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In fact, businesses invested around 28.5 billion in machine learning worldwide in 2019.
If you’ve ever wondered how to build a machine learning app, this article will answer your question.
Here’s what you are going to learn:
Let’s dive right into the article explaining machine learning software development.
Machine learning is one of the subsections of science dedicated to the development and study of AI. It is focused on building systems that learn and evolve through data analysis. Machine learning (ML) is closely associated with Artificial Intelligence (AI). Machine learning allows AI-powered applications to learn from collected data and improve their accuracy over time without manual programming.
Machine learning algorithms find patterns in massive amounts of data to make data-based predictions and decisions. The more data ML processes, the better algorithms become and the more accurate predictions and decisions they make.
Below, you can see a visualization of machine learning paradigm basics.
Machine learning app development is a popular practice today. A lot of solutions that you use every day rely on ML. Let’s review some of the most famous machine learning implementation examples.
Netflix is one of the most obvious machine learning app development examples.
Have you noticed that Netflix offers you the most interesting content?
That’s because machine learning together with artificial intelligence is learning and analyzing your content preferences all the time. They are laying behind its robust recommendation system.
For example, if you watched a movie yesterday, the next day Netflix would recommend similar content for you. And if you started watching a movie, abandoned it, and gave a bad review, machine learning algorithms would understand that you don’t like such content and won’t provide you with similar recommendations.
If you want to know how to build an app like Netflix, check our article explaining how to create such solutions:
|How to Start a Streaming Service: Step-by-Step Guide for Ambitious App Ideas|
Tinder is another great example of machine learning application development. Unlike what it might seem for end-users, the solution doesn’t use spells to find their perfect match. Instead, machine learning analyzes how users swipe right and left and, based on the collected data, provides them with more relevant recommendations.
You can learn how to create the next rival of Tinder in our article:
|How to Create a Dating App: From Concept to Successful App|
Snapchat is an example of a powerful combination of machine learning and augmented reality. The application uses augmented reality and ML to identify user’s faces, create a 3D mesh and deform it to apply different filters.
Even though many users don’t suspect it, this platform relies on machine learning as well. The company uses this technology for a number of tasks, such as content discovery, spam moderation, advertising, etc.
Examples of machine learning applications are numerous. You can also find such solutions as Google Maps, Twitter, Yelp, and others among examples. Check them out to understand how ML works in real life and to get a few inspiring machine learning app ideas.
Smart and self-driving cars have already entered our lives. Artificial intelligence enforced with machine learning algorithms are used to monitor the traffic and predict changes. The combination of these technologies allows for cars to drive without human assistance.
Machine learning is accelerating the healthcare industry. Such solutions collect and analyze patient data to diagnose and advise possible ways for treatment. Predictive healthcare is gaining traction.
The finance industry has a huge amount of data that needs to be analyzed to make predictions for future data collection, management, investment, and transactions. Machine learning and artificial intelligence are used in the industry for fraud detection and fraud prediction. For this purpose, the technology is also used in other industries.
Cybersecurity is another industry benefiting from machine learning. The technology can analyze data about incident reports, alerts, and more to identify potential threats and improve security analysis or even advise response.
Machine learning offers wide opportunities for manufacturing. Companies with assembly lines can use ML algorithms together with sensors and artificial intelligence to predict the need for maintenance. Thanks to their use, companies can avoid unplanned downtown and drops in production.
As it turns out, machine learning is widely used for smart and data-driven predictions. The eCommerce industry already benefits from this technology. Personalized product recommendations increase the average order value and revenue.
It’s another industry that can benefit from the use of machine learning. This technology can be applied in the ads industry to improve the efficiency of ad campaigns by targeting users with highly personalized ads tailored to their specific interests and needs.
As you can see, machine learning and artificial intelligence can fit into any industry. Automation adoption is revolutionizing different aspects of our life, placing decision making from humans and on technology and reducing the risks that some aspects might be overlooked, leading to wrong decisions.
You can also watch this Machine Learning tutorial video.
Machine learning application development is a process consisting of a few consecutive steps.
You can make an app using machine learning by following the below plan:
Let’s review each of the steps in more detail.
Start the task of machine learning app development by identifying the main work for it. You need to understand what problem you have and how machine learning should approach it.
Answering the following questions will help you move on with this step:
When dealing with this part of machine learning application development, you need to remember that machine learning can recognize only the patterns it has seen before. Therefore, the data is crucial for your objectives.
After you have framed the problems, it’s time to start one of the most important steps of machine learning development – the collection of data. You can either collect data from your own historical database or use open data sources.
After the data is collected, you might clean it from irrelevant data inputs. This will directly affect the accuracy of your machine learning algorithms and the accuracy of predictions in the future.
The most important thing to understand is that machine learning can only analyze numbers. It means that you need to convert all the data, such as text or images, in the format that your ML will understand. You will also need to create a data pipeline that will consolidate data from multiple resources to make it suited for analysis.
In some cases, row data doesn’t have specific characteristics needed for machine learning algorithms to work properly. That’s why you need to start feature engineering, a technique that creates additional features combining two or more data features in an arithmetic operation that is more relevant and sensible.
As statistics show, data preparation is the most time-consuming part of machine learning app development.
