The machine learning industry is going through some of the same growing pains that software developers also had. We are now figuring out how to scale AI using AI practices that make a lot of sense. As with anything, the main goal here is doing machine learning and artificial intelligence projects as efficiently as humanly possible. We want to reduce the problems that have plagued software over the years. For example, one of the biggest problems in software was being late and over budget.
We also had problems with maintenance and other issues that would keep certain products from even getting off the ground. Just like software, many machine learning initiatives end up failing. There is a high failure rate in the industry, and we need to know AI best practices that can help you deliver AI at scale. Because of our experience in software engineering, we have a lot of previous knowledge, and it is going to help us provide more value as time goes on.
Agile vs. Waterfall
The great thing about artificial intelligence is that many of the same processes in software engineering can be used in AI. We can create new AI practices, but a lot of it will be all the lessons we learned from software development. For example, we have the agile and waterfall processes in software engineering. Both of these can work well, but you have to figure out which process will work best for your specific scenario.
The bad news is that agile has won the war in software engineering. However, many artificial intelligence projects still rely on a waterfall process for the main workflow. In terms of AI best practices, you are bound to do things much slower by doing it this way. One of the most pressing issues will be to use agile in the same way we do for software engineering. That means will have to find an agile process for the data side of artificial intelligence applications. If you are looking to scale AI, it will be the main solution.
Wrangling Data the Agile Way
The main step we have to figure out is how to scale AI using the agile process. The data operations we use in AI are a little bit different than software engineering. However, we have to figure out AI practices that utilize agile. These practices have to deliver excellent value when compared to the alternatives.
The benefit of agile has been treating the user as a developer. We no longer have a long, drawn-out process to develop new features or fix bugs. We will have to take that same energy when doing artificial intelligence and machine learning. By doing it this way, we are guaranteed to deliver a much shorter development cycle for machine learning and artificial intelligence projects. It will eventually be what leads to AI at scale.
Scaling AI Processes
Data is at the heart of machine learning and artificial intelligence. Because of that, the way we use data is vital to how impactful our artificial intelligence projects can be. We have to ensure that we have excellent quality, velocity, and coverage. The way to scale is to have everyone in the organization access the data and the tools needed to build extremely accurate models. This is where having a dashboard like xpresso.ai can come into play. It allows you to scale AI using AI practices that have been proven to be effective. It fundamentally delivers the same benefits that agile data to the software development world.