10 Machine Learning Tools For API Integration

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.

5 AI Deep Learning Tools Companies Need to Watch

Deep Learning is a concept that’s still in its early stages of development, though it has the potential to create significant change when it comes to how we understand and use Artificial Intelligence. Contributing to the growing umbrella of AI and Machine Learning technology, Deep Learning is yet another tool that’s here to process information, make decisions for us, and enhance the extent to which service providers can deliver solutions.

What is Deep Learning?

The intent behind creating Deep Learning was to have technology that can process information and make decisions just like that of a human brain. What the technology does, in this case, is use algorithms and neural networks for pattern recognition that resemble the way a person would process information and make decisions.

Deep Learning, also referred to as Deep Neural Networks can be used across a variety of industries for a variety of purposes. At the moment, its most notorious usage methods include self-driving, predictive analytics, and speech recognition. Do you think it’s going to stop there? We guarantee you it isn’t…

Deep Learning offers a closer glimpse at the capabilities of artificial intelligence that are quickly surfacing, and it's expected to have a significant impact on various fields, including healthcare, finance, and cybersecurity. With their ability to process and learn from large amounts of data, Deep Learning algorithms will revolutionize how businesses approach complex problems and make decisions to solve them.

How is it Useful to Businesses?

The most important thing right now for companies in industries such as Finance, Manufacturing, Healthcare, Insurance, E-Commerce, Advertising, and industries alike is to evaluate how they will adapt their service delivery model to the increasing demand for integrating AI technology. 

Deep Learning can be a game changer for these industries, as it will provide them with the ability to process and analyze vast amounts of data, as well as gain insights from that data that were previously impossible for companies to obtain. 

3 Examples of This:

• A pharmaceutical manufacturing company could use Deep Learning to analyze findings from clinical trials and predict how effective a new medication will be. 

• A financial firm could use algorithms from Deep Learning to analyze trends in the stock market and provide that information to investors.

• For a manufacturing company using complex machinery, Deep Learning can predict when maintenance will need to happen, identify failures before they occur, and even optimize the manufacturing process itself.

When you see examples like this, the value of process automation seems obvious though not many fully understand what it entails. Some businesses aren’t ready for AI technology simply because they don’t understand its full capabilities and how those capabilities will benefit their business.

How Deep Learning Keeps Companies Competitive

In coming years, the ability to organize, analyze, and process data will be the most important and sought-after function for every business's daily operations, workflows, and especially service delivery. Deep Learning and AI algorithms alike are what companies are increasingly turning to when supporting their back-end, which includes tasks such as data management and predictive analytics. 

To lay it out more clearly, here are 5 specific Deep Learning tools to pay attention to:

1. TensorFlow: A deep learning tool developed by Google, written in C++ and CUDA. This tool supports various programming languages such as Python, Java, and Go. It simplifies the creation of machine learning models for mobile, web, desktop, and cloud, and supports large-scale neural networks with multiple layers.

2. DeepLearningKit: This is an open-source Deep Learning tool developed in Swift for Apple devices with a graphics processing unit (GPU). It supports pre-trained models and low-latency Deep Learning calculations. It also supports Convolutional Neural Networks and aims to support other deep learning tools such as Torch or TensorFlow.

3. Keras: Keras is a high-level neural network API written in Python that runs on top of TensorFlow or Theano. It supports recurrent networks and convolutional networks, as well as multi-input and multi-output training. It offers simpler and faster prototyping with its modularity, minimalism, and easy extensibility.

4. Caffe: An open-source Deep Learning tool developed by Berkeley AI Research and community contributors. Its advanced architecture allows for innovation and application propagation. According to research, Caffe is faster than TensorFlow.

5. H2O.ai: This is an open-source Deep Learning tool written in Java that supports various machine learning algorithms. It seamlessly integrates with Apache Hadoop and Spark for big data analysis and has an easy-to-use Web UI.

Remain Competitive Using AI

Although not every company that will undergo digital transformation in the next few years will necessarily integrate Deep Learning tools right away, it’s still important to know about its presence. Deep Learning can be a separating factor for businesses that want to maintain a competitive edge in their industry amid the widespread adoption of AI.

Deep Learning tools like the ones listed above are great options for workflow optimization and data management, which, as we said, cannot be understated when planning for the future of your business.

What’s Next For AI?

Save this infographic for a quick reference.

The future of AI looks bright with continuous advancements in Deep Learning and other AI technologies. As more industries and businesses continue to adopt AI, programmers will play a crucial role in developing and improving AI algorithms and tools. However, the best thing for any business to do right now is to start strategizing for how they will use these tools to support longevity.

The way to do this is by bringing in AI experts to help identify areas within workflows that are repetitive and that could be automated to promote efficiency and save time.

Written By Ben Brown

—

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.

