Machine learning or ML is changing our everyday life. Nowadays, software engineers tend to integrate this powerful technology into the development process. ML can make application building more profitable and smarter.
Such enterprises as Google and Microsoft have already created machine learning platforms. As a result, the developers got an opportunity to implement artificial intelligence features into the software they create.
In our article, we will discuss several popular machine learning platforms as well as study application where this technology has been already integrated.
What is MLaaS?
Machine learning as a service or MLaaS tends to handle matters connected with the infrastructure. As a result, software engineers shouldn’t mess up with model training and evaluation.
Simply saying, ML as service defines different cloud-based platforms that tend to work with such issues as data processing, model training, and model evaluation.
Moreover, MLaaS providers offer various tools such as data visualization, APIs, face recognition, predictive analysis, and so on.
There are several powerful examples of MLaaS in the IT market. For instance, such tools as Amazon Machine Learning service, Google Prediction, and Microsoft’s Azure Machine Learning don’t require much technical knowledge for getting started with them.
Top Machine Learning Platforms
There are several platforms that we’ll discuss as examples of successful machine learning integration.
1. Amazon Machine Learning
This service is one of the leaders in the market. It’s a perfect solution for the projects that have strict deadlines. However, the level of automation of this service causes some limitation-related issues. As a result, we can’t call it the most flexible ML service in the IT market.
There are a lot of use cases of Amazon machine learning platform. They vary from the classification of documents to detection of frauds. Moreover, Amazon ML service provides SDKs for different programming languages such as Ruby, Node.js, Java, and so on.
2. Azure Machine Learning
Azure machine learning is a cloud service that provides you with opportunities to develop, deploy and share predictive analytics solutions. The service is focused on the ML Studio framework that offers to develop modular solutions.
So, this machine learning platform tends to offer solutions for both — newbies and data science adepts. However, you can perform this platform’s operations manually. There are good documentation and a lot of ML solutions stored in a Cortana Intelligence Gallery. So, the developers can use these solutions in the future.
3. Cloud Machine Learning Engine
Google was one of the first that developed machine learning as a service platform. The service was called Cloud Prediction API. However, in 2018, the company decided to deprecate the project.
Since machine learning technology is beneficial, Google created a new product — Cloud Machine Learning Engine. The platform tends to provide the developers with an opportunity to build and run powerful ML models in production.
Google has developed an MLaaS that provides software engineers with a lot of use cases. For instance, there are cases for detecting spam, recommendations, and so on. Google provides the developers with detailed guides that can assist you during the creation of some basic models. Also, it’s necessary to mention that Google offers pre-trained models to make the work of software engineers easily.
There is well-written API documentation. As a result, developers can change the platform as fast as possible. To sum up, Google has created a machine learning environment that is perfect for integrating ML into a project development if you are limited in time.
This service was developed to satisfy the needs of each user. As a result, BigML covers a lot of aspects. For example, there is a wide range of prices along with good documentation.
BigML offers a lot of robustly-engineered ML algorithms that are able to solve problems of the real world.
The developers tend to use this machine learning as a service platform for basket analysis, predictive maintenance, and more. The service provides opportunities to use ready-made scripts.
This platform provides various predictive applications for such industries as aerospace, energy, entertainment, financial services, food, Internet of Things or IoT, transportation, and so on.
If you need to be responsible for your machine learning deployment, this service is a perfect choice for you. PredictionIO is an open source server that provides developers with an opportunity to build predictive engines.
Apache PredictionIO is an open source ML service that allows software developers as well as data scientists to build predictive engines for any ML task. For example, using PredictionIO you can do the following:
- Create and deploy an engine as a web service fast;
- Respond to dynamic queries;
- Evaluate and tune several engines at the same time;
- Speed up ML modeling;
- and others.
Moreover, PredictionIO has SDKs for a lot of programming languages such as Java, Ruby, PHP, Python, and so on.
6. IBM Watson
IBM has several capabilities that can assist the developers. For example, this platform is able to visualize data and describe how various data values interact with each other.
IBM Watson was created to bring automated predictive analytics and cognitive capabilities. The service fits data scientists as well as users with no technical background.
The platform is constantly developing and starts to offer more and more interesting use cases. For example, the service allows speeding up the development process due to the automation of model training.
TensorFlow is an end-to-end open source machine learning platform. This platform tends to assist software engineers in developing and training machine learning models. TensorFlow claims that the platform is perfect for both — experts and beginners.
This machine learning platform provides a lot of tools, libraries, and community resources that allows to create and deploy ML application easily. Moreover, the service provides guides that show the workflows for text classification as well as describe steps to train and evaluate a model.
A lot of enterprises have chosen to use TensorFlow. For example, Airbnb, Coca-Cola, Twitter, and so on.
Choosing the Data Storage Solution
Looking for appropriate storage for collecting information and processing it can be a challenging task. However, you can utilize such databases as NoSQL and SQL. A lot of solutions (e.g. Amazon S3, Cassandra) support this combination.
Machine learning platforms allow making these processes easier. You can use the same provider for both — data storage and machine learning. Choosing this way, you can avoid problems with adjusting a data source. However, there are some platforms that offer integration with third-party tools.
Applications that Use Machine Learning
There are apps that have already integrated machine learning. So, let’s discuss them more precisely.
Snapchat is popular among millions of users and utilizes machine learning to add more fun to snaps. Let’s discuss an example. Snapchat provides users with different filters. They are able to track people’s faces correctly and add such things as glasses, hats, and more.
For humans, it seems easy to recognize the face but it’s a complex task for machines. So, the algorithm behind this feature has been taught to recognize not only faces but also facial characteristics.
One more wide-spread application also uses machine learning. Of course, this platform provides the users with the same facial recognition as Snapchat does but there is one more interesting use case. This application uses artificial intelligence in order to block spam messages along with offensive comments. Instagram’s users can turn this feature on or off.
Twitter is one more social network that utilizes machine learning. A team of developers at Cleveroad has built the algorithm for this application that can analyze the tweets. The users can turn on this feature pressing ‘Show me the best tweets first’ button. As a result, machine learning technology analyzes tweets and evaluate them according to different metrics.
Wishing to increase engagement, Twitter’s builders have taught the algorithm to match the tweets evaluation with users preferences. As a result, users see only relevant content.
Pinterest is based on the relevant and customized content, that’s why this company has acquired an ML company called Kosei. It’s focused on the algorithms related to recommendation and content search.
So, there are a lot of features applied to machine learning. This powerful technology allows detecting spam, personalizing content, and so on.
This application is a good example of ML integration in the financial sphere. Simply saying, Oval Money assists you in saving money. The app can combine ML with lessons that clients teach each other to create collective intelligence. After that, the application tracks your spending habits and combine them with collective knowledge. Having analyzed all the information, Oval Money offers the best ways for you to save money.
Of course, there are platforms using ML except on social networks. For example, Salesforce CRM gets a lot of profit from using machine learning technology. It provides an opportunity to analyze the potential customers basing on past sales. Moreover, you can create product recommendations based on user interaction information.
This mobile application is one of the most obvious examples of implementing machine learning. The app is so good because it knows what you want to watch even before you do. That’s what machine learning algorithms can offer.
So, Netflix provides the uses with personal recommendations. The content of the application is usually categorized on actors, genres, length, and so on. The algorithms study users’ behavior and provide them with perfect recommendations.
So, wrapping up our article, there are a lot of solutions that can assist you in the creation of machine learning algorithms. Of course, you need to wait for some time before your application will work perfectly.