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Do you want to create an AI solution, but don’t know how to start?
Starting AI research might be tricky, and you should avoid many pitfalls in the initial development phase.
I have prepared a short list of 5 tips to help you start working with machine learning and guide you through the research process.
Know your business case, decompose it and understand dependencies.
Understanding the case you want to solve and its context, goals, and constraints is essential. You should know the purpose of the AI solution, what are input data, from what source, what is the expected outcome and why. You should map and understand functional and non-functional requirements for this solution. It will let you make the correct technical decisions.
Be precise when defining the task for the AI. Ensure you know what can bring the essential business value.
Analysing the project requirements and trying to figure out technical solutions, you should be precise and careful when defining a target task for your AI. Primarily, ensure that it is aligned with the business case. It’s easy to overshoot and to fall into a trap and create incredible machine learning models with an outstanding performance which does not solve any business problem.
Start small, mitigate the risks, and iterate.
It takes time to create a robust and quality solution. Do not try to solve all your problems and challenges with a single AI pipeline and a single iteration. It’s an incremental process, and you should develop Ai building blocks that can create a complex solution in the future. What’s more, do not expect a robust, high-quality machine learning model after the first iterations. Your PoC will probably be good for common examples and situations, but the corner cases might still be too hard for the AI model. It’s OK. You can improve it in the future.
Availability of balanced, clean, relevant and up-to-date data sets significantly increases your chance of success.
Data is the most crucial resource for any machine learning project. It can help to identify patterns and make predictions. But it should be clean, relevant, and up-to-date before starting your machine learning experiments. Data cleaning is often one of AI research’s most time-consuming and labour-intensive parts of the research process. Therefore, ensuring that data is unbiased and aligned with the business problem you want to be solved is vital. Otherwise, you might end up with a lot of useless data that will only slow down your process and confuse your algorithms.
Expecting AI to give you always 100% accuracy is typically unrealistic.
AI is not perfect and never will be. No matter how advanced and robust is your machine learning model, there will be a few mistakes here and there. Therefore, you should choose your target metrics values wisely and be sure they are high enough to bring you a business value but with room for errors and improvements. Moreover, it’s crucial to set your priorities, like do you prefer high precision or recall. Bad decisions about expected performance may unnecessarily ruin your AI project in its early stage.
These five tips may not solve all of your problems and challenges, but they can make it a bit easier to start the work on your AI solution. I hope they let you avoid some critical mistakes many make at the start of the project.
Do you have other advice and things to remember starting the AI project? Please share it in the comments.
Have an idea for AI functionality you’d like to implement? Let’s discuss how our AI skills and expertise can support your business and create a solution for you. Get in touch.
Follow me on LinkedIn and visit the Responsappility website.
This article is an enhancement of the Responsappility slide show.
This article was originally published on my Linkedin profile.
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