In recent years, there's been a growing interest in machine learning technology, as it can provide a competitive advantage for companies in almost any industry. This increased interest in machine learning has boosted a demand for software based on this technology. However, business owners may face certain challenges when working on machine learning projects. This article offers a brief overview of machine learning applications and provides some tips for those who are interested in creating one.

 

Written by:

Anna Bryk,

Market Research Specialist

 

Contents:

Machine Learning Is Here To Stay

Machine Learning Methods and Techniques

Machine Learning Solutions around Us

Challenges of Machine Learning Development

Tips for Developing Your Own Machine Learning Solution

Conclusion

Machine Learning Is Here To Stay

Machine learning is widely associated with Artificial Intelligence (AI), but it's only one current implementation of AI. While the ultimate goal of AI is to imitate the human brain, machine learning simply finds patterns in existing data and tries to predict similar patterns in new data. The ability to learn from experience allows machine learning systems to automatically adapt to changes and make better decisions.

Machine learning technology is already being used for solving such tasks as image and speech recognition, web search and product recommendations, user behavior analysis, data protection, and many other purposes. The application of machine learning application are almost unlimited, so we can expect to see further uses of this technology in future.

Machine Learning Methods and Techniques

The following learning methods are currently used for machine learning systems:

  • Supervised learning teaches machine learning systems to compare actual inputs with correct outputs using labeled or known data. Typical business uses of supervised learning include recognizing objects in images, predicting financial results, detecting fraud, and evaluating risk.
  • Unsupervised learning uses algorithms aimed at exploring unknown data to discover patterns and structure the data. Common use cases include clustering customers for audience segmentation, managing identities, researching multi-factor natural phenomenon, categorizing news, books, and other things, recommending items to customers.
  • Semi-supervised learning combines a small volume of known information with a massive volume of new data in instances when labeled data is too expensive for training process. This type of learning is used for detecting spam, classifying web-content, and analyzing speech.
  • Reinforcement learning uses a trial-and-error paradigm to teaching a system the ideal behavior that will lead to a desired result. This method are mainly used in robotics, games, and self-driving cars.

Machine learning methods

Machine Learning Methods

Deep learning is an innovative machine learning technique that tries to simulate the process of human thought processes through neural networks. Deep learning systems can process massive volumes data, also known as big data, in multiple processing layers in order to make complex decisions in real time.

The main advantage of deep learning (and the key to its huge potential) is that it effectively combines automated feature extraction with task-specific decision-making. It allows people to use built and already trained neural networks for new tasks. Most successful machine learning solutions related to image recognition apply deep learning.

Machine learning algorithms are particularly well-suited for finding patterns and abnormalities in big data through an advanced process called data mining. In data mining, intelligent methods are applied to uncover connections in unstructured data, give the data an understandable structure, and use it to predict outcomes. Data mining is based on neural networks and uses artificial intelligence techniques and advanced statistical tools.

Since artificial intelligence is based on learning, machine learning technology is an integral part of any artificial intelligence system.

Machine Learning Solutions around Us

Machine learning solutions are widely used in most popular services that deal with cybersecurity, big data, and cloud computing. Here are some examples of sucessful machine learning applications:

 amazon machine learning

  • Amazon Machine Learning is a cloud-based service that allows engineers to create new models by applying off-the-shelf algorithms and advanced visualization tools. In addition, developers can test their models with the help of simple APIs. This service is effective for improving new applications, as developers can see how their product will interact with end users and can forecast product demand.

 ibm watson analytics

  • The artificial intelligence platform IBM Watson uses machine learning technology to process information and apply advanced analytics to business. The IBM team provides Watson with a massive knowledge set from public and private sources in a way that doesn't violate privacy. Watson Analytics performs predictive analysis, detects patterns in data, and visualize the results. This cloud application can interact with you in the form of questions and answers due to its ability to understand natural language. Using the Watson platform, companies can make better decisions about their business and meet new market challenges.

