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Try Overchat FreeTo create an AI model you need to first plan out what it’s going to do, and then develop it. Here’s the outline of the general steps:
- Outline the goal
- Collect and pre-process data
- Choose a model type
- Choose a framework and tools
- Train and evaluate
- Deploy and monitor
Let’s unpack each step in more detail.
1. Outline the Goal
Before jumping into actually creating the model it helps to have a general idea of what it will be used for. This will help you make a list of requirements for the model, as well as create an actionable roadmap.
To outline the goal:
- Define your objectives, problem scope and goals. For example, do you want the model to predict sales, or recommend products? Is it a classification or a recommendation model? Or an autocompletion model, also known as an AI chatbot?
- Make a list of requirements. This will help you gather all the resources before the project’s start, as well as choose the right tools. Consider accuracy, latency, data privacy
- Do a feasibility study. Check yourself: can I access the data I need, is it even technically possible to make the model do what I want, how much will it cost to run, and how long will it take to complete the project? Then decide if the project is practical with existing resources.
- Define measurable KPIs. Consider using metrics such as accuracy, F1-score, and ROI. This will give you an objective way to tell if the project was a success.
Tips: Ensure problem statements tie to the real value of your users or yourself.
2. Collect and pre-process data
You need to collect the data that will form the future knowledge base of the model. This step is absolutely critical. Here’s what it might look like, generally:
- Identify your data sources. List all relevant databases, logs, APIs, or public datasets you’ll use for pre-training.
- Collect all the data. You can use APIs, web scraping, database queries or data engineers to extract data.
- Label everything. For supervised problems, label the data accurately, using manual annotation or tools. Unsupervised tasks do not require labels.
- Clean the data. Any missing values, outliers, and duplicates should be cleaned.
- Prepare the final dataset: Merge or join multiple sources, and create new features if helpful. For image tasks, apply augmentation (rotations, flips) to expand the dataset.
- Split Data: Reserve separate sets for training, validation, and testing. Typical splits are 70/15/15 or using k-fold.
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Now that we understand the general steps, let’s look at an example to understand how to apply them in practice.
Let’s say you have a spreadsheet called reviews.csv with two columns: one for the review text (e.g., "This phone is great!") and another for the sentiment label (e.g., "positive" or "negative").
This file is your data source. Open the file in Excel or Google Sheets. If the labels are missing, you or someone else will need to go through and manually assign them based on whether the review is good or bad. This is your basic labeling step.
Next is preprocessing. First, remove any rows that are empty or duplicated — most spreadsheet tools have a built-in function to “Remove Duplicates” or filter out blanks. Then standardize the format: for example, make sure all review text is in lowercase so “Great” and “great” are treated the same. You now have a clean, labeled dataset in table form.
Finally, split your data into three parts: the training set (to teach the model), the validation set (to tune it), and the test set (to check how well it performs on new data).
The simplest way is to sort your rows randomly and then copy 70% of them into one sheet (training), 15% into another (validation), and the final 15% into a third (testing).
Tip. Remember the "garbage in, garbage out" rule. The quality of your model depends on the quality of the data you used during pre-training.
3. Choose a Model Type
Select a learning paradigm that fits your problem:
Choose a model that fits the type of data and the goal of your project.
- If you're working with images or sound, use a neural network like a CNN (for images) or RNN (for sequences like audio)
- If you’re working with data in a table use decision trees or SVMs
- For recommendation systems use collaborative filtering
- If you want to predict yes/no answers, logistic regression is a good choice.
- If you’re predicting a number, like the price of a house, use linear regression.
Tip: using the wrong model for the task is a common mistake to avoid.
4. Choose a Framework and Tools
When choosing, consider community support, hardware compatibility, and how well you’ll be able to work with each tool.
Tip: Start with high-level APIs — Keras or Scikit-learn, before working with lower-level TensorFlow, or PyTorch.
5. Train and Evaluate
Training means showing your data to the model so it can learn patterns and improve. During training, the model makes predictions, checks how far off it was, and then updates itself using an algorithm.
You can adjust hyperparameters — settings like learning rate (how fast the model updates), and batch size (how much data it sees at once). You can tune these by trying different combinations manually and seeing what produces the best result.
Creating a new AI model is as much an art as it is science!
But one pitfall you’ll need to avoid is overfitting. Overfitting happens when your model memorizes the training data instead of learning general patterns. To prevent this from happening, split your data into separate training and validation sets.
A simple rule: if your model is too complex and you have too little data, it will likely overfit. But if your model is too simple, it won’t capture useful patterns — this is called underfitting.
To see how well your model works, test it on data it hasn’t seen before. Choose metrics that match your goal:
- For classification: use accuracy, precision, recall, or F1 score
- For regression: use RMSE (Root Mean Squared Error) or MAE (Mean Absolute Error)
Tip: Always evaluate on the validation or test set — not on the training data.
6. Deploy and Monitor
How to Deploy
Once your model works well in testing, it's time to put it into use — this is called deployment.
Decide where the model will run. You might run it in the cloud, on your company’s servers, or directly on devices like phones or cameras, which we call edge devices.
This will determine how fast the response needs to be, how private the data is, and how much traffic you expect.
To serve end users, you’ll turn the model into a service.
This usually means wrapping it in a web API using Flask or FastAPI, or using a tool like TensorFlow Serving or TorchServe. To make sure it runs the same way everywhere, you can package the service using Docker containers, and use Kubernetes if you need to scale up to many users.
How to Monitor
After the model is deployed, connect it to your existing systems: websites or apps. That’s where real users will interact with it. Before launching widely, it’s always a good idea to run some integration tests. This is a way to check if the model works with other systems. Load tests are also useful — to see how it handles many users at once.
Last but not least, you need to monitor how the model performs in the real world. Track accuracy and error rates. Also, watch out for data drift — when the kind of input the model sees starts to change over time. This can slowly break your model if it’s not retrained.
Bottom Line
Creating a new model from scratch is not easy, and all we can do in this article is give a general idea of the steps involved. Thankfully, there’s a way to continue learning — ask AI questions, and get step-by-step guidance and even working code.
Have questions left? Head on over to Overchat AI, ask our free chatbot, and build the model together.
