Integrating AI/ML Software with Your Existing Tech Stack: Best Practices

You're probably already well aware of how the AI and ML tech stack can benefit your business, and you're ready to move from words to deeds. Well, we don't want to waste your time, so let's move on from talking about it and get into the specifics of integrating artificial intelligence and machine learning into your business workflow and what it can do for you.

You’re integrating AI with your technology stack: How can you get it done?

It probably won't surprise you to hear that the artificial intelligence market is booming and shows no signs of slowing down. It is predicted to be worth a whopping $407 billion by 2027 alone. 

There’s one more statistic to consider:

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However, difficulties can arise where you least expect them - in the process of integrating AI/ML software into your existing technology stack. In fact, implementing AI and ML requires a strategic approach. It will ensure consistency and clarity of action so that you can count on clear results:

• Thanks to AI algorithms, you'll be able to analyse customer behaviour and offer them a personalised shopping experience;
• Machine learning and AI models can predict and prevent equipment failures in production, thereby reducing downtime;
• You can gain a competitive advantage by marrying website development with AI to deliver an engaging user experience and much more.

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Integrating AI/ML software with your existing technology stack can be a real game changer. Here are some best practices that will help your organisation navigate the integration process smoothly and achieve the desired results.

Hire talented and, most importantly, experienced developers

It may sound trite because it is an obvious fact, and yet many companies ignore this step. Meanwhile, it is fundamental: You need experienced software developers on your side who know how much time and money developing AI software can take, and what other skills are needed in the product development process. Still think you can do without freelancers or that the experience of your in-house technical staff is enough? To convince you of the importance of hiring experienced AI developers, we can show you the details of the AI software development process. You'll see that it's a complex process that requires really valuable, proven expertise, updated with fresh knowledge.

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What it will give you:

• You'll have the expertise of specialists who work with AI technology every day, which means their experience is up to date and extensive.
• These developers are trusted because they are full members of your contractor's team, working full-time and available at all times.
• Not only do you have the developers on your side, but you also have an experienced project manager, which is important for effective management of workflow, time and other resources.

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Believe it or not, hiring the right people from the start will save you a lot of time and money.

Plan each step and the overall strategy

You might think that every company starts by clearly and consistently planning all the steps involved in implementing AI and other workflow integration tools. In fact, this is not the case. You're right, it's a bit strange. 

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However, unlike those companies that have skipped the planning stage (which, of course, is not a good idea), you can go the other way and plan your actions.

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Here's what you should consider in the first stage:

1. Clearly understand why you need to implement the latest technologies and what you expect them to do;
2. What business processes do you expect to improve by implementing AI and ML? Imagine you already have integrated workflow solutions in place. What benefits will they bring to your organisation?
3. Work with your team to develop the most effective algorithm for creating an AI solution, including how to train, test and improve it;
4. Learn about common software mistakes to avoid in your organisation, so your team can prepare for and avoid bottlenecks in advance.

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You should have a clear idea of the sequence of development and deployment phases, be aware of available resources, and know what to do in case of an unexpected situation (including an idea of available resources).

Know what AI technologies and tools you will be using

Again, this problem can be avoided by hiring experienced developers. But what if the developer lacks experience and knowledge and therefore makes a mistake in choosing the right technology?

The advantage of working with experienced developers is that they will know exactly how a particular technology will behave with your existing technology stack, how to integrate any existing software with the chosen technology, how to scale the product, how to improve it, and so on. AI and ML models need to be constantly monitored and improved to maximise performance because they are not static. You will need to create mechanisms to track key performance indicators of the engines, collect feedback and iterate. Some of the technologies you'll need include Amazon SageMaker, TensorFlow, PyTorch, Kubernetes and others. 

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Working with artificial intelligence and other AI-based tools requires expertise, so take your time, listen carefully to your team, and work in a coordinated way. 

Start with small tasks and build up gradually

To avoid big problems at the beginning, start with small tasks. These should be small but manageable endeavours with quick results, or at least a quick understanding of whether you are doing the right thing. This cautious approach will allow your organisation to gain valuable experience, build confidence and then take action based on quantifiable metrics and concrete results.

Start developing AI/ML software to integrate into your workflow 

With the team and technology in place, it's time to start building, training and testing the system. Make sure you choose the most appropriate project management methodology (experience shows that Agile gives you the freedom and flexibility you need) and that your team uses the most effective and up-to-date methods for training AI models. 

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Keep an eye out for ethical and other issues to address them in time

Another thing worth adding as a best practice for seamlessly integrating AI/ML software with your existing tech stack is the need to keep up with the changing rules of AI use and ethical standards in this regard. The fact is that AI brings not only benefits but also concerns. To cope with those that evolve alongside the technology, you also need to keep an eye on the regulatory and ethical considerations (such as GDPR compliance) that must accompany the technology's development. Therefore, you need to be aware of the relevant laws, regulations and industry standards to ensure that AI is compliant and secure as part of your workflow integration tools. 

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Conclusion

Integrating AI into your existing technology stack is no walk in the park. To get the process right and get the results you want, you need to bring in the right expertise, understand how AI will benefit you, take stock of your data, existing technologies and tools, and generally have a long-term plan for AI and ML tech stack integration. Regardless of what your business does, and even if you're not a tech-savvy customer, it's helpful to know how you can leverage modern technology to gain a competitive edge in the market. In this article, we have outlined the key stages in the process. We hope you find the information useful and relevant