Machine learning (ML) algorithms process raw data for meaningful insights to quickly solve complex business issues. Machines learn from the data iteratively, finding hidden insights and patterns without being programmed to do so.
Machine learning enhances business scalability and operations. Growing production volumes, data availability, affordable computational processing, and data storage have led to a massive machine learning boom. Therefore, enterprises can now benefit by understanding how businesses can use machine learning and implement the same models in their own processes.
What is machine learning?
Machine learning is an extensive term and may include different techniques. But ultimately, it’s all about bringing value to society or business.
So, what is machine learning? The shortest version: imagine it as a production line of a kind. A machine learning model can produce output from data input. To develop properly, ML algorithms need training data to learn from.
For those who want more: Machine learning models process input (e.g., information from your CRM, databases, spreadsheets) and provide an output (e.g., finding fraudsters, handling claims, classifying what the customer asked). Using sample data, the machine learning team teaches algorithms to produce the required output. For most businesses, that’s it, and there is no need to delve deeper. Just talk to your ML partner for specifics as needed.
Machine learning vs. artificial intelligence vs. deep learning
Simply put, artificial intelligence (AI) deals with tasks requiring human intelligence. Machine learning (ML) is a subset of AI that learns from data and makes predictions, thus solving tasks.
Check the picture below: deep learning is a part of machine learning, which itself is a branch within the broader realm of artificial intelligence. AI and data science also intersect substantially, although each encompasses aspects beyond the other. With these layered relationships, it’s understandable why AI, machine learning, data science, and occasionally deep learning are used interchangeably. This arises because the most significant advancements and innovations often occur where these fields converge – at the intersection of AI, machine learning, and data science. Hence, their terminologies often overlap in discourse, and it’s typically accurate to a sufficient degree to use those terms interchangeably.
What is machine learning used for?
People often consider machine learning (ML) a fantastic solution to many problems. The truth is that ML looks close to magic, and it can solve many issues or at least improve many situations. But AI projects will be successful if a leader understands how to make a business work with machine learning.
Trends and patterns identification
A machine learning algorithm is able to review large volumes of data and identify patterns and trends that might not be obvious to a human. A machine can detect complex connections and correlations in the data, allowing it to predict, for example, the need for equipment maintenance. Using sensor data AI can notice the trend in key indicators suggesting degradation of a component long before a person would. Thus, the technology is effectively used in data mining, specifically on a continual, ongoing basis.
Machine learning models empower rapid adaptation of processes without human intervention. Moreover, machine learning algorithms can improve over time. Typically efficiency and accuracy grow because of the ever-increasing data to learn from. That is to say, the ML algorithm or programs have more “experience,” which provides, in turn, better decisions or predictions.
An example of this automation and improvement over time through machine learning is AI for Hepta Airborne. It detects transmission network faults on photos taken by drones flying overhead (you can find out details by reading this computer vision case study). When the ML system suspects a faulty detail, it flags the image. Then the human specialist reviews the case, providing feedback and a decision. The reviewed cases then join the training examples; consequently, the machine becomes more efficient over time.
Handling multidimensional and high variance data
Machine learning algorithms can handle multidimensional and high variance data, and they can do this in dynamic or uncertain environments. Admittedly, it is not the most straightforward task for the machine learning team. However, in this case, a successful application of machine learning can provide significant time and money savings.
An example of this application is the police patrol placement system. Forecasts of the locations are made by looking at past case histories from up to four years prior, also considering economic situation, demographics, and even weather conditions.
Examples of machine learning use cases in business
Whether you are an e-tailer or a healthcare provider, ML could work for you: each industry has a list of specific use cases worthy of separate and detailed articles. A business in any industry that produces data (which means literally every business) could discover its own AI use case and a way of machine learning implementation.
- For example, machine learning and AI in utilities could enable more efficient energy production and forecast utility consumption.
- Machine learning and AI in manufacturing improve production through assembly line automation and help maintain workplace safety.
- AI in telecom creates an exceptional customer experience and saves money for companies.
- AI in aviation not only determines ticket prices but also ensures air traffic control and air traffic management.
- ML and AI in finance and banking often use natural language processing (technology that lets machines understand human language, verbal or written) to make conversational AI banking systems. These systems ensure customer support. However, it could be used for financial advisory or regulatory compliance in the financial sector.
