How AI Architects Can Save Your Business

Artificial Intelligence is here for one thing: to move the world forward using technology. Like the introduction of the internet and computers, it will take over jobs, but it will also create opportunities for new jobs and redefine existing ones. The idea that work needs to be labour intensive, demand immense creativity, and require various levels of collaboration is challenged at the sight of AI.

Again, as everyone knows by now, this is a tool— a series of tools— that will change the way people approach their work in ways we cannot fully predict. What can be predicted is that machines will execute tasks based on what they’ve been told to do and will continue to learn and adapt based on the data and feedback they receive. When it comes to running a business, it’s critical to find ways to incorporate this now as it truly is a matter of staying up or going way under.

Data Management

The foundation and overall value of AI tools come down to their ability to manipulate and manage data. With businesses re-evaluating their strategies and looking for opportunities to incorporate AI solutions - the most important aspect to consider is how these tools can handle their organization's information. First, let’s gain some perspective on the level of data that needs to be managed. 

The average enterprise manages about 10 Petabytes of data - for reference, 1 Petabyte is equal to 20 million large filing cabinets or even 500 billion pages of standard print text. Now, something this data-intensive won’t be managed by Chat GPT 3000 words at a time. What will happen is a robust system will be built that is not only efficient but can handle data at scale, at any level of complexity, and that can collect, process, store, analyze, and retrieve data within seconds. 

The Data Life Cycle

Data management involves several stages, including data acquisition, data integration, data quality, data governance, and data lifecycle management. What’s important to know as a business shaking in its boots trying to strategize using AI and Machine Learning is that implementing these solutions demands a coordinated approach. 

What that means is that stakeholders need to work together to establish clear goals, clear objectives, and be open to new ways of working with AI and Machine Learning solutions (some businesses still resist change even when seeking advice).

Key stakeholders vary but generally, when it comes to enterprises, the ones who need to collaborate cross-functionally include IT, data science, operations managers, and legal.

This is all great to know but it doesn't do much in terms of specificity, so with that said, here are a few additional considerations for businesses looking to implement AI and Machine Learning:

Skill Set: Implementing AI and Machine Learning solutions requires skill sets that may not be available in-house. Businesses in this case will need to invest in training or hiring data scientists, AI engineers, or various other professionals. Outsourcing is a great option in this case as it provides access to the required expertise that can not only build the solution but identify gaps to fill with it and then maintain the solution over time. 

Data Preparation: Before implementing AI and Machine Learning solutions, businesses need to prepare their data for analysis. This will involve cleaning and processing data, as well as transforming data into a format that can easily be read by AI tools. Labeling all data when training Machine Learning models is very useful in this case.

As you can see, it’s not an easy “one-and-done” task and it can actually be very time-consuming. Data preparation is one of the most complex but also most important parts of AI and ML model integration.

Iterative Process: Implementing AI and Machine Learning solutions, at this moment in time, is an iterative process, meaning it requires ongoing testing, validation, and refinement. Businesses must be prepared to adapt and adjust their strategies based on the insights generated by these AI tools (going back to the point about embracing change).

When AI and Machine Learning Meet Your Business

The AI tools that we are currently aware of and using such as Chat GPT, Jasper AI, MidJourney, and anything of the sort are mere droplets compared to the ocean of capabilities that Artificial Intelligence and Machine Learning technology will have to offer. 

No matter what industry you’re in, you’d be surprised at the amount of untapped potential in your business that a team of AI consultants can uncover to not only save time and money but also improve the overall efficiency of your business and the effectiveness of your workflows.

This is a lot to take in and, frankly, it’s a little overwhelming. But the truth is, AI and Machine Learning are not going anywhere. They are here to stay and will continue to shape the way we work, live, and interact with the world around us. With that said, here are 3 digestible takeaways from this for you to consider when re-evaluating how your business can strategize using AI and Machine Learning:

Data management is key: The effectiveness of AI and Machine Learning tools is only as good as the data they are given. By investing in efficient and effective data management systems, businesses will be able to leverage the full potential of these technologies.

Embrace change: It’s essential to adapt and adjust strategies based on the insights generated by AI and ML tools. Businesses must be open to change and be willing to work collaboratively across departments to make this happen.

