Who is MLOps Engineer?

What is MLOps?

Machine Learning Operations (MLOps) is an acronym for Machine Learning Operations. MLOps is a basic component of Machine Learning engineering that focuses on optimizing the process of deploying machine learning models and maintaining and monitoring them. MLOps is a team effort that frequently includes data scientists, DevOps engineers, and IT. MLOps aims to boost automation and improve the quality of production models while simultaneously concentrating on business and regulatory needs, similar to DevOps and DataOps methodologies. MLOps began as a set of best practices, but it is gradually becoming a stand-alone solution to managing the ML lifecycle.

Who is MLOps Engineer?

The Machine Learning Operations Engineer, also known as an MLOps Engineer, is a critical and valuable addition to your Data Science team. If you're a current Software Engineer who wants to work cross-functionally but specifically with Machine Learning algorithms, or if you're a current Data Scientist who understands how the algorithms work but wants to focus more on Software Engineering, Data Engineering, and model deployment, you might want to consider switching to this role. When working as an MLOps Engineer, you may be given a Data Science model created by a Data Scientist for the first time. Aside from machine learning skills, you'll also require a solid understanding of DevOps. DevOps is a career that combines the responsibilities of software engineers with those of operations teams in order to automate workflows.

Why do we need MLOps?

It's difficult to transform machine learning into a profitable venture. Many complicated components make up the machine learning lifecycle, including data import, data preparation, model training, model tuning, model deployment, model monitoring, explainability, and so on. It also necessitates cross-team communication and hand-offs, from Data Engineering to Data Science to Machine Learning Engineering. Naturally, keeping all of these processes synchronized and running in unison necessitates a high level of operational discipline. MLOps refers to the machine learning lifecycle's experimentation, iteration, and continual improvement. Enterprises aspire to achieve a number of goals using MLOps systems and successfully adopting ML across the company, including −

  • Reproducibility of models and predictions

  • Diagnostics

  • Deployment and automation

  • Scalability

  • Governance and regulatory compliance

  • Collaborations

  • Business Uses

  • Monitoring and management

MLOps, for example, is a standard practice that considers all of the aforementioned factors, which can help businesses streamline processes and avoid problems during deployment.

Skills needed to be an MLOps Engineer

  • Good programming skills, hands-on experience with machine learning frameworks, libraries, agile settings, and implementing machine learning solutions utilizing DevOps concepts are in high demand.

  • This industry necessitates a mix of machine learning, data engineering, and DevOps approaches.

  • Throughout the CI/CD cycle, DevOps engineers should always engage with Quality Assurance (QA) teams and be aware of the testing history. A grasp of the framework/environments guided by QA is required to understand how your code is tested and maintained.

  • Because machine learning relies so largely on data, an experienced MLOps engineer should be familiar with data structures, data modeling, and database management systems.

  • MLOps is a branch of the DevOps field. It is essential to understand how to automate the full DevOps pipeline, including app performance monitoring, infrastructure settings, and configurations.

Roles and Responsibilities of MLOps Engineer

An MLOps Engineer is supposed to handle the below-mentioned responsibilities −

  • Responsible for optimizing hyper parameters in a model, and evaluating those models. These engineers focus on Workflow for onboarding, operations, and decommissioning are all modeled, Model version control and governance, Version control and data archiving, Keeping an eye on the model and how it's changing.

  • Create and apply standards, metrics, and monitoring to gauge and enhance service quality.

  • For automated and efficient model operations on a wide scale, provides the best options and also conducts proofs-of-concept.

  • Develops and maintains scalable MLOps frameworks to accommodate client-specific models.

  • As the MLOps expert for the sales team, they support RFPs with technical design solutions.

Updated on: 26-Aug-2022


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