 AI2

  • AI2 was founded by PatternEx in cooperation with MIT’s Computer Science and Artificial Intelligence Laboratory. This cybersecurity platform can predict attacks and withstand intrusions significantly better than existing security systems. With the help of unsupervised learning, the platform clusters all available information and finds meaningful patterns. Analyzing system reports, security experts can confirm malicious attacks and apply supervised learning to train the AI2 with new labeled data for even better detection of suspicious activity in the future.

 google machine learning

  • Google has widely implemented machine learning technologies in its products and services to benefit from the massive information it can obtain by doing so. For instance, using semi-supervised machine learning, Gmail Inbox can automatically categorize your emails and offer a Smart Reply feature. The Cloud Vision API provides developers with powerful machine learning models for processing image content. Additionally, the Cloud Machine Learning Engine allows technical professionals to train their machine learning models at scale.

 azure machine learning

  • Microsoft has developed its own Azure Machine Learning Studio, which offers ready-to-use algorithms for creating machine learning models and getting predictive analytics solutions. Using the Azure Machine Learning Studio, developers can create their own ready-to-use web services.

Challenges of Machine Learning Development

Business owners should understand the challenges they may face when creating machine learning solutions.

  • Advanced analytical technologies. Unlike traditional software, machine learning systems use advanced analytical algorithms in order to automatically make a human-like decisions. However, it's quite challenging to develop a viable mathematical model to describe a subject domain. Business analysts must closely cooperate with a team of mathematicians (who build a mathematical model for a subject domain) to let them know about all the business limitations and advantages of ML technology within the context of the current task.
  • Data dependency. Data is vitally important for training and testing machine learning systems. The more data you have the better your solution will be. Besides, data used for machine learning analysis often changes, so there's a constant need for relevant data. If you want to work on a machine learning product, data is the first thing you should take into consideration. Thus, you should either invest in gaining new data or obtain access to big data through providers.
  • Quality assurance challenges. Quality assurance experts may face difficulties testing machine learning solutions, as traditional practice is not applicable for these types of systems. Testers may fail to determine the correctness of input and output data and algorithms if they apply traditional approaches to ML software. In the case of machine learning, testers can only check the system interface, while developers need to test system performance; quality assurance testers usually don't know how to determine an algorithm’s efficiency. Obviously, there may be some cases when the system will make mistakes that are uneventful and are actually admissible for machine learning algorithms. Specially trained testers or machine learning developers should check whether these system mistakes are too frequent. There are mathematical metrics for each type of algorithm that evaluate different indicators of their quality and performance.
  • Data privacy and other legal concerns. Using big data in machine learning raises many concerns regarding the privacy of personal information and the disclosure of sensitive data. In order to avoid information discrimination, you can perform feature analysis using processed data that doesn't contain any identifying information. Additionally, some machine learning developers try to use containerization technologies for data processing that allow them to create the minimum number of libraries without using any personal data. Microsoft offers to convert trained neural networks to CryptoNets that can deal with encrypted data.
  • Uniting the efforts of model builders and system engineers. These two types of developers take different approaches to machine learning, but only the synergy of their efforts can ensure a high-quality result. The complex architecture of machine learning requires model builders to create a model that is the core of the system and test its predictive accuracy. Meanwhile, system engineers need to concentrate on system design and performance using dynamic programming languages like Python or R. Some system performance requirements can force model builders to re-consider the model itself and modify the algorithms used.

