The Ultimate Guide to Building and Managing AI Frameworks

With GenAI playing on everyone’s mind, I’m sure you would concur when I say that reliable artificial intelligence 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 technology, including what it means, its key components, and what drives it. So, without further ado, let’s dive into the AI world.

What does AI mean?

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.

What are some key components of AI?

Let’s get acquainted with the key components of AI:

Fig 1: Key components of AI

Now that you are familiar with the elements that constitute AI, let’s turn to the drivers of AI.

What drives AI?

• Increase in computing powers of machines: The expansion of AI is closely linked to the growth in the processing power of powerful computers.
• Cloud computing: AI on the cloud is a robust technology that can automate monotonous tasks, take decision-making to the next level, and augment productivity.
• Rise of data-based AI: The goal of data-centric AI is to enhance data quality and attain better outcomes by viewing code as an entity that is set in stone. AI can deftly work with data analytics, analyzing colossal datasets and forming actionable insights.
• Progress in deep learning: Deep learning drives an array of AI applications and services that make automation better. Analytical and physical tasks can be performed effortlessly, removing the need for human intervention.

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.

Building an AI Framework

Here is a list of the steps you must follow to build a strong AI framework:

Fig 2: Building an AI Framework

Now that you have a robust framework in place, let’s consider the management of AI.

Managing an AI Framework

Here are some good ways of managing an AI framework:

Fig 3: 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:

• Team: Which expertise is required? Are there the right people on board and do they have sufficient time to realize the project?
• Data: What data is available (in-house, open data, or for purchase) and can it be used to solve the analytical problems based on these data? Is it necessary to conduct data collections (e.g. a survey)?
• Infrastructure: Is everyone appropriately equipped with software, hardware, and cloud resources?

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.