Modern business requires using all advantages available. This happens due to constant growing expectations, client requirements, and brutal competition in various markets. The most significant benefits companies can gain all related to machine learning. This technology can significantly boost the efficiency of all business processes, starting with supply chains and marketing and ending with launching new products.
This article will tell you about the experience of various companies that use the privilege of artificial intelligence on machine learning at each level of their business processes. Also, we will share a piece of advice on how to choose an ML developer that will help integrate this technology into your business.
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What is machine learning?
What is artificial intelligence? According to the most popular definition on the internet, Artificial Intelligence (AI) is a program or a machine that can function likened to humans. This means that AI can imitate the cognitive functions of humans, perceive the environment, react to it, and act to maximize the chances of AI reaching the goal. By cognitive functions, they mean self-learning, searching for solutions according to an algorithm, and also the ability to create solutions based on a relatively small volume of information and previous experience and knowledge.
What is machine learning? In general, it is the path to artificial intelligence, one of the cognitive functions mentioned above, namely — self-learning. It goes without saying that a human's learning process and AI software are different, yet the fundamental concept is alike. Machine learning (ML) is the most efficient way to teach a program and provide it with experience that can be employed later or uploaded to another machine, teaching it everything that the original machine knows.
There are two phases of developing software using machine learning. Firstly, it requires writing the ML algorithm. Secondly, teaching it how to accomplish various tasks. There are several options for machine learning (with/without a teacher, with support, with partial backing, etc.). The possibilities are commonly related in one way or another to the following cycles: AI takes decisions or acts and analyzes whether it is now closer to reaching the goal. Regularly, it requires analyzing thousands or even millions of scenarios, and after the AI algorithm shows at least 80% of successful results.
The thing is that systems that use machine learning technology process the input data autonomously to create and then test algorithms that will help to reach the goals. It is essential that machine learning is not generally done in practice since it does not require a lot of money, resources, and time. Usually, the developers use data models (it is some kind of advanced development option). These models help to adapt the same ML algorithm for various environments and tasks.
A brief explanation of artificial intelligence, machine learning, and deep learning.Source
What can AI and ML introduce to your business?
Enhancing the functionality and the performance of the products. The most significant improvement that AI/ML introduces to business is intelligence and automatization. These features can replace human labor and increase the performance and the bandwidth of the transactions.
Optimization of internal business processes of a company. Businesses can also use this technology to optimize and automate invoicing, customs calculations, tax deductions, filling out invoices, and other paperwork and accounting. ML makes the processes faster, cheaper, and more secure since no corruptions, sabotage, or faults on the user side are possible.
Offering the employees more time for creative work. Automatization of business processes can create conditions that will allow the employees to focus on completing tasks without any need to go through repetitive things a thousand times every workday. For example, a chatbot on ML can answer up to 80% of users’ inquiries (usually, these are simple problems). Hence, the support team member can focus on complicated clients’ issues.
Make more thoughtful decisions. Even experienced leaders sometimes make wrong decisions based on emotions, prejudice, or lack of time for analysis. AI makes it more straightforward since it does not have sentiments or prejudice and can process vast data.
Forecasting and optimization. AI can use various analytical methods to forecast multiple processes. Typically, these are models that help to forecast weather, demand, sales volumes, warehouse supply, equipment loads, social trends, demographics, as well as yields for short-, medium-, and long-term.
Natural language processing. ML simplifies the interaction of the users and the digital world with the help of chatbots and virtual assistants. They are already so advanced that it would be hard to see the difference between real support teams, sales managers, and AI assistants. However, the technology still has difficulties with recursive connections.
Data analysis and analytics. The modern world is competitive, and it requires making quick decisions in real-time. Employing advanced analytics with AI that uses ML might help identify market trends, hidden connections, anomalies, etc.
Recommendation models. These models increase the users’ engagement by offering them products and services following their tastes and former experience. Famous examples are recommendation algorithms on Spotify, YouTube, TikTok, and Amazon.
Computer vision. This type of AI allows machines and computers to understand and analyze virtual information. It includes the methods of observation and searching for patterns, and classifying the data obtained. Governments typically use these systems to identify traffic and other law violations. Private companies use computer vision on ML algorithms for security purposes and to increase the efficiency of working processes (e.g., punishing work-shy employees).
Identification. One of the most common ways to employ machine learning is to find patterns and trends for a quick and efficient photo, video, and audio content identification. Also, it helps to fight fraudulent activity, lies, internal weak points, attacks on the security systems, etc.
An example of this type of ML system is Google’s search via photo.
Employing AI/Ml in various industries. Source
The features of ML solutions development
Writing the algorithm of machine learning for business is a complicated process that requires a solid mathematical background since all input data (images, texts, audio files, etc.) must be rendered as numbers or similar numeric structures called tensors. Then the ML algorithm will process it within the neural network. Furthermore, the volume of data required for receiving valid results is enormous; sometimes, the number reaches petabytes of information.
