How Your Business Benefits From Cloud Computing and AI/ML Synergy

Cloud computing has become central to scaling your business in 2023. What we’re uncovering is that AI and ML capabilities in the cloud make businesses more efficient, strategic, and insight-driven. Of course, while leveraging new technologies is highly subjective in terms of use cases, understanding when an opportunity arises to get the most out of your workflows from the least amount of effort will directly correlate to longevity. 

Cloud computing, Artificial Intelligence, and Machine Learning are tools that were created for this very reason which makes the synergistic integration of the three quite lucrative for organizations. 

Now when it comes to placing AI in a cloud environment, it’s there to enhance the operation, not replace it. Saas companies are incorporating AI and ML into their bigger software packages which is an effort focused on end-user functionality and the UX overall. 

Two Industry Examples

1) Insurance:

Practices in the insurance industry are limitless when it comes to integrated extensions from AI and ML in the cloud. Traditionally as an insurance broker, you have to manually assess customer information, research policies, and make recommendations. With process automation enabled by AI and ML in the cloud, you expedite that entire process. Here are some examples:

Claims Processing and Fraud Detection:

• Cloud-based AI and ML tools can analyze claims data and assess validity.

• ML algorithms can identify patterns of fraudulent claims by analyzing past data, trends in customer behaviour, and other various external factors, which ultimately improves fraud detection and prevention.

Risk Assessment and Underwriting:

• AI-powered algorithms in the cloud can analyze customer data, market trends, and historical claims to accurately assess risk.

• ML models can generate underwriting recommendations, ensuring that policies align with customer needs and risk profiles.

2) Manufacturing:

In the manufacturing industry, the integration of cloud computing, AI, and ML changes the entire production process as well as overall quality control. Oversight and automation are enhanced through the following:

Production Process Optimization:

• Cloud-based AI and ML platforms enable real-time monitoring of production lines, capturing data from sensors and IoT devices.

• AI algorithms can analyze this data to identify bottlenecks and areas for process improvement.

• ML models integrated with the cloud can predict equipment failures and in turn, optimize maintenance schedules and minimize downtime.

Quality Control and Detecting Defects:

• Cloud computing allows for the storage and processing of vast amounts of quality control data, be it images, sensor readings, or even product specifications.

• AI algorithms in the cloud can automatically analyze this data and recognize defects, deviations from standards, and of course anomalies.

• ML models can continuously learn from historical data to improve defect detection accuracy and ultimately enable proactive quality control measures.

Supply Chain Management:

• Cloud-based AI and ML solutions provide visibility into the supply chain, integrating data from various sources such as suppliers, logistics partners, and inventory systems.

• AI algorithms can optimize inventory levels and demand forecasting, which aids in logistics planning to minimize stockouts, reduce costs, and improve delivery times.

• ML models integrated with the cloud can identify patterns in demand, supplier performance, and market trends, which allows for more accurate procurement decisions.

Safety and Predictive Maintenance:

• Cloud-based AI systems can analyze data from IoT sensors and machinery to monitor and assess safety conditions. 

• AI algorithms can identify potential safety hazards and issue alerts to prevent accidents.

• ML models in the cloud can predict equipment failures based on historical data and sensor readings, which makes maintenance proactive and minimizes downtime in addition to optimizing asset performance.

Technical Logistics

From a technical standpoint, integrating AI and ML in cloud computing involves leveraging various specific tools, languages, and frameworks that can be quite complex. To give you a sense of what that’d look like, here is a rough breakdown of the technical aspects:

• Cloud Computing Infrastructure:

For cloud computing, you’ve got your pick at service providers which include Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP). These platforms provide services such as virtual machines (EC2 instances), storage (S3, Azure Blob Storage), and of course, data processing (AWS Lambda, Azure Functions) which leads to the next point.

• Data Storage and Processing:

To handle large volumes of data, IT teams can utilize cloud-based storage solutions like AWS S3, Azure Blob Storage, or Google Cloud Storage. For data processing, distributed processing frameworks such as Apache Hadoop and Apache Spark are a couple of options. 

• AI and ML Libraries/Frameworks:

Python is the most widely used language in AI and ML, because of its extensive libraries and frameworks. Some great libraries for AI and ML in this case include:

• TensorFlow: An open-source framework developed by Google for building ML models, particularly neural networks. It provides APIs for high-level model development and deployment.

• PyTorch: Another popular open-source ML library with dynamic computational graphs, making it well-suited for research purposes.

• Scikit-learn: A versatile library that provides a range of algorithms and tools for data preprocessing, feature selection, and model evaluation.

• Keras: A user-friendly deep learning library that runs on top of TensorFlow, simplifying the development in addition to training deep neural networks.

Developing and Deploying The AI Model

The team can use TensorFlow or PyTorch to build the actual AI model. The reason we isolated those two is that they offer APIs for creating, training, and most importantly evaluating the model. They can then be deployed using cloud-based services like SageMaker, Azure Machine Learning, or Google Cloud AI Platform, which all have managed environments for training and deploying ML models at scale.

Integrating The Model

To ensure effective integration and interoperability between cloud services and your AI/ML models, API frameworks like REST (Representational State Transfer) or GraphQL can be some good options. These frameworks are what allow for communication and data exchange between different components of the system.

Moving Forward

Once you’ve integrated everything, use tools like CloudWatch or Azure Monitor to gain some insight as to how your system is utilizing its resources and always be ready to make adjustments.

The Takeaway

Innovation on top of what many already consider cutting-edge technology is a recipe for success. Cloud computing, artificial intelligence, and machine learning are all powerful tools that, when integrated synergistically, can revolutionize businesses across various industries. The key takeaway from this discussion is that the combination of the three offers tremendous potential when it comes to driving strategic decision-making and operating as efficiently as possible. 

