DEVOPSdigest

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

Start with: What Companies Need to Know to Advance AI Adoption with MLOps - Part 1

What Worked and Didn't for Other Contexts

As the adoption of AI goes mainstream, it's time for small and medium-sized enterprises (SMEs) to be inspired by the recipe that big tech companies have followed. However, SMEs shouldn't directly imitate big tech as they do not have near-infinite funds or extremely qualified professionals. As was the case with the inception of the internet, it took the industry a good chunk of time to learn how the web could revolutionize business. There is no reason why we shouldn't expect a similar scenario with AI.

The first learning from big tech is: Most large companies — wanting to adopt AI — hire teams to build internal platforms for ML practitioners. But these data scientists or ML engineers are often not familiar with enterprise software engineering. Expecting them to learn is feasible but inefficient, which is where MLOps platforms come in.

If you know enough people working at scale-ups and big corporations, you will have heard about companies trying to develop their own ML platforms internally. After all, it worked for Uber(link is external) with "Michelangelo." Unfortunately, that route doesn't work for companies that haven't managed to secure large amounts of funding. Even Uber stated(link is external): "As we evolve Uber's machine learning infrastructure and platform and support new machine learning use cases, we see new MLOps challenges emerge." Be wary of this route, and don't rush down that path unless completely necessary.

Companies could also look to Tesla, where a big part of their success can be attributed to their mastery of AI and high rate of innovation(link is external), which has been part of Tesla since the company's early stage. Tesla is tackling the data collection and model improvement feedback problem relevant to real-world ML applications. They use data feedback loops to collect the best data on self-driving cars to develop their existing models and solve ML challenges(link is external).

For years, Amazon has also been using ML to enhance product recommendations, create personalized experiences, and even allocate products in distribution centers to minimize transport costs and maximize delivery efficiency. And in return, they created Amazon SageMaker to help streamline the ML lifecycle by automating and standardizing MLOps practices for organizations.

Choosing a MLOps Platform

The number of choices for MLOps platforms can be overwhelming. Here are some factors companies should keep in mind when choosing one:

■ Ease of use: The platform should be easy to use and shouldn't require extensive training to get started. The idea is that companies can immediately use ML platforms without needing a full-on expert in engineering. Unfortunately, few platforms offer good user experience (UX).

■ Flexibility: The platform should be flexible enough to accommodate different types of ML models(link is external) and workflows, such as supervised, unsupervised, and reinforcement.

■ Scalability: MLOps platforms should be able to handle large-scale data and computations. Ideally, you should delegate scaling to a tool, and it shouldn't require you to manually ask it to increase scalability as the demand for models can likely be dynamic.

■ Security: Look to see if the platform provides security features such as authentication and authorization. ML security is important because ML systems often contain confidential information that organizations would not want the public or competitors to have access to.

■ Pricing: Affordability is a must and the platform should offer varying pricing models depending on your company's budget. Try to avoid any products that require a fixed fee per month. That usually means the SaaS company doesn't have an optimal architecture behind to support on-demand, volume-based charges.

■ MLOps tools are sometimes not enough: Companies and business leaders at the very beginning of their AI adoption journey want to see direct results from using AI quickly. Look out for platforms that offer pre-trained models and ready-to-use software infrastructure to complement the MLOps tools.

Various industries have taken a keen interest in AI to accelerate the new industrial revolution, but most companies will not be able to reach their AI goals working alone. Those trying to adopt AI without the proper tools will suffer slow turnover time for projects.

MLOps is quickly becoming mainstream and sought after by data scientist professionals wanting to accelerate and automate ML lifecycles and build highly scalable software infrastructure at companies of varying sizes. But business leaders must ensure these platforms align with their business objectives and goals, offering flexibility, scalability, and security for successful deployment.