The process of developing ML solutions includes three key components. They are described the following way:
Hence, the development of the AI system on ML starts with creating a machine learning model that includes the three components mentioned above. This model is tested, which in the first step is usually based on mathematics: roughly speaking, the developers see if the ML algorithm can conclude that a+b=c. Of course, what exactly is "a," "b," and "c" depends on the task of the particular ML model. A simple example: you let the ML algorithm examine many photos and identify where people are in them, and at the same time, after each conclusion, ML tells whether it is right or wrong or whether there are people in the photo.
Real input data can be used when a working machine learning model has been created and tested. This frequently starts with fairly small samples to test the adequacy of the model's output, as it is often unpredictable due to some unknown variable. This is important because this factor can deviate the model from the desired goals.
Next, the developed ML model is tested on so-called training datasets, which "train" the statistical probability tool to get the right predictions - the right answers/actions. How exactly the ML model is "trained" (what exactly happens inside the ML program) because we don't know exactly. No one knows this, not even the biggest experts in artificial intelligence and machine learning, so it is now common in the community to instruct artificial intelligence to explain how and why it made a particular decision/action.
After training on training sets, the ML model is used on real data (actual business processes) to find the customer needs solutions, such as optimizing supply chains or communicating with customers. And here, you need to remember that success never comes the first time. Developing ML probability requires multiple attempts, known as "cycles," multiple training sets, and a lot of practice on the real business processes of a particular company. In this way, the ML model will come to create a reference probability field.
- Model. Here the design part lays out the basic theoretical infrastructure about how the machine should learn, what it should learn, and what it should do with the experience and knowledge.
- Parameters. Constraints are barriers that force a machine to use data differently. Just as children learn to use language correctly instead of shouting, the ML machine learns to make certain inferences with its input data.
- Student. This is where the fun begins. This is the component where the ML machine can adjust parameters to refine the output so that the reality of what is happening gets closer and closer to the prediction expected by the machine-learning AI system.
Where to look for and how to find ML developers?
You can find a development company for ML solutions on LinkedIn, Goodfirms, Upwork, or Toptal. If your project is blockchain or cryptocurrency-related, you can monitor Coinality, Blocktribe, BountyOne, Crypto.jobs, Beincrypto, or Cryptojobslist for a technical partner. Consider the following points to select the best candidate:
Technical skills. There are two types of technical skills required for machine learning: AI researchers who develop new algorithms and machine learning engineers that employ ML algorithms to create business applications. Here is a simple example: if you want to serve food, you need chefs (ML engineers), not electrical engineers (researchers), who build home appliances. However, if your task is unique and essential, you need to ask the ML researchers first and then ML engineers. Furthermore, you will probably need help from a data engineer since teaching an ML model is impossible without processing vast volumes of data.
Here's a description of the roles and competencies of critical positions in ML solution development.
Experience (portfolio). The best way to see what the technical partner can do for you is to check what they have done for other clients. Hence, before hiring an ML solutions developer, study their portfolio and cases related to your project.
A cryptocurrency exchange platform development case. Source: Merehead
Communication skills. Last but not least significant criteria are the communication skills necessary for coordinating the processes with the clients and the team. You can find this information by directly contacting your candidate's former clients and employees. Also, you can read the feedback on Clutch and Good firms for clients’ reviews and Fairygodboss, Glassdoor, Vault, CareerBliss, JobAdvisor, WorkAdvisor for employees’ reviews.
Please pay attention that honest feedback will include the information of the reviewer: name and surname, company, post, link to social media, or other contacts. Here is an example of excellent feedback on Clutch.com.
Why is Merehead a good choice?
Since 2015 our company has offered technical support for developing software products that function on machine learning fundamentals. We are sure that you will reach the best results possible with us when launching ML solutions in finances, marketing, blockchain, communication, and other industries. We deliver the project on time and for a reasonable price.
The advantages we offer are:
5+ years of average team experience. Our machine learning developers have extensive experience and constantly develop their skills to stay on the edge of progress and create responsive and innovative software products.
Honesty and transparency. Our team works transparently and abides by strict non-disclosure agreements to keep your idea as safe as possible.
Straightforward project management. Our company provides full transparency of the project. We use a flexible interaction model that allows you to monitor development progress and promptly make the changes you need.
Reliability and security. Our software products based on artificial intelligence and machine learning have a high degree of reliability and protection against malicious actions, from fraud and data leakage to DDoS attacks.
- Defining the aim. Contact us and share your business idea; we will help determine the requirements of your project and select the AI and MEL technologies necessary to implement your project.
- Developing the UI/UX design. Next, our business analysts and designers will study the niche, target audience, competitors, and relevant trends, and according to that data, we will create an efficient user interface.