All of these are end-to-end platforms on which you can build, train, and deploy AI models into your app.
And the best part?
One of these platforms is likely your cloud computing provider, as they’re market leaders in that segment.
This will make integrating and deploying your AI model much easier.
In short, you need the right tool for the right job – your AI app’s success depends on it.
Collect and prepare data
Data collection and preparation is a key step during AI app development.
Without high-quality data, even the best designed AI model will perform poorly.
You should always prioritize data quality over quantity.
Otherwise, it could cost you.
And we mean that literally – a Gartner survey showed that organizations believe bad data quality costs them an average of $15 million per year in losses.
To avoid losses like that, you need to make sure your data is properly cleaned and formatted.
But, first you need to choose the datasets you’ll use to train your AI model.
Luckily, there are a number of publicly available datasets you can use.
Let’s say you’re training a natural language processing model.
In that case, Common Crawlis a good option – it’s a free, open repository of web crawl data.
OpenAI used Common Crawl for 82% of the raw tokens they used to train their GPT-3 model.
And if you need more specific datasets, Kaggle and AWS Data Exchange host open datasets you can freely use for your AI model.
Once you’ve found the datasets you want to use, you need to prepare them before use.
You need to do:
Data cleaning
Data preprocessing
Data wrangling
Data cleaning is self-explanatory – it removes inaccuracies and inconsistencies like duplicate entries.
In simple terms, the point of data preprocessing is to improve the quality of the data you’re using.
Once you’ve preprocessed your data, your next step is data wrangling.
This is when you turn your raw data into a usable format for AI models.
And after you’ve done that, you can start training your AI model on that data.
To sum up, data collection and preparation is a key step when building an AI app.
And you need to do it right.
Design and train your AI model
Once you’ve prepared your data, the next step is designing and training your AI model.
Your AI app’s success depends on a properly designed and trained model.
The model is at the heart of your AI app and it needs to be up to par.
So, how do you design an AI model?
First, you need to decide on your training approach.
The 3 main approaches are:
Supervised learning
Unsupervised learning
Reinforcement learning
The choice of approach will depend on your specific needs.
Supervised learning, where the data fed into the model is labeled and the model has access to the correct answers, is the best approach if you need better accuracy.
For example, it’s used for image recognition and price prediction models.
Unsupervised learning, where the AI model learns on its own, is better for more dynamic models like recommendation systems or fraud detection.
And reinforcement learning, where the AI model’s desirable behaviors are rewarded, is a good choice for natural language processing models.
ChatGPT is an example of an AI model that uses reinforcement learning – that’s why it’s a good idea to point out when it makes a mistake.
Once you’ve decided on the training approach, you need to design your model’s architecture.
Ante is a true expert. Another graduate from the Faculty of Electrical Engineering and Computing, he’s been a DECODEr from the very beginning. Ante is an experienced software engineer with an admirably wide knowledge of tech. But his superpower lies in iOS development, having gained valuable experience on projects in the fintech and telco industries.
Ante is a man of many hobbies, but his top three are fishing, hunting, and again, fishing. He is also the state champ in curling, and represents Croatia on the national team. Impressive, right?