Invest in expertise: Implementing AI and Machine Learning solutions requires expertise. Businesses need to be prepared to invest in training or hiring experts with expertise in AI integration or consider outsourcing to access it instantly.

The Takeaway

Avoiding the inevitable hammer coming down on companies who fail to integrate AI starts with assessing vulnerabilities. Of course, businesses never want to admit their flaws or focus on weaknesses because that would take away from the feeling that everything is going well. However, if that’s the mentality companies have throughout the next 5 years, their window for enjoyment will be very small. Now is the time to step into the future.

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 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

—

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.

The Benefits of Process Automation Using Software Development Services

Among the many draws businesses have towards custom software development, workflow automation is among the most recurring reasons. So much so that research is finding around 50% of businesses have plans to implement this kind of automation. Workflow automation? What is that? 

To put it in perspective, nearly 85% of small businesses alone depend on some form of manual task every day. Of that 85%, most likely have ambitions to expand the business, which in order to do so requires being able to focus their energy on more complex tasks. 

Using Automation

Here is an example of process automation:

Think of an e-commerce startup that has to manually input and track inventory or transactions. The owner would spend hours each day trying to keep up and it would become impossible to do manually, especially if the business began to scale up. 

So instead of spending hundreds of thousands of dollars on yearly salaries for people to manage data, the owner hires a team to implement a custom software system that handles these tasks automatically.

This is a very simple example of process automation. The concept is broad and can be anything from automating repetitive tasks to generating and distributing reports, transferring files, or even integrating business applications. 

Process automation is an investment that saves businesses time and money by reallocating their time toward more important tasks. More than that, it gives businesses their most important asset: the ability to scale. We’re going to need more detail to support that claim so with that said, let’s go over the top 6 benefits businesses can expect to see with process automation. 

1. Processing Speed:

   Robotic Process Automation (RPA) is the most common user-friendly technology used to automate repetitive manual processes. RPA uses software robots that can produce several times more output in a shorter period than a person could. This not only opens up the capacity of data that can be handled but also condenses the time it takes to do so.

2. Analytics:

   When using RPA, the system is constantly keeping track of what it’s doing and when it’s doing it which is then composed into analysis reports. What’s great about this is that if an error does occur, you’ll be able to go back and see where. When a company first implements this kind of software, it will be really helpful for identifying the speed bumps in any of the organization's current processes. 

3. 24/7 Work:

   Employees only work an average of 8 hours a day, whereas software can always be on the clock. Stack that up next to a 40-hour work week and that’s 80 extra hours of work businesses are getting Monday to Friday without having to give up any benefits.

4. Compliance:

   Certain industries (Insurance, Banking, Healthcare, etc.) have regulation standards that must be met. Automation software like that of RPAs can assist companies in meeting these standards. It adds that extra layer of control and oversight in business processes.

   The software will be built to perform these tasks to the exact specifications outlined and will continuously do so as the company needs it to. As a result, because the process is very consistent, there is not a lot of risk for error and all records are kept in the event an irregularity does occur. 

5. Reducing Errors:

   In organizations, mistakes made by employees add up and can cost millions of dollars (an average of over $60 million annually). Touching again on the compliance piece, automation software is able to reduce this risk of error dramatically by strictly adhering to the process standards.

6. Employee and Customer Satisfaction:

   By switching from a labour-intensive landscape to a process automation system (for repetitive tasks), employees can direct their time and energy toward the other tasks we’ve consistently referred to as “complex”. This just means that the company's resources are being used to address needs that provide value.

   Also, notice the order of “employee” before “customer”— this is because to deliver value to your audience, the ones delivering the value need to be motivated to do so. 

Reflecting

Every industry is different, especially in terms of its long-term goals and short-term needs. This means that when it comes to technology, the resources being utilized need to have a solid strategy behind them. Custom software development is only effective when this strategy is in place and the stakeholders in the organization are aligned with its purpose. 

Businesses who are looking to go the extra mile need to recognize the importance of what they will do with the time and resources they’re freeing up when using an IT service like process automation. Without that strategy, there’s not a lot of value in implementing this technology and that will be an expensive mistake down the road. 

In order to strategize efficiently, talk to professionals to help your business get on the right track.

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.