Artificial Intelligence – these are two words we see everywhere these days. Some companies say, “We are the AI-first company”, others say, “ we build AI-powered products.” We have realized that these buzz words are not helpful when we want to build a team that can use AI as a tool to solve problems. We must cut through the noise and understand what AI is, what problems it can solve, and how to go about exploring AI, building the AI team, and finally integrating AI with the product.
If you are a product manager, product owner, CEO, programmer or an entrepreneur who is looking to implement AI in your company or your product this blog will give you a high-level overview of the most important things to consider. We call it the 3T approach or the 3T checklist for AI strategy.
The first T of our 3T strategy is the ‘Team.’ AI requires different types of skills, such as data engineering, data science, and machine learning. It is almost impossible to have one person to have all these skill sets. We know there exist no 10x engineers. It requires a team of 1x engineers. Your team can be both an in-house team or a remote team.
Therefore, the team should consist of talents from the following disciplines:
- Data Engineering
- Data Science
- Machine Learning
- Software engineering
- DevOps Engineering
Apart from these, you also need business leaders, managers, and domain experts in your team. It is up to the engineering team to make sure the business stakeholders know what can be done and what cannot be done with AI.
The most likely trigger for AI-related conversation is because one person who has heard about it somewhere. The person can either be a member of leadership or anyone from any hierarchy of the company. The alignment should not be about whether a company must have an overarching AI strategy. Instead, it should be that AI is interesting enough to invest resources in exploring. That’s what we mean by alignment. The smaller the scope, the faster it gets to align the leadership team and start taking action to show progress.
The team you assemble should be of practitioners who can experiment with new ideas through fundamental and applied research.
We don’t see Machine Learning as a black box technology. We don’t see it as a hammer that is looking for a nail. One should unbox machine learning technologies and choose the right algorithm or approach as per the problem. But don’t be overwhelmed by what you find inside. There are already resources online that can guide you to the right algorithm/approach suitable for your solution. Plus it’s all about discovery, you will figure things out as you progress with your product.
Here are some of the types of systems we can build using machine learning. In the machine learning lexicon, we call it supervised learning algorithms.
- Natural Language Processing
- Deep Learning
Tools, frameworks, and platforms
Once the idea is in place, the team should know when to build the data pipeline and choose the right machine learning algorithms or create one from scratch. They should also research and be aware of when to use the state of the art tools, frameworks, and infrastructure platforms. There are plenty of tools available today, and the computation limitations of your machine do not bind you. Cloud AI is gaining popularity, and it is one of the cheapest and most efficient ways to test and run your models.
Some of the tools that we use and we suggest to look after are the followings: