The world first witnessed an increase in the number of physical machines during the Industrial Revolution. This steady rise compelled organizations to create a system, which was done by building factories, assembly lines, and other elements of automated manufacturing. Soon after, the tech boom witnessed Agile systems coming into the picture to automate processes for creating. This was done by operationalizing the product life cycle, which helped promote continuous innovation by clearing out waste. Following this, we saw DevOps being introduced to optimize the production lifecycle even further by introducing big data. Of course, all of these processes have brought us to the present world, where we are turning our attention to machine learning insights. This brings us to MLOps. Machine Learning Operations, or MLOps, is essentially a framework that focuses on the collaboration between data scientists and the operations unit within an organization. The framework is designed to reduce errors, minimize waste, further improve upon automation, and produce more valuable insights with the help of machine learning. MLOps follows a path similar to that of DevOps. While DevOps focuses on shortening the product life cycle by creating better products each time, MLOps drives insights that can be put into better use immediately. The reason why MLOps is considered to integrate the best of both worlds is due to its vested role in improving the organizational operations. MLOps promotes data scientists to look at their roles with organizational interest, which helps bring about clarity and measurable benchmarks. DevOps vs. MLOps: What kind of relationship do MLOps and DevOps have? Many would believe that since Machine Learning is a software engineering field, the principles of DevOps can be applied to it. While this holds true to some extent, there are a few key differences between the two. DevOps is essentially a practice that involves building and operating software systems on a large-scale. DevOps works with two main concepts – Continuous Integration and Continuous Delivery. MLOps involves ML, a software system, and the concepts of DevOps can be applied to build ML systems at scale. As compared to DevOps, MLOps is more experimental in nature. This new framework requires data scientists to try a variety of features, parameters, and models. With these different combinations, these experts try to manage the code base and produce compatible results. While working with ML, the team will typically include data scientists and ML researchers who can help develop models, understand exploratory data analysis, and experimentation. Although they can understand the system better, they cannot offer production-class services like software engineers are capable of doing. Testing ML is comparatively more complex. The process would involve data validation, model validation, and trained model quality evaluation, along with unit and integration tests. ML deployment is also a complex process as it requires you to position a multi-step pipeline, allowing you to automate the process of retraining and deploying models. The constantly evolving data profiles in ML may result in reduced performance if paired with suboptimal coding. Models are capable of decaying in more ways as compared to other software systems, which requires experts to track summary statistics and monitor performance. The similarities between ML and other software systems lie in the continuous integration of source control, integration testing, unit tests, as well as continuous delivery. But, in ML, continuous integration moves beyond just code and components and involves data: testing and validating data and data schemas. Continuous Delivery, too, requires an ML training pipeline that uses automation to deploy a model prediction service. What are the Benefits of MLOps? The operationalization of data helps to gain insight and use this knowledge to develop actionable business value. Here’s how the addition of MLOps can help organizations gain more value: MLOps helps to bridge the gap between the business knowledge of the operations unit in the company and the studies conducted by the data science team. MLOps seeks to leverage the benefits of both spheres and create more valuable ML. While data scientists may be working hard to gain more insight, all efforts could prove futile if your organization runs into trouble with regulatory bodies. MLOps mitigates this risk by paving the way for the operations unit to apply new regulations and best practices, while the data team focuses on development. MLOps helps increase investments in present machine learning and data science tools and technology to a much bigger extent. This helps to build a system of maintaining records between different teams and projects. How can an organization introduce MLOps? Here are some basic points to consider before bringing MLOps into the organization model: Benchmarks : The organization’s key performance indicators should be concise and measurable to bring all members on board. There has to be a steady collaborative effort between data scientists and operations team members to understand their roles and use insights correctly Monitoring : There is a dire need for both units to be monitored through every step of the process. Since ML requires data to be retrained periodically, the organization requires a keen eye to be kept on the process to ensure that everyone is operating within regulations and to be doubly sure that the programs are providing quality information. Compliance : To ensure compliance with regulations at every step of the way, MLOps requires a thorough plan for governance, which helps to ensure that 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 that is used by organizations to automate deployment, scaling, and management for computer applications. As an orchestrator, Kubernetes is used to build scalable distributed systems and is also being leveraged to bring about much-needed flexibility in varied machine learning frameworks for data scientists to work upon. This flexibility extends to the scalability and reiteration required by units that run machine learning systems in products, as well as more control over resource allocation required by the operations unit. When applied in machine learning, Kubernetes can significantly ease the process for data scientists and business operators. Typically, data science and deployment paths are different entities. On one side, data scientists build experiments using one set of tools and infrastructure, whereas the development teams recreate the model using different tools and infrastructure. To make the process more cohesive, organizations should look to bring in a combined pipeline in the form of Kubeflow, which uses Kubernetes to train and scale models on a variety of frameworks, without the requirement of any kind of expertise in infrastructure planning. Conclusion Machine learning is definitely the future of data science, and the integration of MLOps into the organizational structure can go a long way in cutting back on errors and building models with more efficiency. MLOps can benefit from the tools used today in DevOps to implement the best practices of CI/CD and production. Kubernetes is a very good fit for Machine Learning. It’s the perfect platform to deploy machine learning models to production, run scheduled jobs, distributed computing, and CI/CD pipelines. Even if you are not a Kubernetes expert platforms like CloudPlex allow you to create a Kubernetes cluster. (on any major cloud provider or on bare metal) for free and in a few minutes. Using the drag-and-drop tool, you can build, upgrade, and destroy clusters without diving into complex YAML and infrastructure configurations. You can start using it today for free here. Asad Faizi Founder CEO , Inc CloudPlex.io asad@cloudplex.

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