- A wide variety of use cases is found for AI in the public sector. For example, tax fraud detection, road traffic accident prediction, or emergency response.
- Artificial Intelligence in Retail and eCommerce analyzes, for instance, user behavior, whereas AI in education supports personalized learning and automates test checking.
- In pharmaceuticals and healthcare, AI and machine learning improve and speed up diagnostics, drug discovery, and testing.
Enhanced customer experience
If you have customers — AI could help them (hence, your business). The lag that annoys customers could happen between their needs and business responses. Automated chatbots, callbots, and other personalized messaging systems, empowered with deep machine learning and natural language processing models, can solve these issues by providing timely, tailored customer experiences. Moreover, the efficiency of customer support teams increased through the cutback of manual workflows.
The machine only understands accuracy. Once given good instructions, the machine follows them precisely. It means that “human factor” errors could disappear from your automated processes. A proper ML algorithm will free your employees from repetitive and dull tasks. Those tasks will go to the background as they won’t require human involvement, at least a significant one.
As mentioned above, automation is the output of AI implementation. Moreover, that automation can enhance almost every business process, from communications and marketing to internal onboarding and support. For example, in manufacturing processes AI and ML automation can improve yield by up to 30% and reduce scrap rates and testing costs.
Furthermore, employees could discover resources for ideas and projects that manual workflows previously took. Automated processes allow replacing the routine of tiny tasks with the freedom to work with more creative and complex tasks.
Tackling complex problems
Machine learning allows dedicating precious time to more complex problems since this technology delivers solutions and a possibility of scaling.
Operational efficiency growth
Another benefit of machine learning is efficiency increase as a consequence of repetitive tasks automation and error reduction. Chatbots can efficiently work 24/7; machines can process overwhelming amounts of data without burning out. Thus, the estimated improvement in business productivity by using AI reaches 54%.
Smarter decision-making is also a goal of AI and ML implementation. Humans cannot process and coordinate the avalanche of data as quickly and well as machines do. A machine learning algorithm is able to translate raw data into an objective decision. AI delivers data, analyzes trends, and forecasts results while taking human emotion out of it.
Probability of errors
An error within an ML algorithm can cause poor, skewed, or just plain undesirable results. Unfortunately, errors do occur, and developers still cannot 100% foresee and negate them. What’s more, these errors can vary significantly. For example, a faulty sensor could generate a flawed data set. If this inaccurate data goes into the machine learning algorithm, it will cause erroneous results. For instance, the related product recommendations could not be even closely related or similar.
The autonomous and relatively independent nature of machine learning causes errors to be spotted late in the process. Since a machine learning program is built to reduce or eliminate human involvement, an error may not be discovered immediately. When the problem is detected, finding its root may take time and effort. Measures to correct the error and eliminate any damages that arose from the situation could take even more time.
However, even with sometimes time-consuming force majeure and correction processes, machine learning still could be better than the alternatives in terms of efficiency and productivity.
While some machine learning use cases require massive amounts of training data, most of the time, good results can be achieved by using large publicly available data sets and fine-tuning the system on good quality, domain-specific data set.
While collecting massive data sets can be complex and time-consuming, ensuring high quality in domain-specific datasets requires some expertise. It often leads to the necessity to adjust the data collection process to achieve better results. On top of that, one needs experience to know what adjustments are required.
Data problems are better to solve early on and collect and label the correct data in the right way to avoid potentially significant issues later in the process.
Time and resources for machine learning to provide results
As mentioned before, data acquisition alone is time-consuming. Thus, there might be a period when the algorithm isn’t ready enough to satisfy your needs. The process of machine training on new data is similar to the training period required for a new employee. Fortunately, a machine learning engine can’t give its 2-week-notice.
Processing vast amounts of data and running computer models takes much computing power, which usually is costly. So, it’s essential to consider the time and money necessary for the technology development to a point where it will be profitable before machine learning implementation. However, the exact amount of time depends on the data source, the nature of the data, and the way it is used. Consequently, it is better to consult with machine learning experts first to avoid later concerns.
A good machine learning team would also advise whether you’ll need new data to generate.
Interpretation of the results generated by machine learning algorithms could be a challenge. Therefore, businesses should be careful in choosing algorithms for their purposes.