How Artificial Intelligence is Changing Software Development

AI is not a new concept anymore. Public understanding of its capabilities is broadening rapidly and changing how we view professional services. What AI has given us at this moment in time is not a threat to jobs, but rather an opportunity to get behind the remodelling of employment which is something that will become a threat to service providers who don’t.

Software development is one of the key industries that will face significant change from AI, but this is an excellent thing for companies who view it as an opportunity and pivot their strategy accordingly. Chat GPT can write code in seconds. Anyone who’s used it knows it can pretty much do anything you tell it to, which is why its rise has been so meteoric. 

Chat GPT from Open AI is just one example - 39 other companies are just as close in competition with Open AI, never mind all the startups and concepts that have yet to hit the market. With that in mind, it’s safe to say that, unlike other trends that eventually blow over, AI and Machine Learning are here to stay.

Software Development Using AI

The entire purpose of AI is to make processes/workflows better and enhance the results that can be delivered. For example; look at a company like Microsoft which not only invested $10 billion in Chat GPT  but has also taken its AI product development to the next level. What Microsoft did was create various platforms that assist in the programming process in ways that allow software developers to do their job more effectively.

Visual Studio IntelliCode and CodeSpace are both AI services built by Microsoft meant to correct errors made in code for developers. If you ask a software developer today who’s been coding for the last 20-30 years what a difference tools like Chat GPT, Visual Studio IntelliCode, and CodeSpace would’ve made in their past projects, they’d tell you it would’ve saved them countless hours of typing, debugging, and testing. 

That’s what this is all about— adapting to these new tools to enhance what we’ve been doing all this time but to do it better, faster, and more efficiently.

Shift in The Role of Software Developers

The tasks that Artificial Intelligence and Machine Learning handle are typically time-consuming, repetitive, and cover less comprehensive duties. Software development involves a significant amount of these tasks such as extending source code, going through iterations, and debugging. 

In the future, if AI is fully handling these responsibilities without the need for constant supervision - software developers would be able to take on more of a managerial role and handle tasks that are more complex or require hands-on creativity. Tasks that would classify as “complex” or “creative” in software would be as follows:

• Developing new algorithms or even programming languages

• UI/UX design

• Graphic design for software products

• Developing new software features that enhance the user experience

• Developing and implementing new software testing methodologies

• Solving complex software problems

• Creating complex software architectures

• Building new frameworks for development

• Conducting in-depth research and analysis related to software development and emerging technologies

• Collaborating with marketing and sales teams to effectively market and sell software products

• Collaborating with customer support teams to ensure that software products meet the needs of end-users

• Mentoring and coaching junior software developers

• Overall leading software development teams and projects

There is a myriad of things that developers will still be crucial for. As a matter of fact, there’s much more opportunity for a software development company who learns how to manage and integrate AI products than there’s ever been for any new technology in the industry before.

What AI Products Will/Do Clients Want?

The types of AI products that a software service provider would create are highly subjective based on the clients they serve. For example, it doesn’t take an expert to tell you that a hospital is going to require a much more sophisticated and elaborate architecture than an E-Commerce store. 

Nonetheless, there are common interests among companies in various industries when it comes to AI product development that includes the following:

Predictive Analytics: Machine learning and AI are great for analyzing large quantities of data which would allow them to make predictions that are more accurate toward outcomes. For example, a financial services provider could use predictive analytics to recognize patterns in customer behaviour and offer customized opportunities or products based on that data.

Virtual Assistants or Chatbots: Of course, with the popularity of Chat GPT - companies are going to want to integrate chatbots that improve the user experience. Some specific functions of these tools could include processing inquiries, and assisting site direction, or they could be complex like the ability to triage patient inquiries.

Deep Learning Applications: Using artificial neural networks, Deep Learning applications can replicate the function of a human brain. This is the technology behind driverless cars, voice recognition, structuring investment concepts, language processors, fraud detection, and much more. Now, Deep Learning is still a long way from its true potential which makes it a key area to watch for any service provider interested in artificial intelligence integration.

Video and Image Analysis: Video and image analysis is a game-changer for security companies as it can detect people, objects, emotions, and behaviours using AI technology. This not only assists with investigations but also has implications for industries such as advertising and entertainment, which are both finding new ways to leverage this technology to drive growth.

For example, advertisers can analyze consumer preferences as well as what’s trending - then design campaigns based on that information. It can enhance the visual design process by finding what design trends consumers are responding the best to and then incorporating them into marketing materials. 

The Next Steps For AI 

There’s a lot to unpack from everything we just covered, yet this is still just the tip of the iceberg. AI is becoming beyond comprehensible but, again— this is a time with immense opportunity for those who take the time to learn, adapt, and integrate.

Software development has long been a field that demands constant learning and adaptation which is why the best software providers will ensure their business responds remarkably to the demand. 

Written By Ben Brown

—

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.