With traditional software models, the software does not automatically improve itself. Once implemented, traditional models deliver results immediately and the short-term impacts are easily visible. AI models take time to gather data and implement solutions. Far from a solution or remedy, implementing an AI model is a journey, and not all business owners are patient enough to realize its full potential.
These are the six steps to building your own AI model. Each step should be seen as the next leg of a journey towards automation and increased efficiency, and once all steps are completed, the journey is far from over. A successful AI model will be continuously learning, improving, and streamlining operations in a never-ending cycle of improvement.
Step 1: Define the Problem
Every AI model should be designed to solve a specific business problem. Before you start your AI project, you should be able to answer the question: ‘How will the project benefit the business?’
The more detailed the answer, the easier it will be to define, and measure success once completed. You don’t need to set precise targets at this stage, but you should have clearly defined objective of what you hope to achieve.
Step 2: Collect Data
What data set will you AI project leverage? This could be structured data with a set format (like names, numbers, addresses, etc.) or it may be unstructured (pictures, videos, audio, emails, WhatsApp messages, etc.), but every AI project will utilize some data.
Before you can begin writing algorithms, you will need to ensure you have access to the required data and it is “clean”. Clean data essentially makes it readable. It is consistent, labelled, accurate and there are no duplicate entries.
Step 3: Define Success
Once you have the data, you need to decide what success would look like. This will largely depend on the purpose of your project. If you are building an AI system to recommend films to a user based on their previous behavior, you may be satisfied with a system that delivers a matching film 70% of the time. If you are using AI to identify criminals from CCTV images, you may require a more accurate system.
Step 4: Decide on Necessary Features
You can always remove or add features later, but you should have a strong idea of what features are needed to build your model. Features are basically the data within the data. It could be something like the number of times a user logs into their account, or the average heart rate of a patient. It could also be something derived from the data you have, like the time elapsed since the last login (built using login time stamps and the number of logins).
Step 5: Build the model
Now the fun part begins, actually building the model. When thinking about your model, it pays to consider whether you will need to build a model that is interpretable. Interpretable modelling is where you want to clearly understand the model and its responses. For example, if you are assessing the potential outcomes of different marketing strategies, you will need an interpretable model that clearly shows the impact of advertising streams.
Step 6: Fine tuning and enhancing your model
Once the model is built, the journey isn’t over. Now you will need to fine-tune, change hyper parameters and optimize learning rates. You will discard unnecessary features, further increase the efficiency, and constantly be on the look-out for ways to improve its function and effectiveness.