MLOps, also known as DevOps for Machine Learning, is a practice that combines software engineering and data science to automate the process of building, testing, and deploying machine learning models.
It involves various disciplines, including machine learning, data science, software engineering, cloud engineering, and DevOps. This practice is invaluable for Data Science teams, as it helps streamline the development process, minimizes friction, and ensures because of the best practices that models are deployed quickly and efficiently.
"Any organization starting to explore Machine Learning should also consider organizing and managing it with MLOps. As data scientists begin to investigate a problem, they immediately start creating experiments, models, and datasets," says Douglas Blank, Head of Research at Comet ML. "It is much better to have a MLOps plan and implementation in place from the very beginning. Otherwise, your team now faces two problems: the original one they wanted to solve and the management of the solution. Managing ML teams is, in some ways, harder than creating ML models themselves."
MLOps for Data Science Teams
Many companies now recognize the importance of actively engaging in MLOps to ensure that all stakeholders are on the same page and working towards the same goal. It is generally seen as part of the data science teams and not as a separate entity.
“The specific disciplines that should be part of MLOps will depend on the particular needs and goals of the organization, as well as the type of machine learning project being undertaken,” says Dominic Holt, CTO of Harpoon — the No Code Kubernetes startup. "Ultimately, the goal of MLOps is to ensure that machine learning models can be developed and deployed in a way that is efficient, effective, and scalable."
While MLOps can help organizations gain a competitive edge, it requires specialized skills, integration into existing systems, and resources to support it. To overcome these challenges, business leaders must take initiatives to invest in training and development programs to ensure their teams have the critical skills and functional knowledge to implement MLOps.
MLOps practice is complex, requiring new processes and procedures for efficient collaboration between teams to enable rapid issue identification, solution development, and cost optimization.
It is a challenging endeavor that requires business leaders to bring together professionals from different disciplines and backgrounds to work together effectively.
"When I was a researcher, there wasn't such a thing as MLOps. We would keep track of results in very ad hoc ways, such as creating log files and spreadsheets,” Blank shares. “At that time, I was a computer science professor, and very interested in teaching not just tools, but in the core ideas of ML. When I retired from being a professor, I joined Comet and we began inventing the field. We had to create the concepts and workflow that has become known as MLOps. But the most important tools are still those that give insight into the problem and solution. Of course, different people in an organization want insights into different aspects of the problem and solution. I've found that is very difficult to create a one-size-fits-all tool. The best tools allow users to customize them in novel ways — ways that we could never have imagined."
With the demands for intelligent process automation more vociferous than ever before, more organizations are focusing on MLOps practice. "Success for a MLOps team is a moving target,” Blank affirmed. “The field is ever-changing, with new techniques, tools, visualizations, and ideas being created every month. The best we can do is stay as near the cutting edge as possible. That requires a flexible foundation and constantly being able to think outside the box... and the MLOps."
Closing Skills Gap
Technology choices for data science workflows largely depend on the type and amount of data available, and open source technologies also provide a wide range of options for MLOps engineering and data science teams to leverage.
To ensure that all stakeholders can effectively contribute, they must be informed about MLOps workflows, which may involve providing instruction on specific tools and technologies, as well as teaching best practices and methodologies. "The education and adoption of new processes and tools for MLOps enables to develop and deploy machine learning models in a way that is efficient, effective, and scalable," says Holt.
MLOps is essential for successful data science practice, as it enables teams to collaborate and leverage the latest technologies and tools to create the best ML models. By providing workshops, seminars, and training programs, leaders can ensure that everyone is up-to-date with the latest ML/AI technologies and tools, allowing teams to work together to create the most effective ML models.