The next step is model training during which your machine learning algorithm will become better in data analysis.
Before the training starts, you need to split the data into two parts: training and evaluation. The second batch of data will help you understand how well the model generalizes to unseen data.
After you start the training, the algorithms will learn the patterns and mapping between the feature and the label.
You can approach model training in different ways and control its learning rate, regularization, number of passes, and much more.
It’s time to evaluate how well your machine learning algorithm is doing its job. You can use different accuracy metrics, such as over-fitting or under-fitting, etc. Depending on the results you get, you need to optimize the algorithms and training data to improve its accuracy.
This is the last step of machine learning app development. After the algorithm training is over, it is ready to be deployed into production. In simple words, you need to integrate it into your software to make predictions based on real data and for practical use.
This is the most difficult part of the development and requires deep technical expertise.
Machine learning involves working with large sets of data. The specifics of it create a quite challenging situation when you need to develop machine learning with data privacy in mind.
Let’s overview some of the principles that will help build a solution compliant with modern data security requirements.
One of the main concerns of machine learning is the use of information that identified a person. Most privacy lows list data anonymization as one of the key requirements. When you decide to build machine learning, you will need to decide on the technique to anonymize the data used.
One of the possible ways is to apply one-way hashing when data is converted into a number that the original data can’t be derived from the single number.
Aside from data that identifies an individual, you will also need to anonymize personally identifiable data. General Data Protection Regulation (GDPR) clearly states that you can’t store such information without infringing the regulations. There are also other local regulations, such as The California Consumer Privacy Act (CCPA) that you will need to abide by to avoid any problems with the law and possible lawsuits against your company.
According to GDPR and CCPA, users need to provide their consent for data usage by a company. If a business doesn’t have such consent, it can’t use the data.
However, you will also need to meet the requirements of the rule named the right to be forgotten. Users need to be able to withdraw their consent already given to a company and require you to erase all the data you have on them.
Machine learning algorithms make it quite challenging to erase the data already added into the system. Thus, it’s important to design the whole machine learning system with the “right to erase” requirement in mind.
Machine learning processes a lot of data creating a number of challenges for businesses that decide to pursue the idea of ML development. Machine learning algorithms should be designed with data privacy in mind, allowing you to avoid any problems with local regulations in the future.
Let’s review some of the most popular machine learning frameworks.
TensorFlow is a popular framework from Google that is particularly suitable for building deep learning models. Such models use artificial neural networks to enable progressive learning and improvements of the system. The framework is based on a computational graph consisting of a network of nodes. Each now is an operation that performs some complex or simple function.
PyTorch is a direct competitor of TensorFlow as it provides huge capabilities for building deep learning models. It’s also a part of a bigger Torch deep learning framework used by developers to build deep neural networks and perform complex computations.
If you want to know how to build a machine learning app for educational and research purposes, Shogun is a great solution. The framework can process large data-sets and supports vector machines for regression and classification.
Apache Mahout targets mathematicians, data scientists, and statisticians who need help with algorithm execution. This framework has built-in algorithms for distributed linear algebra and concentrates on such tasks as collaborative data grouping, classification, and filtering.
Konstanz Information Miner (KNIME) is a great software that can be used for data visualization, modeling, and analysis without complex coding. The framework uses a modular data pipeline concept combining different components for machine learning and data mining is a single solution.
Weka comprises several machine learning algorithms that are used mainly for data mining. You can perform different functions with it, such as regression, classification, clustering, data preparation, and visualization. If you are just starting up in machine learning, it’s a great tool to explore.
RapidMiner is a comprehensive environment where you can perform different tasks, such as data preparation, machine learning, deep learning, text mining, and predictive analytics. This solution is widely used for education, research, and development purposes.
Below you can see a table comparing the most popular machine learning frameworks mentioned above.
|Cost||Platform||Written in||Software license||Algorithms & Features||Solutions & companies using|
|TensorFlow||Free||MacOS, Linux, Windows||Python, C++, CUDA||Apache 2.0||Library for dataflow programming||Snapchat, Dropbox, Deepmind, Twitter, Uber|
|PyTorch||Free||Linux, MacOS, Windows||Python, C++, CUDA||BSD||Autogram Module
|Facebook, Google, Twitter, NYU, IDIAP, Purdue|
|Shogun||Free||Windows, Linux, UNIX, MacOS||C++||BSD3 with optional GNU GPLv3||Support Vector Machines
Hidden Markov Models
Linear Discriminant Analysis
|Universities for educational courses|
|Apache License 2.0||Preprocessors
Distributed Linear Algebra
|AOL, Foursquare, Drupal, Intel, Twitter, Yahoo!, LinkedIn|
|Java||Works with large data volume
Text mining (plugins)
Image mining (plugins)
|Planned Systems International, Inc., Whole Foods Market Inc|
|Weka||Free||Linux, MacOS, Windows||Java||GPL 2.0, GPL 3.0||Data preparation
Association rules mining
|Monkeylearn, Sclera, Skymind, Trust Sourcing, Rubygems|
Small: $2500 per year.
Medium: $5000 per year.
Large: $10000 per year.
|FirstEnergy, Ameritrade, Hewlett Packard Enterprise,
Since there are numerous companies available on the market, you need to choose the best one. The below list of questions will help you make the right choice: