Disconnects between development, operations, data engineers, and data science teams might be holding your organization back from extracting value from its artificial intelligence (AI) and machine learning (ML) processes. In short, you may be missing the most essential ingredient of a successful MLOps environment: collaboration.

For instance, your data scientists might be using tools like JupyterHub or Apache Spark for processing and big data analysis, while operations and developers might be using Kubeflow and Prometheus for deployments and monitoring. They might be all working towards the same goal, but using different tools and processes to get there, and rarely crossing each others’ paths. 

As DevOps, DevSecOps, and now MLOps have shown, it takes real-time collaboration, hand-offs, and transparency into workflow processes to help ensure development projects are completed successfully and in the most agile way possible. Teams should not work independently in this kind of environment; instead, they should work in concert to achieve the shared goal of creating data-driven applications.

Here are three strategies to bring your teams closer together and ensure a secure and successful application production pipeline.

Commit to Collaborating

Too often, teams are siloed into their own work. Developers work on code. Data scientists and data engineers work on data sets. Operations managers see to it that the right tools are being used properly and as securely as possible. Everyone works independently.

But this process does not lend itself to simplicity and speed, especially when highly complex data sets are involved. Information can get lost or misinterpreted. Sometimes, the data sets that data scientists are working on may never even be used in the applications that are being developed.

But data science is integral to your development processes, which is why you must commit to a culture of collaboration in the form of an MLOps environment. Start by integrating data scientists directly into your workflows. Make them part of the continuous integration/continuous delivery (CI/CD) process for the entire AI/ML lifecycle.

This helps everyone involved. Data scientists’ efforts can be deployed in different ways and in different applications, developers can work hand in hand with the data scientists and engineers to help ensure their data sets work well within the context of the applications and can scale when rolled into production, and operations managers can help ensure that both groups have access to the tools they need to complete their tasks. Along with having a clear data strategy, it is one of the most important components of data-driven development.

Support Self-Service

Next, it’s time to support that collaborative environment by democratizing access to the tools different teams depend on. The best way to do this is to create a self-service practice that enables users to more easily access solutions on their own accord.

For example, data scientists might want access to a bevy of tools to help them do their job without having to become AI experts. But different data scientists might have different preferences, or use specific solutions for various data sets. Giving them access to a set of preapproved tools from a central hub accessible to the entire team – and then enabling them to pick and choose between different solutions for different purposes – can make it easier for them to do their jobs.

This self-service method can also support your drive toward a more agile and expedited development process. Data scientists do not have to spend time issuing help tickets or requests for new solutions, which can slow things down; they simply pick the tools they need, when they need them, enabling them to deliver their findings more quickly. This can also make operations managers’ lives easier, too, as they will not be continually responding to queries from their data science teammates, yet will still have complete visibility into the tools they are using.

Lean into the Hybrid Cloud

To complete the collaborative picture, teams should use a modern application development platform that enables them to learn fast, fail, and adjust together in developing and deploying for the hybrid cloud. An ideal platform should be based on containers and feature Kubernetes-integrated DevOps capabilities. Such a platform can enable teams to work together to quickly deploy and scale their solutions, more easily create new applications, and accelerate development and deployment times.

In this type of environment, different teams can work separately, yet still pool their findings into a common platform for more complete data analysis. For example, teams can work concurrently on different pods, in parallel and isolated within the same namespace, and have their data sets be pooled together into a central and common repository. That way, teams can still work independently while achieving the desired collective result.

There are other benefits to a hybrid cloud approach, including the ability to deploy on-premise for better security and edge deployments requiring reduced latency. But perhaps the biggest benefit is greater consistency. All teams can come together on a unified and common platform to develop, test, and deploy applications across public and private clouds.