SPD Group compares Microsoft, Amazon, and Google Cloud APIs to help determine which machine learning API is best for your project.
Machine Learning (ML) and Artificial Intelligence (AI) projects are complex and multifaceted. If you’re building in these environments, APIs are a tool you simply can’t do without. A powerful way to automate various functions, APIs simultaneously save the budget, decrease time and effort, and give quick access to the desired result and new data streams.
Consuming an API simplifies the process of developing AI and ML projects. But which machine learning API is right for your particular project? In this article, we figure out how ML services work and compare the three top ML APIs on the market.
What Is an API Used For?
An API acts as an intermediary between your application and a third-party service. Such services eliminate the need for developers to create similar technologies. For example, it is common for weather APIs to populate weather widgets for leading companies. Using Facebook’s API, other applications can use a standard authorization process. Or, Google Maps API can provide map search abilities to external applications.
APIs decrease the budget and development time and give users a more impressive experience. Above, we named simple API examples, but the possibilities for using APIs in development projects are much wider — there are many machine learning and artificial intelligence possibilities as well. Therefore, let’s address these topics further.
What Is a Machine Learning Tool?
Machine learning tools are smart cognitive services that provide ready-made opportunities for implementation into a project. Such tools allow you to solve problems more quickly and efficiently. They can be divided into two classifications: platforms and libraries.
- Platforms allow you to implement a machine learning project from beginning to end. Microsoft Azure is one of the most popular ML platforms. It provides visualization tools that guide you through the process of creating a machine learning model without the need to learn complex algorithms and technologies. Once the models are ready, Microsoft Azure makes it easy to get predictions for the application using simple APIs, without the need for custom code.
- Libraries, in turn, are more highly specialized tools. As a rule, they are tied to the ability to solve a specific problem in a certain environment and require additional coding skills to make their use effective. Tensor Flow is an example of one of the most popular libraries. Tensor Flow libraries significantly simplify the integration of self-learning elements and functions of artificial intelligence into applications designed for speech recognition, organization of computer vision, or natural language processing.
ML software is distributed in two ways. An ML tool is local, meaning you download local tools to your computer and get access to the necessary functions. Or, they are accessed remotely. As for the remote tools, here, the interaction occurs through the remote server of a third-party company. These cases are what we mean when we talk about Machine Learning as a Service. The most obvious examples are Microsoft Azure Machine Learning and Google Prediction API.
What Is an ML API?
Thus, an API for machine learning can be defined as a remote tool utilizing ML to solve a specific problem within a specific project. For example, using the Google Prediction API, it’s enough for the user to provide the necessary amount of input data, and then the system itself will offer pattern-matching capabilities.
Where Is the API in Big Data?
Explosive growth in the number of open APIs has led to the emergence of an API economy. And since many APIs are public, this at the same time contributes to an even greater increase in the amount of data generated — and the possibilities for analytics, respectively.
For example, a car-sharing service could use a geolocation API, and in doing so, collect and analyze information about trips and popular routes in different places and at different times. Based on this information, it then becomes possible to develop new products and services, make them more advanced and launch them on the market at a lower cost and in a shorter time, and even enhance such data through the use of an artificial intelligence layer.
How Can APIs Be Used in ML and AI Projects?
Creating applications based on artificial intelligence technology is a time-consuming and complex process, but cloud vendors simplify it by offering paid and affordable APIs. The addition of which to an application allows, for example, to recognize speech or faces in real-time. Such APIs are developed and offered by the largest cloud vendors like Amazon Web Services (AWS), IBM, Google, Microsoft, and Salesforce, as well as small specialized vendors Clarifai and Indico. The booming market offers more and more developed with the use of artificial intelligence technology APIs that have advanced functionality.
Thus, in practice, developers are able to create applications with functions for recognizing faces and languages, predicting consumer behavior, risks and moods, and much more. According to a Statista study, the most popular applications for ML and AI APIs are language processing, speech recognition, vision, data discovery, and conversational tools.
These AI examples affect our everyday lives quite often. We often turn to Google Translate, which is based on machine learning translations and every day, or we communicate with voice assistants like Siri, which is able to recognize our speech, support a conversation and discover data at the same time.
3 Popular Machine Learning APIs
We have already referenced some well-known examples that utilize ML APIs. Now, let’s take a closer look at what the world’s tech giants can offer us.
The main advantage of this solution is that Google has an incredible amount of data at its disposal. This allowed Google developers to pre-train advanced algorithms and provide APIs that have skills for performing fine-grained tasks, such as speech recognition or machine translation.
In short, Azure Machine Learning is a cloud solution that allows the construction and use of sophisticated machine learning models in a simple and visual form, an ecosystem designed to spread and monetize off-the-shelf algorithms. Azure Machine Learning is a new, highly productive tool for working with machine learning algorithms. Perhaps this is even the only environment that makes it so easy to publish your algorithms as a separate service and subsequently use them in your applications.
Amazon Machine Learning is a really handy tool. All stages are intuitive, and most importantly, everything is perfectly documented in the basic manual, so it is possible to work with this technology even without deep theoretical knowledge and extensive practical experience. It is good for learning the principles of machine learning – this is facilitated by a “soft” pricing policy and a detailed description of each stage in the manual and in the program itself, where you can experiment with settings.
Definitely, the choice of API for machine learning project will depend on your project specifics and industry. Although, for example, a predictive analytics API could be equally suitable within both retail and banking projects. Each of the leading tools have its own strengths: Amazon has very affordable prices and the ability to test many functions for free, Azure invites beginners to join the process of creating ML models, and the obvious advantage of Google in the amount of data with which the models were pre-trained.