There are many reasons why companies integrate Machine Learning into their fundamental activities. Enhancing consumer segmentation and targeting and eventually raising a company's revenue, growth, and market position are all just some of them.
Today Machine Learning is used in a wide range of applications across different industries. The main question that the field of Maschinenlernen is trying to answer is:
"How can we build computer systems that automatically improve with experience, and what are the fundamental laws that govern all learning processes?"
Another definition of Machine Learning states that this subset of Artificial Intelligence is the science of getting computers to act without getting explicitly programmed.
Regardless of the definition you pick, the fact is that Machine Learning is present in almost every industry, from medicine, marketing, software development to manufacturing, logistics, cybersecurity, and financial services. Machine Learning allows automation of virtually every task with a data-defined pattern. The additional business benefit is that it enables businesses to transform tasks previously performed only by humans.
It is become difficult to imagine an industry not being transformed by Machine Learning. Adopting these new technologies offers numerous business benefits, but it also comes with particular challenges. That is why, before you decide to transform your business with Machine Learning, you should take a couple of things into consideration to check whether your company is ready for new technology adoption.
The first step is to define what your business needs. What problem do you want to solve with Machine Learning? There are a couple of questions to help you with this:
How can you determine which of your company's challenges are susceptible to machine learning?
Begin by differentiating between automation difficulties and learning challenges. Machine learning may aid in the automation of your operations, but not all automation challenges necessitate learning.
When the task is simple enough, you can apply automation without learning. These are simple tasks with a precise, specified sequence of actions currently being done by a human. In the case of more complicated tasks, standard automation applicable in the first case is not enough. To successfully apply Machine Learning to this type of task, the model needs to learn from data.
Machine Learning is based on data - algorithms build a model based on sample data and use them to make predictions and decisions themselves. With the increasing amounts of available data, Machine Learning will probably become more present as a part of business and technological progress.
Machine Learning works by learning from past experiences. It explores data and identifies patterns. These models, however, are unable to acquire any knowledge that is not contained within the data you supply. Therefore, we can say that data is a potential limitation for successful Machine Learning implementation that can negatively affect in two ways.
So, in this part, the main question is: do you have enough quality data?
Before giving valid results, Machine Learning algorithms often require big amounts of data. The size of the dataset is frequently responsible for the lack of performance in Machine Learning projects because without enough data, you cannot train any model. But how much data do you need? Unfortunately, there is no set of established rules that will provide you with a clear response, but there are a couple of things you can take into consideration:
Besides lack of data, another issue can be caused by the lack of good data. Poor data quality is one of the main adversaries of Machine Learning's successful and profitable adoption. It is generally understood that training data determine the efficiency of machine learning models. Quality data provides quality outcomes, but insufficient data does not.
"Poor data quality is enemy number one to the widespread, profitable use of machine learning," says Thomas C. Redman, one of the pioneers of data quality management.
In the case of poor data, there are three potential scenarios:
To capture the most business benefit from Machine Learning, you need a team of actual experts. In Machine Learning adoption, in-house developers might lack the expertise to build Machine Learning based models. This technology gained popularity relatively recently, which makes in-house experts not only hard to find, but they are also quite expensive. An additional problem that may arise in the recruitment process is validating their expertise because it requires you to be already familiar with Machine Learning.
It is often much more expensive to hire in-house developers than it is to hire outsourced experts. Costs of in-house developers include much more than just a salary. They include infrastructure, overhead, additional employee perks, training expenses, and so on.
On the other side, a business consultant, or even an entire team of experts, already knows the best practices and has seen what strategies work in different places and projects.
A dependable partner will help your business development by providing reliable and consistent Machine Learning consulting services and custom solution implementation while also understanding the challenges of projects like these. When done correctly, outsourcing produces custom solutions faster, reducing time-to-market while keeping the quality high.
What is the best way to find an outsourcing partner?
If you opt to outsource Machine Learning development, you need to put in the effort to select the most qualified team for the job. To make it easier, we have prepared a short checklist.