MLOps for Data Science teams and responsible leaders

MLOps is a set of strategies and techniques that allow data science teams to quickly and effectively create, deploy, and maintain ML models in production environments.

MLOps for Data Science teams and responsible leaders
MLOps for data science | Source - The Enterprise Project

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 a variety of disciplines, including machine learning, data science, software engineering, cloud engineering, and DevOps. This is an invaluable practice for Data Science teams as it helps to streamline the development process, minimize errors, and ensure that models are deployed quickly and efficiently.

"Any organization starting to look at Machine Learning should also be thinking about how to organize and manage it with MLOps. As data scientists begin to explore a problem, they immediately start creating experiments, models, and data sets. It is much better to have a MLOps plan and implementation in place at the very beginning. Otherwise, your team now has two problems: the one they originally wanted to solve, and now the management of the solution. Managing ML teams is, in some ways, harder than creating ML models themselves," explained Douglas Blank, Head of Research at Comet ML.1

Let's dig a little deeper to take a look — a bird's-eye view — with the lens of quality leaders.

MLOps for data science

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 specific needs and goals of the organization, as well as the type of machine learning project being undertaken. 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," commented Harpoon CTO Dominic Holt.2

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.

Let's gain further insight from industry experts regarding the thought processes prior to implementation.


MLOps Adoption

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.

MLOps for data science
expect it to be messy
"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. 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," Douglas reflected.

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. 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," Douglas specified.

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. "Ultimately, 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," Dominic affirmed.


Open source can help

Technology choices for data science workflows largely depend on the type and amount of data available, and open source technologies provide a wide range of options for MLOps engineering and data science teams to leverage.

— Kubernetes

Kubernetes (a.k.a. K8s) is a powerful container orchestration system to automate deployment, scaling, and management of containerized applications widely used in the world of DevOps. "Kubernetes-native methods can help ensure that an MLOps project is scalable and can handle increasing demand for the machine learning models it deploys," Dominic remarked.

🔥 Kubernetes resources stack for data science, DevOps and cloud engineering.
— Open source training

Open source training can help stakeholders stay up to date with the latest advancements in MLOps, as they can access the latest tools and techniques.

  • Kubernetes certification training is exceedingly helpful for developers, data engineers and DevOps professionals to gain the critical skills necessary to deploy and manage ML models in a Kubernetes environment.
  • The Linux Foundation's vendor-neutral software developer certification programs equip developers with the specialized skills and knowledge profitable for data science practice.
  • Responsible MLOps courses build up data practitioners with specialty skills to optimize the MLOps process, including best practices for deploying, managing, and scaling ML models in a cloud environment.
Notable mention

Comet ML is a machine learning platform that helps data scientists and developers manage their machine learning projects. It provides a suite of tools for tracking experiments, managing datasets, and deploying models with features such as automated hyperparameter optimization, model versioning, and model deployment.

pip install comet-ml
Python project: Comet ML

Closing Note

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.

References/Citations

Recommended read — MLOps overview by Team Comet.
  1. Douglas Blank — Head of research at Comet ML
  2. Dominic Holt — CTO Harpoon.io

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