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.

5 of The Best Debugging Techniques

Your application, platform, or website relies on error-free code to ensure optimal performance and to provide an overall higher-quality end-user experience. If you neglect this, bugs will wreak havoc on your product and turn users off from it altogether. Though quality assurance testing is common practice during software development, developers still play a vital role in the debugging process. 

Debugging techniques vary by language and tool, yet there are many straightforward methods for developers when approaching the debugging process that can be applied to almost any software. In this article, we'll explore some of the most practical debugging techniques that programmers can use to enhance their software simply and effectively. In addition to this, we’ll look at 5 specific debuggers and what exactly they’re good for. 

How Can I Debug Easily?

When a programmer begins debugging, they are simply identifying and removing any defective code within the program. Although finding bugs is a crucial element of QA testing, it’s still just one part of a broader phase that entails verifying other aspects of quality, including usability and overall performance.

The reality of debugging “easily” is to think about the problem less from a technical aspect and more from a systematic approach. With that being said, here are 5 ways to get started:

1) Break The Issue Down Into Simple Questions:

The first step to debugging effectively is to define the issue at hand and ask yourself the simple yet important questions related to it. For example:

• What is the desired outcome of your program?

• How is your program currently functioning?

• What errors are you encountering?

• What past experiences have you had with similar problems? And what solutions from then may apply now?

• How many possible causes and locations of the bugs can you list?

Questions of this nature will lead the developer to form a theory about where to begin debugging and how to approach it logically.

2) How Do I Deal With Error Messages?

Though there is no feeling more gut-wrenching for a software developer than seeing an error message appear for the 3rd time, it’s the only way for them to realize what’s going wrong. So with that said, anytime an error message does appear, make sure you write down what’s wrong. By the same token, you can give it to Chat GPT, Visual Studio IntelliCode, or CodeSpace to get suggestions on how to solve the issue.

The fact remains that developers cannot ignore error messages or just hand them over to an AI to solve them. Getting suggestions from resources is a great first step and can speed up the debugging process - but attention to detail during software development is essential. 

3) Keep a Log

Branching off the previous point regarding writing down error messages, the key benefit of this method is that it’s great at identifying patterns/recurring issues. For example, take a developer who is integrating a REST API for a weather app but keeps getting the error message "404 Not Found" when trying to access the API endpoint to retrieve current weather data. 

The cause of this issue could be an incorrect endpoint URL or the API is simply unavailable or down. Either way, documenting the error and its details will make it easier for the developer to identify and address the problem. For instance, verifying the endpoint URL or seeking support from the API provider could be the best options when trying to resolve this issue. By logging that, the developer will be prepared should the issue happen again.

4) What About Debuggers?

A debugger can aid in pinpointing and fixing bugs in your code. These tools are also referred to as “debugging mode" or “debugging tool”. To take full advantage of a debugger, developers need to execute programming within it. This allows software developers to monitor it in real time and observe errors as they occur. By pausing the program while it's running, developers can identify and investigate the issues that are causing errors and then analyze the code line by line.

Some good debuggers include:

Visual Studio Debugger: This is a powerful and comprehensive debugger that is included with the Visual Studio IDE (Integrated Development Environment). It supports a variety of programming languages, including C++, C#, and Python, and even includes features such as step-by-step debugging, breakpoints, and data visualization.

GDB: This is a command-line debugger that is widely used in the Linux and Unix world. It supports a wide range of programming languages, including C, C++, and Fortran, and provides features such as breakpoints, watchpoints, as well as remote debugging.

Xcode Debugger: This is the debugger that is included with Apple's Xcode IDE. It supports programming languages such as Objective-C and Swift and also provides features such as source-level debugging, stack traces, and breakpoints (just as the last two do as well).

Eclipse Debugger: This is a debugger that is included with the Eclipse IDE. It supports multiple programming languages, including Java, C++, and Python, and offers features such as conditional breakpoints, expression evaluation, and remote debugging.

PyCharm Debugger: This is a debugger that is included with the PyCharm IDE for Python. It provides features such as step-by-step debugging, variable inspection, and remote debugging.

5) How Can a Developer Approach Debugging Logically?

It’s not unheard of for software developers to run into “analysis paralysis” when encountering issues with their code. For this reason, localizing the problem (line-by-line removal of code until the issue is found) is a great tool. Though it tends to be a tedious process, it can be the most efficient and effective way to solve the problem in some cases. 

Software testing, detecting, and addressing bugs before they disrupt the application is the foundation for a “logical” approach to debugging. While the QA team is responsible for thoroughly checking the product, developers should always perform simple tests during the development phase, such as unit testing, which may involve testing individual parts of the code, or units. 

Whatever the case may be, having a toolkit of simple debugging techniques is essential for creating and refining software, which ultimately leads to better products. Now, depending on the specific issues and situations encountered, some techniques may just be more suitable than others. However, top professionals would recommend that developers familiarize themselves with all of them to have a variety of tools available.

Summarizing Everything

Debugging is an essential part of building a sustainable software architecture. There are a variety of tools available to software developers today that can make this process much more efficient and effective. Software developers without debugging techniques are like a mechanic without a wrench. No matter how experienced or well-trained a software developer is, they will always run into errors and issues in their code which will lead to an unreliable and unsustainable software system. In a rapidly evolving digital landscape, debugging is paramount to ensuring that software systems are functioning as intended, and to prevent any issues that should arise from becoming bigger problems.

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.

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.