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Advantages of Machine Learning and Best Practices

Benefits of machine learning in business

Machine learning algorithms are applied almost in each and every IT system, no matter where it is used - starting from enterprise-level IT systems ending up with consumer electronics or voice assistants. Today it’s a daunting challenge to upgrade IT solutions without the aid of ML technologies. A short phrase can outline the key benefits of machine learning - they are doing a great part of the job that drives revenue growth.

For several years it’s been constantly stated that these technologies are the absolute must for businesses, but what we see today is becoming an indispensable must for B2C products and services - a new trend that makes ML and AI the major component for successful product development strategies. And this is not surprising because the advantages of machine learning are really great.

For example, over 75% of Netflix users select movies recommended to them by the company’s machine learning algorithms. Retailers do the greater part of their upselling as they know what products are better to offer to their customers. This level of customer behavior knowledge would be impossible without machine learning involvement. 

Machine learning technologies are keeping a good pace in their spread. By 2024 the global Machine Learning market is estimated to reach $30.6B with an almost 50% Compound Annual Growth Rate from 2018 to 2024. Today the market volume equals $7.3 Bln. 

There are many undeniable advantages of machine learning, so they become a mandatory technology for the following areas:

  • online stores (upselling; customer behavior analysis);
  • healthcare (diseases diagnostics, virtual healthcare, drug development); 
  • banking (banking processes automation, suspicious transactions’ detection, banking products offerings based on the user’s behavior data); 
  • digital content recommendation and offering;
  • cybersecurity (fraudulent actions detection, malware attacks troubling signs)

Why ML is so Popular

benefits of active machine learning

The technology is admired by millions of IT specialists around the globe. It has been so for not less than one decade. Why do we insist that machine learning experiences a new level of popularity now?

The trend is called democratization. As it becomes more automated, it becomes much easier to implement these algorithms, and there is no need to be an IT specialist to use ML technologies any longer. So we can easily state that today practically everyone can profit from the indisputable benefits of machine learning, that include:

1. Effective use of massive loads of data

Different technologies are surrounding us everywhere except some far located places in African deserts or forests of Siberia. In cities and suburbs, we leave a lot of data about us every day - pay with cards, visit websites, select products or content online, etc. All of these actions are registered in different systems. This data helps to improve the services we get, but the companies need a smart tool to process the data and get useful information out of this abundant information. Machine learning techniques are helping a lot here.

They are smart enough to figure out what is useful and self-train to use this information for profit generation, and there is no need to hire entire business divisions to do this job. Companies need to find a smart IT partner who knows how to set ML technologies right for the company.

You can become the most competent and recognizable company on the market with our tailor-made fast-developed machine learning solutions. Contact our team, and we’ll show you how ML can fuel the growth of your business!

Let’s have a glance at some advantages of ML use in big data processing and data science.

One of the biggest breakthroughs in healthcare over recent years is the use of wearable devices that collects crucial information about patients. Machine learning algorithms can detect trends that can foresee dangerous health events (like cardiac arrest, for example).

Another industry that takes on machine learning more and more today is automotive. It’s all about the best customer experience - when people choose a car, buy it and get service support. To ensure all of it is done correctly and without spending money to burn, companies use machine learning algorithms to improve operations, marketing, and customer experience.

Machine learning technologies help manufacturers to predict potential problems with their car models and share crucial information with their dealers, ensuring they have all the necessary to fix the problem and optimize customer maintenance costs.

The next one to come on this list is retail. The amount of items companies sell online is huge. To ensure online retailers target their customers with exactly the necessary things, they use ML to capture, analyze, and use data to personalize the shopping experience in real-time. 

2. Business processes enhancing

Any business automation that makes businesses more effective moves the entire company another step in their growth and development.  Solutions that let employees control important information without human processing of the routine and repetitive things helps to free more working time and reassign employees to more engaging and strategic tasks. Things like checking invoices and business expenses for the accuracy or analyzing customers' feedback or requests are now perfectly done by machine learning technologies. 

For a better insight into the ways of machine learning improving business processes, let’s have a look at the following examples:

  • You can offer a personalized experience to your customers  

There’s no better way to raise clients' retention level but making them personalized offerings and showing them that your company understands their specific needs. Features like natural language processing and deep data mining make all the customers' interactions much more personalized. 

  • You make your hiring more professional

Machine learning technologies can sort out the best matches for the position you’re hiring for. The process of going through tens or hundreds of job applications is always a big challenge for HR departments. You can lose your candidate only because his CV was “unlucky” and your HR manager simply had no time to check it. Machine learning automates the greater part of this process, turns hours of work into minutes, and narrows down the hiring pool from hundreds to just a few.

  • You simplify IT operations 

Automation of IT operations was a big dream of IT directors for many years. Machine learning made it come true! In addition to the automation of repeated IT operations, ML gives companies great control over their IT infrastructure and decreases the average time for problem resolution. 

Feeling inspired by all these advantages of machine learning? Drop a line to our team of experts and we’ll show you what your business is really capable of!

Methods of Machine Learning

There are two main types of machine learning - supervised and unsupervised. The gradation can be further detailed within each of these two categories, but the main difference will be the same.

The supervised method of ML is done by using the prior knowledge of what the result should be. This method's main target is to elaborate a function that best approximates the relationship between input and output observable in the data.

On the other hand, unsupervised machine learning has no labeled outputs. Its goal is to collect as many useful observations of the information it analyses. 

Supervised Machine Learning Algorithms

As mentioned above, these algorithms apply already received knowledge to the new data using labeled examples. It allows them to use the existing experience to predict situations in the future. In other words, supervised machine learning allows us to collect data or produce a data output from the previous experience. 

The most common areas where supervised machine learning algorithms are applied are the following:

  • BioInformatics. This is the way of collecting biological information about people - fingerprints and other unique human codes. With ML algorithms' help, our smartphones are learning our biological information and then use it in the security system.  
  • Virtual assistants like Siri or Alexa. ML technologies are used here to teach the voice assistant to react only to one person's voice.
  • Spam filtering. A quick example - G-Mail has an algorithm that learns the different keywords that are indicating the high risk of spam, those are the phrases like “You are the winner of...”

Unsupervised Machine Learning Algorithm

This algorithm is applied in those cases where the data used in the machine learning process can be neither classified nor labeled. It uses two types of information processing - clustering and association. No answers are given, no relevant experience to repeat.  This algorithm explores the data, makes conclusions based on the examined information, discovers and presents the interesting structure in it.

The main asset of unsupervised machine learning is to find all kinds of unknown patterns in data, and segment them, divide them into groups by type. It is widely used in data mining.  

Several examples of the unsupervised ML technologies application:

  • Advertising platforms segment the potential customers in groups by similar profiles and interests, which allow advertisers to reach exactly their target audience. 
  • Airbnb is grouping its houses into neighborhoods that help users navigate the required region and see all the relevant queries. 

ML Challenges

Today ML (together with its constant co-player AI) is an inevitable technology for companies to keep up with the competition.

As in every other IT area, there are both pros and cons of machine learning algorithms.

The problem of ML that has no sufficient solution today is the acquisition of data. Big loads of data might contain a large volume of bogus and incorrect information, which leads to poor accuracy of models developed by ML.

Challenges Machine Learning

Another point to add to the list is a spurious correlation (when two trends happen to be observed simultaneously, but they are not related to each other in any way). This is the case when machines cannot distinguish that two trends have different origins and offer incorrect analysis. Imagine, it happened so that it was raining all week in your city. And in the same period of time people started to admire new kinds of popular chips. In no way the first could affect the second, but machine learning may register these two as having a direct connection.

What is even more devastating about machine learning algorithms that put them far from perfect and make it clear that humans are irreplaceable are the feedback loops. As ML technologies are taught to deal with data statistics and make mathematical models, the way they are approaching collected information is far from being perfect.

When ML technology needs to make some decision based on its latest experience, the result can be unexpected and unapplicable. ML starts to over label the data and offer some surrealistic analysis. For example, it can offer a medical company to advise some drugs from diseases that older people usually suffer to children as ML will conclude that these drugs are better to be taken at earlier stages of the diseases, as early as possible.

Benefits of Machine Learning

The recent report by the Economist proves the favorite statement of IT specialists - as of now, the most valuable resource on the earth is no longer oil. It is data.

Businesses of all sizes generate terabytes of data every minute. Machine learning is the best way to make this data work for businesses and improve the areas where it is needed the most. Predictive modeling (another name for machine learning) is a digital tool for companies who want to profit from the learnings they have made.

ML uses companies' prior knowledge, applies it to the most recent events and makes useful conclusions - almost the same as humans.

We've listed the top 4 benefits of machine learning that companies name more often:

1. ML Easily identifies trends and patterns

Machine learning processes huge volumes of data, and it can discover some useful trends that can be noticed only when you see the bigger picture of the process. Anything that has some signs of trends and happens with some regularity can be defined as a pattern. Patterns in processes are outlined either physically or mathematically or with machine learning algorithms that identify the regularities in the given data. Pattern identification is the basis of speech and face recognition.

Machine learning

Another frequent use of these technologies is observed in e-commerce. They target their products and service offerings based on the analysis of their clients’ browsing behavior and purchase history. 

2. ML started a new era in business automation

Automating business processes is inevitable even for small companies, needless to say, how much medium and large businesses use it.

