Challenges faced by Businesses in adopting Machine Learning

The global machine learning market is projected to grow from $15.50 billion in 2021 to $152.24 billion in 2028, according to a report by Fortune Business Insights. Enterprises all over the world are increasingly exploring machine learning solutions to overcome business challenges and provide insights and innovative solutions. And even though machine learning benefits are becoming more apparent, many companies are facing challenges in machine learning adoption.

As the name suggests, machine learning involves systems learning from existing data using algorithms that iteratively learn from the available data set. With this, systems are able to come up with hidden insights without being explicitly programmed where to look.

Importance of Machine Learning

The interest in Machine Learning can be comprehended by simply understanding that there is a growth in volumes and varieties of raw data, the different processes, and hence, there is a need to find an affordable data storage.

The need of the hour is to implement a method by which organizations can quickly and automatically analyze bigger, more complex data. Not only this, by implementing and integrating Machine Learning in an organization, it becomes easier to optimize the process. How? Because Machine Learning helps deliver faster, and more accurate results.

What is simply required is to build a precise and customized model, in which Maruti Techlabs can serve as a fundamental assembling point, where your organization can find the best Machine Learning solutions.

Challenges faced while adopting Machine Learning

Machine learning is helping organizations make sense of their data, automate business processes, and increase productivity, and gradually profits too. And while companies are keen on adopting machine learning algorithms, they often find themselves struggling to begin the journey.

All the companies are different and their journeys are unique. But essentially, the frequently faced issues in machine learning by companies include common issues like business goals alignment, people’s mindset, and more. Let us discuss and understand the 6 most common issues which companies face during machine learning adoption. 

1. Inaccessible Data and Data Security

One of the most common machine learning challenges that businesses face is the availability of data. The availability of raw data is essential for companies to implement machine learning. Data is needed in huge chunks to train machine learning algorithms. Data of a few hundred items is not sufficient to train the models and implement machine learning correctly.

However, gathering data is not the only concern. You also need to model and process the data to suit the algorithms that you’ll be using. Data security is also one of the frequently faced issues in machine learning. Once a company has dugged up the data, security is a very prominent aspect that needs to be taken care of. Differentiating between sensitive and insensitive data is essential to implementing machine learning correctly and efficiently.

Companies need to store sensitive data by encrypting such data and storing it in other servers or a place where the data is fully secured. Less confidential data can be made accessible to trusted team members.

2. Infrastructure Requirements for Testing & Experimentation

Most companies that are facing machine learning challenges have something in common among themselves. They lack the proper infrastructure which is essential for data modeling and reusability. Proper infrastructure aids the testing of different tools. Frequent tests should also be allowed to develop the best possible and desired outcomes, which in turn, assist in creating better, stout, and manageable results.

Companies that lack the infrastructure requirements can consult with different firms to model their data groups aptly. Then, they can compare the results with a different perspective and the best one can be adopted accordingly by the company and subsequently, by the board.

The stratification method is usually used to test machine learning algorithms. In this method, we draw a random sample from the dataset which is a representation of the true population. The common practice is to divide the dataset in a stratified fashion. Stratification simply means that we randomly split the dataset so that each class is correctly represented in the resulting subsets — the training and the test set.

3. Rigid Business Models

Machine learning requires a business to be agile in their policies. Implementing machine learning efficiently requires one to be flexible with their infrastructure, their mindset, and also requires proper and relevant skill sets.

However, implementing machine learning doesn’t guarantee success. Experimentations need to be done if one idea is not working. For this, agile and flexible business processes are crucial. Flexibility and rapid experimentations are the solution to rigid monoliths.

If one of the machine learning strategies doesn’t work, it enables the company to learn what is required and consequently guides them in building a new and robust machine learning design. The willingness to adapt to failures and learn from them greatly increases the company’s chances of successful machine learning adoption.

4. Lack of Talent

This is the most worrying challenge faced by businesses in machine learning adoption. While the number of machine learning enthusiasts has increased in the market, it’ll still take a while for the same numbers to reflect on the number of machine learning experts.

With artificial intelligence and machine learning being relatively younger technologies in the IT industry, the talent pool required to fully understand and implement complex machine learning algorithms is limited. And if you don’t have the right people to implement it, then it is difficult to unlock the true potential of machine learning applications.

Organizations are gradually realizing the avenues machine learning can open up for them. As a result, the demand for experienced data scientists has skyrocketed. And so have the salaries in this space. Job sites list data scientists as one of the highest paying jobs of 2020. With more and more organizations getting on board with big data, AI and ML, this demand is only going to increase in the coming years.

One path companies are taking to overcome this challenge is collaboration. Organizations are partnering up with companies that have the skillset and the experience to harness the power of machine learning and implement the offerings to suit your organization’s business goals.

5. Time-Consuming Implementation

Patience goes a long way in ensuring that your efforts bear fruits. And this cannot be truer for machine learning. One of the most common machine learning challenges is impatience. Businesses that implement machine learning usually expect it to magically solve all their problems and start bringing in profits from the get-go.

Implementing machine learning is a lot more complicated than traditional software development. A machine learning project is usually full of uncertainties. It involves gathering data, processing the data to train the algorithms, engineering the algorithms, and training them to learn from the data which suits your business goals.

It involves a lot of intricate planning and detailed execution. And yet, due to multiple layers and the usual uncertainties regarding the behavior of the algorithms, it is not guaranteed that the time estimated by your team for machine learning project completion will be accurate. Therefore, it is very important to have patience and an experimentative approach while working on machine learning projects. To achieve desirable results on adoption machine learning, you should give your project and your team plenty of time.

6. Affordability

If you’re looking to adopt machine learning, you will require Data Engineers, a Project Manager with a sound technical background. In essence, a full data science team isn’t something newer companies or start-ups can afford.

As a result, employing a machine learning method can be extremely tedious, but can also serve as a revenue charger for a company. However, this is only possible by implementing machine learning in newer and more innovative ways. Adopting machine learning is only beneficial if there are different plans, so regardless of one plan not performing up to the desired standards, the other can be put into action. Getting a glimpse into which machine learning algorithm would suit an organization is the only issue that one needs to get by. Once you get the best algorithm with which you’re achieving the required outcomes, you shouldn’t stop experimenting and trying to find better and more innovative algorithms.

Budgeting as per different milestones in the journey works out well to suit the affordability of the organization. If you are not confident on the talent required to implement a full-fledged machine learning algorithm, you can always go for a consultation with companies that have the expertise and experience in machine learning projects.

As a machine learning solutions provider, we at Maruti Techlabs, help you reap the benefits of machine learning in line with your business goals. Our machine learning experts have worked with organizations worldwide to provide machine learning solutions that enable rapid decision making, increased productivity, and business process automation.

Want to explore how machine learning can address your business needs? Get in touch with us here.