API integration sets up web services to process data in ways that benefit the UI/UX of an application or platform. It does so by allowing developers to leverage external APIs and Machine Learning (ML) techniques to enhance the functionality and usability of the software system. If/when they are leveraged, the software system will respond extremely well.

When examining the role that Machine Learning plays in API development - the key area to focus on is analytics and how applications use this to speed up product development. Machine Learning consists of algorithms and models that, when leveraged during API development, create a high-functioning data management and automation system. Now, the key variable to keep in mind here is the quality and quantity of data that the system has access to.

Processing Data

API development and integration are common practices when building a platform's back end, however, Machine Learning is still on the rise in terms of adoption. With that in mind, here are some of the benefits you can expect when combining ML algorithms with APIs:

• API provides a clean and well-defined interface for analytics

• API integrates easily with any application with a simple cURL command

• API endpoint remains the same even if the algorithm or input data changes

• API checks data and requests at the door for anything not corresponding to the specification, resulting in an error

• API separates the iterative world of data science from the world of IT and software

• Algorithms need frequent updates, software needs to be stable, reliable, and robust

• Data scientists can focus on building models without worrying about infrastructure

Many businesses run into problems when managing their data which include anything from quality issues with the data itself to errors when processing large quantities of data. This is where API and ML integration can be the saving grace - when automating data management processes, it results in the following:

• Cleaner and more structured data 

• The ability to predict and detect anomalies

• Provides personalized recommendations

• Automates repetitive tasks

• Manages large volumes of data with ease

Tools That Integrate Machine Learning Techniques 

We’ve looked at a few of the tools on this list but seldom have we taken a closer look at how they assist Machine Learning integration. Here are 10 tools that are great for this process:

1. H2O.ai: A Machine Learning platform that enables programmers to build and use large-scale Machine Learning models.

2. RapidMiner: A platform that allows developers to build predictive models and perform data analysis using Machine Learning algorithms.

3. Google Cloud AI Platform: A cloud-based platform that provides developers with tools to build, train, and deploy Machine Learning models.

4. TensorFlow: A Google-developed open-source platform that enables Machine Learning model creation and training.

5. Scikit-learn: A Python package that offers various data mining and analysis features, including Machine Learning techniques.

6. Keras: An advanced neural network API created in Python that can be used alongside TensorFlow.

7. PyTorch: A neural network development and training library that is open-source and based on the Torch library.

8. Microsoft Azure Machine Learning Studio: A cloud-based platform that allows developers to build, train, and deploy Machine Learning models.

9. Amazon SageMaker: A fully-managed platform that provides developers with the tools to build, train, and deploy Machine Learning models.

10. IBM Watson Studio: A cloud-based platform that also allows developers to build, train, and deploy Machine Learning models using various tools and services.

The main priority in mind when software developers integrate Machine Learning techniques with APIs is the flexibility and ease of integration on the back end. What this means is that the platform will be highly scalable, able to handle various data formats, and easily integrate with different programming languages as well as existing software frameworks. 

Developers often experience the temptation to expose an endpoint of the API to the code of the ML model but this does incur risk. When you maintain the separation of your model's code from that of the infrastructure, it allows the application to function in a more secure, reliable, and scalable manner. Whereas exposing endpoints can lead to error responses to requests and downtime of the endpoint. 

Going Forward With MLaaS (Machine Learning as a Service)

APIs provide programmers with a clear interface that organizes analytics and the application utilizing them which in turn speeds up the product development process and allows ML models to be used and reused across a variety of applications. With this, companies can expect to see a lot of change in their industry as the gold standard today calls for a strong digital strategy. In order to get ahead, businesses need to remain aware and begin strategizing before taking action.

Written By Ben Brown

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ISU Corp is an award-winning software development company, with over 17 years of experience in multiple industries, providing cost-effective custom software development, technology management, and IT outsourcing.

Our unique owners’ mindset reduces development costs and fast-tracks timelines. We help craft the specifications of your project based on your company's needs, to produce the best ROI. Find out why startups, all the way to Fortune 500 companies like General Electric, Heinz, and many others have trusted us with their projects. Contact us here.