What You Need to Know About Machine Learning's Impact on Back-End and UI Development

In the rapidly evolving world of web development, certain advancements are reshaping how applications are built and experienced. Among some of the top developments is the integration of machine learning into back-end and UI development. Many factors contribute to this shift, but the most significant lie in the demand for automation, personalization, and interactivity between the platform and users. 

These days when someone visits a website they’re looking for quick access to something. When you load up Google or Chat GPT, there’s a search bar waiting for you. It’s no surprise that their infrastructures are powered by machine learning, and it should serve as a benchmark for the transformative impact of machine learning on web development. With that said, let’s look at it in action:

Machine Learning in the Insurance Industry

After looking at a report from McKinsey, it’s clear that the insurance industry will be one of the sectors greatly impacted by machine learning in web development. Instead of the traditional approach of "detect and repair," machine learning enables insurers to shift towards a "predict and prevent" model. This transformation impacts various aspects of the industry, but especially back-end and UI development. 

For example, McKinsey outlined that wearable data can be directly integrated with insurance carriers, or connected-home and auto data can be made available through platforms like Amazon, Apple, and Google. What that’s going to do for back-end development is driving the demand for well-rounded data processing and storage systems that are capable of handling real-time data at scale from devices.

On the front of UI development, machine learning is going to need to focus on creating interfaces that are not only visually pleasing but also highly intuitive. For instance, the interface can use interactions from the user to learn and adapt over time which will help with features such as personalizing content recommendations, creating a dynamic user interface, predictive user flow, and that’s just scratching the surface.

“How does this benefit a company's longevity?”

Over the past 3 years, fraud rates have gone up by 70%, risk management is a top priority for companies of all sizes, and website personalization (even for anonymous visitors) is a major draw for consumers.

Machine learning remediates the issues associated with all of this in a few ways. First of all, its ability to analyze data in real-time at scale is something that’s going to detect and prevent fraud like nothing else could. This goes back to the “predict and prevent” model, fraud prevention is all about detecting patterns and anomalies which can save companies from massive attacks.

When it comes to risk management, this is where data-driven machine learning models shine. They take into account multiple data sources and provide risk assessments that are much more efficient than manual analysis and historical data.

Lastly, the personalization aspect comes to life by analyzing user behavior and preferences which the machine learning models can then use to deliver highly tailored content.

When it comes to scalability and adaptability, machine learning is one of those things that truly excels. As data volumes and business complexities grow, the need for systems that can manage and process information at 10x the speed a team of people can becomes critical. 

Best Tools Use

What good would this information be without having actionables to implement it effectively? When it comes to leveraging machine learning in web development, having the right tools is crucial. Here are some of the best ones to use:

Gradio

This is a Python library that simplifies building user interfaces for machine learning models. It streamlines UI development and offers an easy-to-use interface for model visualization.

TensorFlow.js

TensorFlow.js is a library best for developing and training ML models in JavaScript. It can be used for both back-end and front-end development and can run in the browser or on Node.js.

TensorFlow

TensorFlow is also very popular for machine learning since it provides a JavaScript library that makes models more efficient. It can help when training and building your models, and you can even run your existing models with the help of the model converter in TensorFlow.js.

Scikit-learn

Scikit-learn is a great machine-learning library that’s used for machine-learning development in Python. Its tools are simple and efficient for data mining and data analysis.

Cortex

Cortex is an open-source platform used for deploying, managing, and scaling machine learning models. It’s going to let you deploy all types of models and is built on top of Kubernetes to support large-scale machine-learning workloads.

MLRun

This is a tool for model development and deployment. It runs in a variety of environments and supports tons of different programming languages such as Python, R, Java, and Go. It can help automate the entire machine learning workflow, with everything from data preparation to model deployment.

Keras

Keras is a high-level neural network API, written in Python and able to run on top of TensorFlow, CNTK, or Theano. It’s meant to enable fast experimentation with deep neural networks and can be used for both research and production.

PyTorch

PyTorch is an open-source machine learning library used for developing and training neural network-based deep learning models. It’s actually primarily been developed by Facebook's AI research group and can be used with Python as well as C++.

Hugging Face

Hugging Face is another open-source library, it provides models for natural language processing (NLP). It can be used for tasks such as text classification, answering questions, and even language translation.

OpenCV

OpenCV is an open-source computer vision library that can be used for image and video processing. It’s got tools for object detection, face recognition, and various other computer vision tasks.

The Takeaway

Machine learning is going to be the greatest driving force behind the future of technology and innovation. We can give you the tools, but without a proper strategy, you’re a gardener in a war. We want you to be the warriors in a garden of possibilities which is why ISU Corp is offering AI consulting services. With our expertise and experience in the realm of AI and machine learning, we can work closely with your organization to craft a tailored AI strategy that aligns with your objectives and needs to excel in your industry.

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.

