The popularity of AI and ML technologies is so widespread today that companies feel pressured to introduce them or otherwise they will look outdated or fall behind their competitors. But a mere desire to be innovative is not enough and not all are successful at harvesting the full potential of AI. Lots of firms simply lack the tools or AI lifecycle management experience to carry out their data science projects.
Trying to overlook key practices for BI and analytics and go straight to AI adoption is a common mistake of many AI enthusiasts and the one that leads to devastating failures. So what is the right lifecycle of building AI products and services (which, by the way, vary from traditional software engineering lifecycles)? And how to build a strong AI foundation? Let’s discuss
Companies that are familiar with Big Data technologies and have successfully adopted them for data integration / ETL, data governance, and other data services, have a strong foundation for their future AI and ML projects. Others need to get some basic BI and analytics going first. And here’s our step-by-step guide:
Start by figuring out the key areas you want to focus AI technologies on. The general practice shows it’s better to involve data scientists at this stage to discuss which direction you want your project to take.
You need to decide which of the tasks you want to automate first. To help you decide answer these questions:
Is this particular task data-driven?
Is the scale the automation can bring here really worth the effort?
Is there enough relevant data to support the automation, is it clean enough, labeled well?
It’s important at this stage to clearly state your business objectives and make sure that the tasks you are putting in front of AI are achievable. we need to be certain we’re planning to task our AI with a problem that’s solvable with available data.
Next, data science team will begin with cataloging all the data sources, and researching how clean the available data is, its relevance to the task, whether there’s a properly labeled training dataset (or if further annotation is needed), or whether the data scattered across disparate MES, RIP, SCADA platforms (in different formats) is somehow joinable.
This is when you might discover that a good chunk of relevant data is missing, or that some service logs are inconsistent and unusable.
Data cleansing will substantially reduce your initial dataset, which means you may have to reconsider which area to aim the AI model at.
This stage is a teamwork between data science team and decision makers on compiling a list of features that have the strongest predictive signal. The data scientists then must score the predictive potential of these features against how hard they are to compute and pick a few optimal ones to kick off the experimentation.
Similar to engineering, start with the simple thing — build a baseline model, incorporate a few simple features, and iterate from there.
These common and on the surface simple algorithms (logistic regression, random forest) are the ones that often go into production; they have but a few parameters to tune, don’t require much training, and, in some cases, are surprisingly robust to overfitting.
Don’t rush into this. There should be an experimentation framework in place (even some primitive A/B testing will do) that would allow us to deploy gradually and minimize risks, as well as debug algorithms end-to-end.
The feedback from end-users should be incorporated into development early on, and we must remember that raw predictions solve nothing. We always need some post-processing functionality (APIs, workflow tools) to make the model’s outputs useful and explainable so that the company knows which factors are driving them.
Then, we’ll finally start working on a more advanced web app; sometimes making feature-extraction production grade is a time-consuming process and wrapping models into software packages that other applications can query for predictions requires a massive engineering effort.
Artificial Intelligence isn’t magic, it’s applied statistics and linear algebra.
Your models will be as good as the data you feed them. So if you only have databases that are full of inconsistencies, have gaps and are structured in some chaotic fashion, even the most advanced ML techniques won’t be able to help you desired results.
It’s a common practice to ignore the engineering part of all data science projects as they're not as exciting as modeling. But you must remember that the success of your AI endeavors depends on the quality of data your company generates and processes.
The post appeared first on Ukraine IT Outsourcing Company Perfectial.