When is the right timing for setting up the MLOps practices?

We asked our Head of AI Solutions Alexander Finn to sum up the signs that indicate the need to start building a full-blown MLOps infrastructure and processes. Alexander is an expert in creating complete software development lifecycles for machine learning projects, all the way from requirements analysis to production operations with a special focus on agile and lean methodologies.

When do I need MLOps?

According to Alexander Finn, when the following three statements are true at your organization, it’s time to move from early AI experimentation towards building a robust MLOps process.

1. The machine learning-based solution is at the core of the organization’s business

As an example, think about a company selling large machinery with intelligent quality control or a software company that builds its value based on identifying the user’s needs. Both of these businesses rely on machine learning to bring extra value to the core offering and the whole business. Another thing in common is that the machine learning solution is part of a bigger software solution. It is crucial that MLOps becomes a part of a larger DevOps process and integrates the deliverables of the data science team into the overall software delivery process.

Such a solution needs to be continuously monitored, improved, and updated. Therefore a streamlined process for training, deploying, and managing the updates – both in terms of code and the model – needs to be in place.

In addition, there are oftentimes many data scientists working on the solution and they need to have a streamlined and standardized process of testing, validating, and deploying their changes to the production environment – which brings us to the second statement.

2. Your data science team grows beyond 2–3 experts

When you have one hero data scientist experimenting with one model to validate the business value to estimate the return-of-investment, it’s fine to run the experiments on one machine and have a model version history on one’s laptop.

But as soon as the team grows beyond two to three experts it is impossible to get a transparent view of what is happening and how the team progresses. The proper MLOps process enforces knowledge sharing and allows data scientists to share the same best practices and infrastructure solutions needed for the scalable data science teams. Such a process also reduces the onboarding time for new data scientists and the effort needed to help junior team members to become productive.

Getting a machine learning or deep learning model into production is not a simple process and requires a variety of expertise not only in model building but also from the engineering side. Oftentimes, data scientists and AI experts who are good with data or have detailed knowledge about the latest ML models are not the ones who possess a deep understanding or even care about writing ssh scripts to connect to a cloud server to orchestrate the instances or about using docker to pack their models to Kubernetes environments for deploying.

A proper MLOps infrastructure eliminates the need for the data scientists to understand the details of the deployment process and helps them to be productive at what they do best – at building the intelligence inside your core offering.

3. There are more than one production ML solutions

It’s natural that companies start experimenting with just one or a few possible use cases for machine learning. However, soon after the business value has been proven, companies seek new ventures to benefit from AI.

For example, Tesla cars’ autopilot has 48 different neural networks embedded and Netflix uses several machine learning and deep learning techniques to support their operations across the company. There must be a technology agnostic and streamlined process to allow data scientists to share the infrastructure for ML development and deployment purposes. This also dramatically reduces support efforts required from the IT department. Such a process enables organizations to ensure the required level of quality, reliability, and transparency of the delivered solutions across all the existing ML cases.

Are you ready to take the next step?

If you check all the boxes, it might signal that Silo AI – with a track record of hundreds of real-life AI projects – could support your way towards scalable AI operations. MLOps is not just an off-the-shelf product but a process that puts people working around the machine learning solutions to the core of the process. MLOps is always customized for your special needs, be it a manual validation step for the model before the deployment or a check-point to ensure that privacy, quality, and model bias are taken into account. Silo AI makes sure that you have everything covered. To learn more, watch the webinar recording: Building operational AI/ML with MLOps.

Would you like to discuss MLOps with our experts? Get in touch with Teppo Kuisma at teppo.kuisma@silo.ai.