10 Step Guide to Problem Solving With Artificial Intelligence

One of the biggest misconceptions of AI has to be that it’s a shortcut. While it absolutely can make the execution of tasks more simple, it sets a new bar in terms of what people can achieve and doesn’t just help them avoid work altogether. But this is assuming you’re willing to put in the effort to understand and utilize AI effectively.

The extent to which one person can achieve is no longer limited to their unique capabilities. As a result, the problems they can solve scale exponentially which when starting a business can become lucrative before needing to hire staff. If we’re going to dive deeper into this, we need to think about all the facets involved in utilizing AI for problem-solving in business.

Full Leverage of Artificial Intelligence Integration

Utilizing AI for problem-solving in business typically involves two key aspects: internal operations and customer experience. A general overview of these aspects is as follows: 

Internal Operations:

• AI ensures data management is effective, guaranteeing data quality and accessibility for other AI solutions being used. If we look at financial services, AI can detect patterns in data which can improve risk management and guidance— especially for clients.

• AI automates tasks by leveraging machine learning algorithms to make predictions or decisions without explicitly being programmed to do so. For instance, think of QA; AI can identify issues early and minimize the need for manual inspection. In manufacturing, this is great for anticipating system failures.

• Supply chain optimization with AI enhances efficiency and delivery. By analyzing demand patterns and production capacities, AI can identify bottlenecks and streamline procurement.

Customer Experience:

• AI enables personalized experiences based on customer data and preferences. For example, an e-commerce platform can use algorithms to suggest products based on the customer's browsing and purchase history.

• AI chatbots provide 24/7 support and quick issue resolution.

• Analysis of customer feedback helps improve products and services. A hotel chain for instance can use AI to analyze every customer review or article written about them and find areas they need to improve. This is great for any business trying to identify specific pain points and then make data-driven decisions when looking to enhance products or services.

The future of your business, no matter what industry you're in, is going to either be very bright or a flash in the pan depending on how you adjust to the new standards of solving problems. These problems go both inside and outside of your organization.

10-Steps to Problem-Solving with AI

When you feel there is an opportunity to leverage AI to find a solution, this is typically the process you’ll want to follow:

1) Define the Problem: Clearly articulate the problem you want to solve with AI. Understand the context, challenges, and desired outcomes.

2) Collect and Prepare Data: Collect relevant data from diverse sources and ensure it is cleaned and organized for the AI to analyze.

3) Choose the Right AI Technique: Select the most suitable AI technique, whether it's machine learning or natural language processing, to address your problem.

4) Train and Test the AI Model: Train the AI model with labeled data and evaluate its performance using test datasets.

5) Interpret and Validate Results: Analyze AI-generated insights, understand limitations, and validate results with domain experts.

6) Iterate and Refine: Keep improving your AI model and problem-solving approach based on feedback and outcomes.

7) Implement and Monitor: Implement the AI solution in real scenarios and monitor its performance.

8) Address Ethical Considerations: Ensure fairness, transparency, and accountability in AI-driven decision-making. In other words, strive to avoid biases because transparency in AI algorithms and decision-making is vital to building trust with stakeholders.

9) Embrace AI for Customer Experience: Use AI to personalize customer interactions, offer 24/7 support, and assess feedback to improve offerings.

10) Integrate AI in Internal Operations: Automate tasks, optimize processes and leverage AI-driven analytics for decision-making and efficiency.

How it Looks in Action

Talk without action means nothing, especially in business. With that in mind, here is a concept of what it might look like when a company goes through this process:

Telecommunications Example

Imagine a telecommunications company that is facing a challenge with customer churn rates (the number of customers who cancel their subscriptions or switch to competitors). 

Step 1: Define the Problem

The telecommunications company identifies the need to reduce customer churn and retain existing customers. They want to develop a strategy to enhance customer satisfaction and loyalty.

Step 2: Collect and Prepare the Data

The company gathers a vast amount of customer data, including call records, service usage patterns, customer feedback, and social media interactions. The data is organized and cleaned to make sure it’s accurate.

Step 3: Choose the Right AI Technique

The company goes with machine learning algorithms to analyze customer data and identify patterns that lead to churn. Natural language processing is then used to extract insights from customer feedback and social media interactions. 

Step 4: Train and Test the AI Model

The AI model is trained using historical data on customer churn. The model is then tested with a separate dataset to evaluate its accuracy in predicting churn.

Step 5: Interpret and Validate Results

The model provides insights into customer behaviour and identifies factors contributing to churn. The company validates the results with domain experts to ensure their accuracy and relevance.

Step 6: Iterate and Refine

Based on feedback and outcomes, the telecommunications company iteratively refines the AI model and strategies to better address the issue.

Step 7: Implement and Monitor

The company implements targeted customer retention strategies based on the AI-provided insights and closely monitors their effectiveness.

