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.