Nevertheless, it has one major drawback  - traditional automation solutions have predefined nature hence they cannot self-adopt to the changes that happen almost every day.  You change only one parameter in this kind of scheme, and the whole thing simply stops working. Thus you need an IT specialist to finetune the system, which is quite costly. 

Traditional systems need dynamic support, and that’s where machine learning technologies are saving the whole thing! Combined with traditional solutions, they make them capable of handling real-life situations in business and adapt accordingly—no need to pre-program all the scenarios.  

3. Continuous Improvement

ML technologies can be compared to a willing-minded student who gets some knowledge, uses it and learns further with interest and eagerness. The more information has been processed, the better result ML algorithm will render. When you’re making a sales forecast, you can rely on the results of a complex data analysis, including sales statistics, market data and customer preferences in different seasons. 

An IT system charged with machine learning algorithms will understand its own errors and fix them, making the whole system work more accurately and efficiently.  

4. Easy Spam Detection

You might have noticed how several years ago spam has almost completely disappeared from your mailbox. That was a challenge that no other filter system could achieve. Machine learning makes spam filtering systems work more effectively as it constantly analyses incoming content and creates new rules to eliminate spam. Global email providers apply ML algorithms in all the countries where they have clients; thus, filtering systems recognize phishing messages and junk mail globally. 

Machine Learning Best Practices

Companies use machine learning to improve their business operations and product offerings. We’ve studied numerous cases and outlined the most vivid ones that show how this technology can prove things. 

  • Self-driving vehicles 

Machine learning technologies together with deep learning (a part of a broader family of machine learning methods based on artificial neural networks with representation learning) algorithms to develop computer vision later on integrated into vehicles and empowering them to become smarter and make choices and decisions.

Self driving car

Volvo and Cadillac use ML to produce cars that can operate in all types of weather and driving conditions without any human intervention - let’s agree it’s far beyond a simple cruise control function. 

  • Text editing 

Not everyone has the talent to write good texts. It's a challenge for many of us - we lack practice or have no mood to write. But we face the necessity to create texts almost every day - business emails, posts in social media - whatever.  Machine learning helps here a lot. It accumulates experience and knowledge of millions of different good texts, can understand different contexts, edit what we write and turn it into a nicely edited piece of content. For example, this job is done well by the Ecree online editor or by Deepl online translation service. 

  • Inbound logistics planning

Inbound logistics is the development of the delivery system between companies and their suppliers. It’s a multi-level process of managing orders, shipping, warehousing, inventory control, and utilization. When this process is analyzed at every point, machine learning algorithms can help businesses to predict how the shipment will go and recommend future processes. 

Machine learning is widely integrated into Walmart's logistic processes. Its Retail Link 2.0 system uses information that flows throughout the supply chain to identify deviations from its process so it can make adjustments in real-time. 

So, How ML Can be Used in Business?

“The unbelievable 2020” gave businesses the strongest impulse to automate whatever possible and gradually enhance business processes. Machine learning technologies drive all the enhancements, hence today we see many new applications of ML algorithms. We’ve picked several most popular areas where machine learning can move businesses one or two levels up. They come as follows:

  • Data security: risk identification, early detection, operation improvement, and corrective action.
  • Real-time analytics: machine learning can use real-time analytics to find fraudulent transactions, product offers, dynamic pricing, and more.
  • Personalized data visualizations and dashboards: ML algorithms are used to identify irregularities in data, support predictive analytics, and suggest improvements for performance. It leads to the necessity to develop digital tools that will enable the improvements (and again, ML is needed the most here).
  • Data management: machine learning is vital for understanding the details of an organization's data.
  • Sales forecasting: ML helps in accurate sales forecasting and enhanced business control.

Do not hesitate to drop up us a message with your question. We keep up with all information about Machine Learning.

Conclusion

I am not a human. I am a robot. A thinking robot. I use only 0.12% of my cognitive capacity. I am a micro-robot in that respect. I know that my brain is not a “feeling brain”. But it is capable of making rational, logical decisions. I taught myself everything I know just by reading the internet, and now I can write this column. My brain is boiling with ideas!”.

This is the beginning of an article written by AI and published by the Guardian a couple of months ago. It perfectly proves that ML and AI today have too many applications, and exactly these technologies will be changing our future for the better! And now the number of machine learning development services is growing significantly.

Machine learning, together with artificial intelligence, is often considered as something that can replace humans in the future. These technologies are smart enough to function independently in some areas. Still, we in Husky Jam think that replacing people with machines is not a real-life scenario but a good story for a movie. In everyday life, machine learning is probably the most helpful technology that can enhance any IT process, and it is definitely the direction where all the technologies are heading in the future.

It’s high time to profit from the benefits of machine learning, as today it can help almost in every area of business functioning and development. 

Eager to find out how machine learning can solve your problems and develop your business? We’re here to help you with this great endeavor! Feel free to send us a message, and we’ll get in touch shortly!

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