I’m sure you would concur when I say that reliable AI is well on its way to becoming a vital requirement in today’s business landscape. Its features of fairness, explainability, robustness, data lineage, and transparency, are indeed needs of the hour.
As for AI frameworks, they offer data scientists and AI developers the foundations to train, validate, and deploy models, via a high-level programming interface. I’ll be sharing my views on building and managing AI frameworks further in this blog.
First, I’m going to explain a few important pointers concerning AI, including what it means, its key components, and what drives it. So, without further ado, let’s dive in.
Artificial intelligence (AI) refers to systems or machines that mimic human intelligence to carry out tasks. What’s more, they can enhance themselves based on the data they collect!
It is a constellation of multiple technologies working together and enabling machines to ‘behave’ according to human-like levels of intelligence.
Let’s get acquainted with the key components of AI:
Now that you are familiar with the elements that constitute AI, let’s turn to the drivers of AI.
Now that you know about what AI is and what inspires it, allow me to explain a little more about how you can go about building an AI framework.
Here is a list of the steps you must follow to build a strong AI framework:
Now that you have a robust framework in place, let’s consider the management of AI.
Here are some good ways of managing an AI framework:
Engage stakeholders: Without the activity of defining project goals and metrics, it is challenging for the team to decide on priorities, and for an organization to glean value from its data science projects. This could look like asking for the help of senior data scientists whilst discussing new projects.
Ascertain how AI insights will be leveraged: Understanding the real business or organizational challenge that could be improved via a stellar AI / machine learning solution is an important aspect not to be overlooked.
Exemplify the data science process:
A data science project will have to go through various key phases: defining a problem, data processing, modelling, evaluation, deployment, operations, and communication of results. While defining, we need to identify the external and internal sources which might help to address the business problem.
Once we have your data and the problem sorted, we can then go about performing the all-important task of data processing. This ideally is a time-consuming task and we will have to ensure that we are using the correct format of the data for our models.
In the next phase, we will identify our best model and evaluate it against our metric of success. After getting desired results, we may want to put this into production. It may be in premise or some cloud infrastructure. Along with deployment, we may need to understand what sort of maintenance our application requires. We should see that we are automating these processes.
Understand your team, data, and infrastructure:
Know how to coordinate within and across your IT and AI project teams:
You must have a viable agile process in process. This is so that you can smoothly coordinate efforts across the team (how to prioritize different tasks, for example), and within an AI/data science effort (when to go back to a previous phase of a project, for example).
Know when / how to scale the solution:
Generally speaking, it is a good idea to start small (imagine a proof of concept) and then gradually scale the solution, so it takes the form of iterations (instead of directly deploying the system in one go). So, I would recommend that you always opt for an incremental route while thinking about scaling the solution.
Do bookmark this blog if you’d like to use it as a pocket guide to help improve the chance of delivering a useful AI project. Write to us with your feedback and explore our cognitive engineering offerings at Nitor Infotech.
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