The ability to train ML and deep learning models is essential; however, developing a practical production model demands highly skilled professionals. While the industry has varying metrics on what being a good MLOps engineer is, here we have curated essential skills a sound MLOps engineer should have.
“Typically, they need to have strong programming and ML expertise, experience with ML frameworks like scikit-learn, Tensorflow, Keras and others. The role needs to focus more on creating pipelines, scaling ML, and having experience in ML focusing on taking models to production. Moreover, they should help organisations put architecture, systems, best practices, governance in place to ensure smooth deployment of models in production,” said Hamsa Buvaraghan, Smart Analytics & AI Platform Solutions Manager.
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Getting mastery of
- Come up with good programming skills. The demands for hands-on experience with ML frameworks, libraries, agile environments, and deploying machine learning solutions using DevOps principles is quite high.
- Machine learning relies heavily on data; a skilled MLOps engineer should know data structures, data modelling, and database management systems inside and out.
- The field calls for a combined set of ML, data engineering, and DevOps practices.
- Understand the tools serving different purposes in the pipeline, including Continuous Integration servers, Configuration management, Deployment automation, Containers, Infrastructure Orchestration, Monitoring and analytics, Testing and Cloud Quality tools, as well as network protocols.
- DevOps engineers should work with Quality Assurance (QA) teams at all times. Understanding the testing activities, knowing the history of testing throughout the CI/CD cycle, and understanding frameworks/environments led by QA.
- MLOps is modelled on the existing discipline of DevOps. It’s a necessity to know how to automate the entire DevOps pipeline, including app performance monitoring, infrastructure settings, and configurations.
- In addition to traditional code tests like unit and integration testing, evaluating an ML system entails model validation, model training, and so forth.
Image Credits: NVIDIA
Successful deployment of ML models in production remains heavily dependent on two critical factors, code and data. Understanding the relationship between the two is vital. Data originates from the infinite entropy source known as “the real world,” whereas code is carefully developed in a controlled development environment — real-world data witnesses constant change without an engineer controlling it. Bridging the gap between data and code remains the foremost challenge for an ML process.
- It starts with identifying an ML problem and selecting appropriate input data. The next step that follows is data preparation and processing. Tasks including cleaning (imputations, checking for outliers, formatting, etc.), feature engineering, and selecting features contributing to the output remains the key. Design and code a complete pipeline.
- Train the ML model for better reproducibility by versioning models and data. Open-source tools like Data Version Control (DVC) and Continuous Machine Learning (CML) help add version control to the components of ML systems.
- Understanding the system’s requirements, such as triggers, computing needs, and parameters, is helpful. Additionally, choosing appropriate cloud architecture, constructing training and testing pipelines, and data validation need to be taken care of.
- Deploying an ML model to the production system, either static or dynamic deployment.
A degree from institutes or universities in AI/ML, Computer Science, Data Science, and Cognitive Computing is good to have. Reputable organisations including L&T, Siemens, and MPL demand MLOps engineers with experience ranging from two to eight years, depending on the required level and expertise. In the end, ML applications are here to serve a business need. Therefore, success metrics or key performance indicators (KPIs) for the business application should be tracked and correlated with the introduction of and subsequent optimisations to the ML application. This correlation provides visibility for all stakeholders, thereby ensuring that ML investments are generating adequate returns.
MLOps provides a road map for individuals, small teams, and even businesses to fulfil their objectives regardless of constraints, such as sensitive data, limited resources, or a limited budget.