Step 8: Address Ethical Considerations

The telecommunications company ensures transparency in its AI algorithms and decision-making processes to build trust with customers. Biases are identified and mitigated.

Step 9: Embrace AI for Customer Experience

Customer interactions are personalized, and the AI provides tailored offers and enhances customer support.

Step 10: Integrate AI in Internal Operations

AI-driven analytics are employed in internal operations such as optimizing network performance and maintenance, the goal is to improve efficiency and quality of service.

The Takeaway

The one thing there will never be a shortage of in business is problems. As the way we solve problems innovates, knowing how to leverage tools to optimize your internal and external operations becomes the most vital business skill. If you don’t love your product, what makes you think someone else is going to? Learn the next steps in integrating AI in your business here.

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.

Best Chat GPT Plug-ins For Software Development

We know about the prompts Chat GPT has to offer software developers, which are great starting points— but truly, they are nowhere near the full scope of leveraging streamlined workflows from AI. Since the dawn of its existence, coding has been about 3 things; creativity, problem-solving, and innovation. With artificial intelligence, the horizon for these aspects of coding expands exponentially.

Plug-ins elevate this further, especially when a programmer knows exactly which to use and when. What plug-ins do is leverage AI algorithms to cut down the development process by automating tasks and getting data-driven insights. A typical scenario for development with plug-ins might look like this:

Scenario of Leveraging Plug-ins

A programmer is working on developing a web application for a client. The project involves developing systems for user authentication and authorization, implementing complex business logic, integrating with external APIs, and load balancing. The deadline is also tight. 

To start, they use a plug-in that automates the process of setting up secure user authentication systems, including user registration, login, password encryption, and session management. Once that’s done, they go to an AI-driven tool that will give them 3 things; intelligent suggestions, code templates, and algorithms for handling not just super complex workflows, but also calculations. Still with me? We’re almost there.

As the project involves integration with external APIs, the programmer moves to an AI-based tool that simplifies the process of connecting to external services. The main purpose of this tool is 4 things: automatically generating code for authentication, making API requests, handling responses, and managing errors. By now, communication with external APIs is seamless and they move on to the last phase. 

They need load balancing for performance and scalability. For this reason, they use a tool that does 3 things: monitors server loads, distributes incoming traffic across servers, and optimizes resource allocation. With this, the workload can be distributed which ultimately lets the system handle user traffic without compromising stability. 

Throughout this entire process, the programmer is leveraging a tool that analyzes code and performance metrics. The Chat GPT plug-ins utilize these insights and data from the analysis to generate code snippets and insights. So if you thought plug-ins were limited to providing prompts, think again. With that said, here are some Chat GPT plug-ins that developers need to pay attention to:

Zapier

This is likely the most popular integration tool as it integrates over 5,000 apps with ChatGPT. You can connect apps like Google Sheets, Gmail, and Slack directly to ChatGPT. Users can leverage any of Zapier's 50,000 actions, such as search, update, and write to automate tasks. 

Code Interpreter

Although currently in a closed alpha phase, this plug-in is very promising for the future of software development. It enables the execution of Python code directly within a chat session with ChatGPT. The plugin allows users to upload and download files, make code adjustments, and even receive suggestions and modifications from ChatGPT. 

ChatWithGit

ChatWithGit is a plugin that enables users to search GitHub directly within the ChatGPT interface. It allows users to find relevant code snippets and provides a preview of the code along with a link to the corresponding repository. This feature saves developers time by leveraging existing code resources on GitHub without having to reinvent the wheel.

Visual ChatGPT Studio

Visual ChatGPT Studio is an extension from the Visual Studio Marketplace. It integrates with Visual Studio. Its features include method code autocompletion, adding unit tests, bug detection, method optimization, explanation writing, commenting, providing summaries for C# methods, and users can ask questions (code-related questions) and get answers within the editor.

Prompt Perfect

The Prompt Perfect plug-in helps users create effective prompts by optimizing and rewording them for better results. It’s also not exclusive to ChatGPT. All you do as a user is begin your prompt with “perfect” followed by your request. 

Wolfram Alpha

The Wolfram Alpha plugin addresses ChatGPT's limitations in math. It gives ChatGPT access to computation, math functionalities, curated knowledge, and real-time data through Wolfram Alpha and the Wolfram language. Using this plugin, ChatGPT can give you improved responses and visualizations for math-related queries.

Link Reader

Link Reader enables ChatGPT to extract and analyze information from various types of links, like PDFs, PPTs, Word docs, and all the fun stuff alike. Users then have ChatGPT translate, summarize, interpret, and even analyze the contents of the given link, to get insights far more in-depth than a typical search engine.

Moving Forward

Emad Mostaque's statement that "There are no developers in 5 years" is something companies in the tech space need to really think about. The amount that one person can achieve increases exponentially with AI-powered tools. And trust me, you won’t lose your job to a machine, but the person who knows how to leverage machines will blindside you.

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.