Any project involving machine learning requires a development team with very specific knowledge and expertise. You may need the following people on your team:

  • Analysts and developers with advanced math background. Analysts should have strong math backgrounds so they can build sophisticated mathematical models. Though there are libraries with pre-build algorithms, it’s almost impossible to imagine a real-world task for which an out-of-the-box algorithm would work without pre-processing or modifications. At the very least, some algorithm customization will be required. A lack of developers and analysts with advanced math backgrounds can make machine learning development almost impossible.
  • Developers with backgrounds in data science and data architecture. Machine learning technology works with a massive volume of raw data, so data science specialists are crucially important for data analysis. Even if you have technical professionals who are familiar with machine learning basics, they may not be able to handle the advanced data integration necessary for machine learning solutions. Experts who also understand statistics and data mining tools will bring a significant benefit to your project.
  • Developers with heavy data engineering skills. Machine learning developers also need advanced skills in data engineering that they can apply to acquiring data. These skills include a deep knowledge of databases and engineering practices for real-time data processing.

Tips for Developing Your Own Machine Learning Solution

Here are some tips for business owners who want to work on machine learning projects.

Become familiar with machine learning technology

Though machine learning solutions include some traditional tools and practices, engineers still require additional knowledge and skills specific to machine learning algorithms and architecture.

Analysts should have a thorough understanding of machine learning technology in order to build mathematical models. They also should know the advantages and limitations of different algorithms so they can select the best ones for each task. Developers need experience in machine learning so they can properly deploy, test, and refine systems.

MLP

Machine Learning Development Process

Define the problem and prepare data

Data representation and mathematical model are key elements of any business task to be solved by a machine learning solution. You need to define what objects are involved, then find an efficient way to represent them mathematically. Then you need to interpret processes in mathematical terms, building a mathematical model of a real-life task. Only after the problem is modelled mathematically can your team start selecting the right machine learning algorithms.

Select machine learning algorithms

Consider all machine learning algorithms available, keeping in mind criteria such as scalability, reliability, and efficiency. It's important to understand that there's no universal model, so it may be reasonable to apply two or more algorithms and compare results. For instance, when Apriorit worked on computer vision with OpenCV, we picked up two approaches: machine learning algorithms and a background subtraction algorithm.

Use machine learning platforms and toolkits

There's a great variety of open source libraries and toolkits that you can use for machine learning development. You can employ several of them in your solution for better testing of your model.

Depending on your needs, you can apply mlpack in C++, Caffe in Python and C++, or NuPIC for streaming analytics in Java, Python, Clojure, JavaScript, C++, and Go. If you want to implement deep learning in Scala or Java, use a programming library such as Deeplearning4j; Theano or Neon will provide you with the best opportunities for Python. If you deal with human language data, then NLTK will help you build programs in Python. Shogun toolbox is popular among machine learning developers as it can be used for many languages, including MATLAB, Octave, Python, R, Java, Lua, Ruby, and C#.

As you can see, Python is considered one of the most convenient languages for machine learning . However, C/C++ is also widely used for developing machine learning applications.

Google, Amazon, and Microsoft have already made available their own toolkits for developers, so you can use one of them to create your own product as well.

Apply and adjust algorithms

Conduct experiments to evaluate the results of each algorithm you use. Document every step and your results, as this will be valuable for the further development of your solution. Keep in mind that your data is changeable, so you need to choose the most flexible algorithm. If you're unsatisfied with your results, think about how you can customize your existing algorithms to improve outcomes.

Experiment with your machine learning solution

Deploy your machine learning solution in order to create a first use case. Continuously monitor and test the performance of your machine learning algorithms, which tend to regress over time because of variable data. Evaluate and track the metrics of deployed algorithms periodically so you can properly measure the model’s prediction accuracy.

When the deployed model's accuracy drops over time, you'll need to update it or choose another model. You may also need to retrain the system to achieve your targets. Machine learning solutions require continuous management to maintain their quality and performance.

Conclusion

Machine learning technology has a unique capability to process data without human interference and existing applications show its great potential. However, developing machine learning software is more complex and challenging than developing traditional software.

Development of machine learning solutions requires having a deep understanding of machine learning technology, as well as advanced knowledge of mathematics and data science. Apriorit has an experienced development team whose skills in business analysis, data processing, and software development can help you successfully implement your own machine learning solution.

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