage. Users turn to ML for predictive analytics, and the first thing to do is to fight data fragmentation.
For example, in the tourism industry, data fragmentation is one of the main problems. In this case, the hotel knows the guests’ credit card details, home addresses, contact numbers, and other details. This data is transmitted to various departments of the hotel. As a result, the hotel team cannot always put together all the data streams.
Well, this can be a problem for guests. A large amount of data and missing values in the data can reduce the accuracy of the forecast, which is unacceptable. Thanks to Machine Learning, the approximate and predictive values that are provided to them are considered “more accurate” to the software algorithm.
The points described here seem simple and clear. However, you still need a Machine Learning expert if you need to develop a data collection and storage methodology, fully configure the infrastructure, and find complex Machine Learning problems.
4. Cost related issues
Smart companies are realizing the opportunity to rely on data-driven decisions in their operations. And a lot of data requires a lot of storage space. So, how can all this be useful and how much does it cost? In many cases, an analysis of the cost of options is necessary to make an informed decision.
Let’s break down the steps required to develop a custom Machine Learning model and how the cost can change for each step.
Requirements: The requirements phase is all about understanding what your business needs from the model. We recommend that you have clear ideas about what you want for your business because a vague idea can cost you a lot.
Data: You can think of data as the experience your business model needs. The more good quality experience you give the business model, the better it will learn to solve our problems. The cost of this process can be zero if your data is ready for the model, otherwise it will incur some cost. And if we are talking about large datasets managed by a cluster, the cost can be in the thousands.
Model: It is always important to evaluate your business model in order to effectively measure costs. It will also take several days to evaluate and select an algorithm, train a business model, test it and implement it. This is the main part of your business process and it is really difficult to determine its cost.
Production: Once your algorithm is tested and ready for production, there are two main steps to take:
- Integration with existing business structure.
- An update to the business model that may require learning new data or implementing new features.
Both are optional for your business, so here the cost can also range from zero to several thousand dollars.
Thus, the ML algorithm and Deep Learning development work are the main factors to pay attention to. Performance really depends on the client’s business goals and the cost of data prediction. Machine Learning projects take time to achieve the best results.
Even if you’re lucky and your ML algorithm hits the benchmarks right away, the chances of your program running efficiently or failing outright are equal.
Therefore, continuous monitoring can only protect your ML model from degrading performance, but things will get better over time. In addition, in determining the performance of your business, planning the technical architecture at a very early stage determines the success or failure of your ML adventure.
Conclusion
We hope it turned out to be a really deep dive into the world of implementing ML in business startups. The fundamental point is to minimize the problems and create as many benefits as possible, which will give your business all the key capabilities of Machine Learning. The first step to minimize the problems is to seek help from experts who solve such problems every day. Machine learning consulting is the first step to success in such a complex field.
