Machine learning has become fundamental to many software development projects. This means software engineers need to work with data scientists, adding complexity to their projects.
Machine Learning Ops, or MLOps, integrates the core principles of DevOps with machine learning. This brings the DevOps concepts of continuous integration, observability and high software quality practices together with the world of data scientists and applied AI engineers, to ensure that machine learning solutions are delivered in a reliable and sustainable form into an organisation's production environment.
Although MLOps is an emerging practice, there are powerful tools that support businesses as they embark on software engineering projects that leverage machine learning. Azure Data Services and Azure Machine Learning provide a solid foundation of scalable, cost-effective compute and storage. These are backed with source code collaboration tools, like GitHub, and automation of machine learning model performance monitoring and retraining processes. This is all delivered through a platform that is accessible from anywhere, securely.
In today’s world, teams need to work remotely. As well as physical distance, we can add cultural diversity and time zones to the complexity that needs to be managed. New collaboration tools allow teams to not only work together on code, but also across the exploratory data analysis and experimentation processes associated with machine learning.
Companies are often faced with systems that are inflexible, time-consuming to update, hard to test and difficult to scale. These challenges result in it being hard to innovate and adapt. Adopting DevOps practices and cloud-native development will help build an innovative culture that fosters better outcomes for businesses and attracts new talent to software development teams
When Insight moved from the office to working from home during the pandemic, they didn’t have to sacrifice their focus on concepts such as continuous integration, continuous deployment and infrastructure as code – which are part of every solution the company develops.
Today’s business world is one where threat actors are on the lookout for vulnerabilities. These platforms support strong governance and robust security to ensure the solutions developed within well thought out DevOps and MLOps processes, delivered by a remote team, aren’t just functionally sound but protect important corporate assets.
By allowing teams to work together in this way, through a central platform that supports information sharing and teamwork, technical issues are brought to the forefront quickly so the team can work together to resolve them. Integration with Microsoft Teams allows colleagues to readily share information and discuss different approaches to problem solving. Virtual whiteboards and dedicated messaging groups have replaced meeting rooms and daily scrums with the ability to easily link pull requests for software code to libraries in GitHub which is integrated into Teams.
Other tools, such as Visual Studio Live Share, allowed developers to work from their own instance of Visual Studio while working on the same piece of code. Developers can highlight and make suggestions, working together just as they would if they were sitting next to each other in an office even when separated by thousands of kilometres.
Companies that successfully integrate DevOps and MLOps don’t just focus on tools. There also needs to be a program to help people transition from traditional ways of working to delivering business success in the post-pandemic world. While many people have transitioned to working from home, software development projects require a high degree of collaboration. And machine learning adds further complexity to this work.
It is critical, for the success of these projects, that everyone is familiar with how the tools are used and that teams are supported as they become accustomed to collaborating remotely using new tools.
The DevOps movement has delivered great value to businesses over recent years. The ability to deliver software faster than ever before, taking advantage of cloud technologies has allowed companies, larger and small, to adapt to changing market conditions faster than ever before. And the emergence and democratisation of machine learning has given companies many new opportunities and capabilities.
MLOps brings these two important and powerful disciplines together. With the right tools and infrastructure, and support for the teams delivering machine learning solutions to the business, it’s possible to create powerful software while teams are working remotely.
Making the move to adopting MLOps and DevOps can seem daunting. Working with a reliable and experienced partner, like Insight, can make the journey smoother. Rather than reinventing the wheel, Insight’s experience and expertise optimises the move to adopting DevOps and MLops practices in your organisation.
Watch a short video on how Azure services support MLOps practice: