This article was published as a part of the Data Science Blogathon.
Introduction
The constant rise prompted organizations to create a system that was implemented by building factories, assembly lines, and other elements of automated production. Soon after, the technology boom saw the emergence of agile systems that automate creation processes. This was achieved by operationalizing the product life cycle, which helped drive continuous innovation by eliminating waste.
Of course, all of these processes have brought us to the current world, where we are turning our attention to the insights of machine learning. This brings us to MLOps.
What is MLOps?
Machine Learning Operations, or MLOps, is a framework that focuses on collaboration between data scientists and the operations unit within an organization. The framework is designed to reduce errors, minimize waste, further improve automation, and produce more valuable insights with the help of machine learning. MLOps follows a similar path to DevOps. While DevOps focuses on shortening the product lifecycle by building better products every time, MLOps delivers insights that can be put to better use immediately.
MLOps is considered to integrate the best of both worlds because of its mandated role in improving organizational operations. MLOps encourages data scientists to view their roles with organizational interest, which helps ensure clarity and measurable metrics.
DevOps vs. MLOps: What is the Basic Difference?
Although many would believe that since machine learning is a software engineering discipline, DevOps principles can be applied to it. While this is true to some extent, the two have several key differences. DevOps is a practice involving building and operating software systems at scale.
MLS includes ML, a software system, and DevOps concepts that can be used to build ML systems at scale.
However, ML systems differ from other software systems in several ways –
- Compared to DevOps, MLOps is more experimental. This new framework requires data scientists to experiment with different functions, parameters, and models.
- When working with ML, the team will typically include data scientists and ML researchers who can help with model development, understanding exploratory data analysis, and experimentation. Although they can better understand the system, they cannot offer the production-level services that software engineers are capable of.
- ML testing is comparatively more complex. The process would include data validation, model validation, and trained model quality assessment, along with unit and integration tests.
- Deploying ML is also a complex process because it requires a multi-step pipeline to be in place, allowing you to automate the process of retraining and deploying models.
- Constantly evolving data profiles in ML can result in reduced performance when paired with suboptimal encoding. Models can break down in more ways than other software systems, requiring experts to track aggregate statistics and monitor performance.
Similarities between ML and other software systems are continuous integration, resource management, integration testing, unit tests, and continuous delivery. In ML, however, continuous integration goes beyond code and components to include data: testing and validating data and data schemas. Continuous delivery also requires ML training that uses automation to deploy a model prediction service.
What are the benefits of MLOps?
Operationalizing data helps gain insight and leverage that knowledge to develop actionable business value.
Here’s how adding MLOps can help organizations get more value –
- MLOps helps bridge the gap between the business knowledge of an operating unit in a company and the studies performed by the data science team. MLS seeks to take advantage of both spheres to create more valuable ML.
- While data scientists may be working hard to gain better insight, all efforts can prove futile if your organization runs into trouble with regulators.
- MLS helps drive investment in current machine learning and data science tools and technologies to a much greater extent. This helps build a record-keeping system between different teams and projects.
How can an organization implement MLOps?
Here are some basic points to consider before implementing MLO in an organization model –
Benchmarks – Organizational KPIs should be concise and measurable to engage all members. There must be an ongoing collaboration between data scientists and operations team members to understand their roles and leverage insights.
Monitoring – Both units must be monitored at every step of the process. Because ML requires data to be regularly retrained, the organization requires careful monitoring of the process to ensure that everyone is working in compliance and that the programs provide quality information.
Compliance – To ensure compliance with every step, MLOps requires a thorough management plan to help ensure the programs created are auditable and within the scope of operations.
How does Kubernetes advance MLOps?
Kubernetes is essentially an open-source container orchestration system used by organizations to automate desktop applications’ deployment, scaling, and management. As an orchestrator, Kubernetes is used to build scalable distributed systems and is also used to bring much-needed flexibility to the various machine learning frameworks that data scientists can work on.
This flexibility extends to the scalability and repeatability required by the units that run the machine learning systems in the products and the greater control over resource allocation required by the operational unit. Kubernetes can greatly facilitate the process for data scientists and business operators when used in machine learning.
Data science and deployment paths are usually different entities. On the one hand, data scientists create experiments using one set of tools and infrastructure, while development teams recreate the model using different tools and infrastructure. To make the process more cohesive, organizations should look to implement a combined pipeline in the form of Kubeflow, which uses Kubernetes to train and scale models on multiple frameworks without requiring any expertise in infrastructure planning.
Conclusion
Machine learning is the future of data science, and integrating MLOps into the organizational structure can go a long way in reducing errors and building models with greater efficiency. MLOps can benefit from the tools used in DevOps today to implement CI/CD and production best practices. Kubernetes is very well-suited for machine learning.
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- MLOps is considered to integrate the best of both worlds in its mandated role in improving organizational operations. MLOps encourages data scientists to view their roles with organizational interest, which helps ensure clarity and measurable metrics.
- It’s the perfect platform for deploying machine learning models to production, running scheduled jobs, distributed computing, and CI/CD pipelines. Even if you’re not a Kubernetes expert, platforms like CloudPlex allow you to create a Kubernetes cluster (on any major cloud provider or bare metal) for free and in minutes.
- MLOps helps bridge the gap between the business knowledge of an operating unit in a company and the studies performed by the data science team. MLS seeks to take advantage of both spheres to create more valuable ML.
- ML testing is comparatively more complex. The process would include data validation, model validation, and trained model quality assessment, along with unit and integration tests.
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