If you’re interested in AI model deployment, you probably already know that there are two distinct areas that play major roles in bringing your model to life: prototype and production. And this lengthy process can seem quite overwhelming, but in reality, it can be broken down into distinct stages.
In this article, we’ll cover the various stages of both prototyping and production, explaining the various phases your AI model will go through as it prepares for launch. We’ll also cover the knowledge that is good to have before you dive right in, in addition to the time you can expect to spend in each stage.
Let’s dive right in.
AI Model Prototyping
The first step in any sort of AI model prototyping is to determine the problem you’re trying to solve. Ask yourself these questions:
What problem am I trying to solve?
How will my AI model solve this problem?
How does it differ from existing solutions?
Who am I designing this model for?
How will the users benefit from this AI model?
What does success look like for this AI model?
How will its success be measured?
With these answers clearly defined, you’ll be ready to move into the next stage: evaluating your data.
The importance of data in AI model prototyping
All AI models are backed by data and that’s why ensuring your plan for collecting, using, and analyzing data is sound before you begin. For the idea you have, what data will you need? How will you collect this data? And are you sure that the data you’re planning to collect is quality data?
Ensuring you give this step the proper research is crucial for the success of your prototype; the last thing you want is for you to advance far into your prototyping only to realize you can’t collect the type of data that you need.
When looking at your data, these are the important things to keep in mind:
The availability of data: you don’t want to be limited in what you can do because you only have a limited amount of data to go off of; guaranteeing that you have multiple, trustworthy sites to source data from will help your model be more effective and accurate.
The quality of data: if the data you’re looking at is frequently missing values, inaccurate, or untrustworthy, that will hurt the overall effectiveness of your AI model. Before you advance too far into the prototyping process, ensure you will have continuous access to quality data.
The ethics of data: you’ve probably heard about ethical concerns within AI models and that’s because there are many problems with using biased data to train AI models. Review AI ethics and data ethics to ensure your data won’t contribute to bias.
Picking the right tools
The next step in prototyping your AI model is figuring out exactly how you’re going to both create the plans for it and start building and training it. Depending on the way you work best and the preferences of your team, you may choose sketches and storyboards for the idea generation process, and then switch to wireframes and mockups to design the actual interface of the model.
Once you have the basics decided, you’ll need to choose the programming languages and frameworks that will support your AI model. Python is one of the most frequently used languages, especially the TensorFlow and PyTorch libraries.
Training and testing the model
With your AI model created, it’s time to start training and testing the model so that you’re able to see where things need to be improved and where you’ve been successful. Throughout this process, you’ll also be looking for errors, biases, or other issues that arise so that you can correct them before your product heads to market.
This phase can be tedious and frustrating, but is essential for the eventual success of your AI model. Take your time and don’t cut corners–this is the time to create the best possible version.
AI Model Production
When you’re happy with your AI model prototype and think it’s ready for the big leagues, it’s time to move into the production stage. The first step is to choose the deployment environment; there are a lot of options out there, but keeping these factors in mind can help make the decision a bit easier:
Your teams’ skills: is your team experienced with various deployment environments or is their experience limited to a specific one?
The environment’s cost: do you have a budget that you need to stick to when choosing a deployment environment?
Deployment security: does your AI model require specific security measures?
Deployment integration: will your AI model need to be integrated with other environments?
Creating a containerized model
Once your environment is chosen, you must containerize the model so that it’s together with its dependencies and the runtime environment. This is an important step in the deployment process; once you’ve containerized the model, you will then need to set up the proper infrastructure in the chosen environment.
Now’s a good time to ensure that network and security settings are correct and that the model has access to all the resources it needs.
Deploy and monitor the AI model
It’s go time: your model is deployed and live! Although it might seem like your job is done, you must set up tracking mechanisms so you can see its progress and correct any flagged issues quickly. It’s also a good idea to keep some of your teams’ time free to deal with any unforeseen issues before they move on to their next product.
Continuous integration and deployment (CI/CD)
Even once the model is up and running for some time, your job still isn’t done. Updates will always be a part of the AI model process and you’ll have to ensure that it meets all regulations and is safe from hackers or other nefarious activity. In addition, new features might be added over time and you’ll need to have a way to seamlessly integrate them into your model.
Deploying an AI model from prototype to production may seem daunting at first, but breaking it down into clear, manageable stages makes the process much more approachable. From defining the problem, preparing data, and testing your prototype to selecting the right deployment environment and monitoring its performance, each step plays a vital role in your AI model’s success.
Remember, continuous updates and improvements will keep your model running efficiently long after deployment. By following these steps, you'll set yourself up for a smooth and effective AI deployment journey.
About the Author:
Juliette Carreiro is a tech writer, with two years of experience writing in-depth articles for Ironhack. Covering everything from career advice and navigating the job ladder, to the future impact of AI in the global tech space, Juliette is the go-to for Ironhack’s community of aspiring tech professionals.
