DEVOPSdigest

Artificial intelligence (AI) will likely have more impacton our society than the Industrial Revolution. It should come as no surprise that AI has overpassed the terms big data, cloud, and machine learning (ML) combined in corporate earning calls.

AI will be the key differentiator between companies that strive for high margins versus those that can barely stay afloat — AI adoption goes hand in hand with cost decreases. Amidst the rapid rate of AI advancement, there are countless benefits for companies who leverage it internally or to improve customer experience.

But assuming that most companies already using AI are observing a cost reduction of 20%, why are others not following the lead?

Companies' adoption rate is still very shy due to a lack of available tools to streamline AI development, with an average change of +2 percentage points across industries from 2020 to 2021. However, machine learning operations (MLOps), combining ML and software engineering, are becoming more mainstream and intend to improve the quality and speed of delivering ML models to production.

AI professionals, such as data scientists and ML experts, often spend too much time on the nitty-gritty of software engineering and should be focusing their energy on teaching machines to learn instead. MLOps allows for collaboration and communication between data scientists and operations professionals to simplify management processes. These AI professionals can focus on their domain of expertise and automate the burden of creating AI from zero in large-scale production environments.

Business leaders are missing out on many opportunities without proper MLOps platforms in place. So, how can companies get started?

Look at What to Consider Before Implementing a MLOps Platform

A MLOps platform can benefit companies of all sizes. Still, it is important to think about how much data your company generates and whether you have the resources to manage and process this data. This is where business leaders should make sure the technology they want to adopt aligns with their business models.

Since implementing a MLOps platform internally can be costly, it is important to consider your company's budget and whether the platform's benefits justify the costs. Some companies may not be willing to allocate a budget to have an entire team of MLOps engineers to support their data teams.

Business leaders need to weigh up whether to build a ML platform internally or employ a MLOps platform from a third-party provider. For most scenarios, using a SaaS offering is the best way, especially a pay-per-use model with no fixed fees. Unless you're a technology company wanting to offer MLOps as a service, there should be no need to build your own MLOps platform.

Then, look at your objectives and goals: What do you want to achieve with a MLOps platform?

Do you want to automate the ML lifecycle to help increase team efficiency?

Are you looking to improve collaboration across your organization by tracking each step in the ML lifecycle?

Do you want to deploy infrastructure as code (IaC) or continuous integration/ continuous delivery (CI/CD) tools to automate building and testing?

Before reaching out to SaaS providers to implement a MLOps platform, assess your company's current infrastructure, technical capabilities, team experience, and where you could benefit the most from AI. You could first look for low-hanging fruit, such as tasks that have a heavy financial burden but don't require creative intelligence.

These steps will help to ensure that any platform can be seamlessly integrated into your company's existing systems. Some companies with pre-defined models and senior professionals with data science experience often choose to embed AI and custom models into existing tools and processes. Others, like startups, decide to have AI at their core from the beginning but also look for solutions with a pre-trained model so the team can integrate it quickly and fine-tune where necessary.

Go to: What Companies Need to Know to Advance AI Adoption with MLOps - Part 2