What are roles and responsibilities of MLOps engineers?
The use of machine learning models by development/operations (DevOps) teams is known as machine learning operations (MLOps). MLOps combines Machine Learning, DevOps, and data engineering into a set of practices. MLOps aims to reliably and efficiently deploy and maintain machine learning systems in production. Machine Learning, DevOps, and Data Engineering come together in MLOps. An MLOps Engineer is a traditional Software Engineer who has specialized in the deployment and production aspects of the overall Data Science process.
Who Needs MLOps?
The short answer is that MLOps is required by everyone.
The market has matured to the point where machine learning models must now demonstrate and prove their worth. In order to eliminate business risk, they must monitor for performance and accuracy. A scalable way to manage production machine learning is the key to achieving this.
Businesses can expect new levels of competitiveness, intelligence, and automation from AI-based applications. Despite increasing investments in data science talent and technologies, only a small percentage of businesses actually use AI in production. Companies cannot generate measurable value from AI without the ability to automate the deployment of machine learning models in production, as well as reliable ways to monitor, manage, and govern them.
Key Skills Needed To Be An MLOps Engineer
- The demands for good programming knowledge, hands-on experience with ML frameworks, libraries, agile environments and deploying machine learning solutions using DevOps principles is quite high.
- A combination of machine learning, data engineering, and DevOps practices is required in this field.
- Machine learning is heavily reliant on data, so an experienced MLOps engineer should be well-versed in data structures, data modelling, and database management systems.
- DevOps engineers should always collaborate with Quality Assurance (QA) teams and be aware of the testing history throughout the CI/CD cycle. Understanding how your code is tested and maintained requires an understanding of the framework/environments led by QA.
- Understand the tools in the pipeline that serve different purposes, such as Continuous Integration servers, Configuration management, Deployment automation, Containers, Infrastructure Orchestration, Monitoring and Analytics, Testing and Cloud Quality tools, and network protocols.
- MLOps is based on the existing DevOps discipline. Knowing how to automate the entire DevOps pipeline, including app performance monitoring, infrastructure settings, and configurations, is a requirement.
- Model validation, model training, and other aspects of evaluating an ML system are in addition to traditional code tests like unit and integration testing.
Roles and Responsibilities of MLOps engineers
MLOps refers to a set of procedures for deploying and maintaining machine learning models in the field. MLOps, in layman’s terms, refers to everything that happens after the model is built. A model is ready for end-use once it has been trained and evaluated. It can then make predictions based on newly entered user data.
- Deployment and operationalization of MLOps, with a focus on:
- Optimization of model hyperparameters
- Evaluation and explicability of models
- Automated retraining and model training
- Model onboarding, operations, and decommissioning workflows.
- Version control and governance for models
- Data archiving and version control
- Monitoring the model and its drift
2. To measure and improve services, create and use benchmarks, metrics, and monitoring.
3. Providing best practises and running proof-of-concepts for automated and efficient model operations on a large scale.
4. Creating and maintaining scalable MLOps frameworks to support client-specific models.
5. As the sales team’s MLOps expert, providing technical design solutions to support RFPs.