Reading Time: 3 minutesIntroduction
As enterprises across the globe step up AI/ML adoption rates, there is an increasing need to have mature processes for smooth AI/ML operations. There is a need for enterprises to clearly define the roles and responsibilities for all stakeholders in the AI/ML operation lifecycle. It is very common for enterprises to have some level of ambiguity regarding the roles and responsibilities of the various stakeholders in the ecosystem. The most common roles in the AI/ML ecosystem are the Data Scientist, Model Validator, Machine Learning Engineer, MLOps engineer, etc. In this blog, we demystify a Machine Learning Engineer’s roles and responsibilities and how they differ for an MLOps persona.
We will cover the following topics in this article:
Engineering vs operations in general
- What is ML engineering? What are the roles and responsibilities of an ML Engineer?
- What is MLOps? What are the roles and responsibilities of the MLOps persona?
- What are the challenges faced by an ML Engineer?
- What are the challenges faced by the MLOps persona?
- How does the ML Engineer compare with the MLOps persona?
- How does Fosfor(Refract) help the ML Engineer?
- How does Fosfor(Refract) helps the MLOps persona?
Engineering vs operations in general
For any product or service to succeed in the market, the offering enterprise should have a strong capability in engineering and operations.
The following is a brief comparison of focus areas for both these functions:
Focuses on innovation. Needs strong technical skills.Focuses on automation. Needs strong automation skills.
| Engineering |
Operations |
| Focuses on building products/services that are highly scalable and highly performant. |
Focuses on delivering products/services in production and ensuring service quality is always maintained. |
| Focuses on providing permanent fixes in response to any incident/ bug. |
Focuses on ensuring product/service is up and running. |
| Not an end-user-facing role. |
End-user-facing role that requires to have strong communication skills. |
What is ML engineering? What are the roles and responsibilities of an ML Engineer?
A Machine Learning engineer (ML engineer) is a stakeholder in the ML lifecycle who works on the research, build, and design of self-running Artificial Intelligence (AI) systems for predictive modeling. An ML Engineer’s primary goals include creating machine learning models and retraining systems when needed. Although responsibilities may differ depending on the organization, some typical duties for this role include:
- Building ML model training pipelines.
- Building ML inference pipelines.
- Integrating models with external applications/API gateways.
- Building CI/CD pipelines for deploying the models to higher environments.
- Controlling the model versions in the development environment.
- Ensuring model robustness in terms of scalability and performance.
Challenges faced by an ML Engineer:
- ML Engineers spend a lot of time building training pipelines and inferencing pipelines. After they write some script or use Python inbuilt packages for building the pipelines, they would have to again write shell script, etc., for automating that pipeline, which is not the best use of their time.
- ML Engineers spend a lot of time packaging all the components of the model for shipping to higher environments.
- ML Engineers spend a lot of time creating APIs for the model, which is not an efficient use of their time.
- ML Engineers need to generate synthetic data for training in case of an imbalanced dataset.
What is MLOPS? What are the roles and responsibilities of the MLOps persona?
MLOps is an integral part of Machine Learning that deploys and maintains ML models in production reliably and efficiently. The MLOps persona seeks to increase automation and improve the quality of production models while also focusing on business and regulatory requirements.
The following are some of the roles and responsibilities of the MLOps persona:
- Deploy the models from QA to the production environment.
- Control model versioning in the production environment.
- Monitor the model in production for feature drift, performance drift, prediction drift, and label drift.
- Monitor the model in production for bias.
- Monitor the model service health in production.
- Monitor the model resource consumption in production.
- Ensuring models can scale up and down as and when traffic increases or reduces.
Challenges faced by the MLOps persona:
- The MLOps persona must spend a lot of time calculating feature, performance, label, and prediction drifts.
- The MLOps persona does not get alerts regarding the performance degradation of the model.
- The MLOps persona will have challenges getting the details of the resource consumption in real-time.
- The MLOps persona will have difficulties managing version controls of the models in production.
Comparison between the ML Engineer and the MLOps persona
| ML Engineer |
MLOps |
| Works closely with Data Scientists and the Model Validation team. |
Works closely with the business owners of the model. |
| Builds training pipelines and inferencing pipelines. |
Automates training pipelines and inferencing pipelines in production. |
| Builds CI/CD pipelines for moving the code to a higher environment. |
Utilizes CI/CD pipelines for deploying the models in production. |
| Model validation for performance factors like accuracy, precision, etc. in development. |
Model monitoring for feature, prediction, label, and performance drifts. |
| Model version control in development /QA. |
Model version control in production. |
| Responsible for building integration with other applications in the development environment. |
Responsible for applying integrations with other components in the production environment. |
| Success is measured by metrics such as the number of defects that occurred in higher environments. |
Success is measured by metrics such as the number of incidents resolved in production. |
How Refract can help the ML Engineer
Refract offers a variety of benefits for the ML engineer. It helps automate various aspects of the machine learning lifecycle, such as data preparation, model training, model deployment, and monitoring. By automating these tasks in an ML pipeline, Refract can also help improve the quality and reliability of the machine learning system by making it easier to debug, test, and optimize the models.
Additionally, Refract can help improve communication and collaboration between team members working on the ML project, such as Data Scientists, ML engineers, and IT engineers. This can lead to better coordination, faster development cycles, and more efficient use of resources.
Refract offers the following features as out-of-the-box capabilities specifically to aid the ML Engineer:
- SDK for data extraction which helps in automation of the process.
- Workflow orchestration for building training pipelines and inferencing pipelines.
- Model version control.
- Build-time metrics for validating the models’ performance.
- Model registration and model deployment.
- Model API.
- Workflow for bulk scoring.
- Scheduler for scheduling based on time or event trigger.
How Refract can help the MLOps persona
Refract can help automate the process of building, testing, and deploying models, making it easier to manage large numbers of models and track their performance over time. Refract offers the following features as out-of-the-box capabilities specifically to aid the MLOpspersona:
- Models developed on other platforms can be deployed in Refract for monitoring.
- Automated alerts based on threshold values for feature, performance, prediction, and label drifts.
- Automated alerts on successful completion or failure of a scheduled job.
- Automated alerts on a service outage.
- Resource utilization metrics for the model over a period.
- Build time metrics for validating model performance.
- Model registration and model deployment.
- Model API.
- Workflow for bulk scoring.
- Scheduler for scheduling based on time or event trigger.
Conclusion
The roles of a Machine Learning Engineer (ML Engineer) and the MLOps persona are essential components of the AI/ML ecosystem, each contributing distinct responsibilities in the development and maintenance of machine learning models. While ML Engineers are primarily focused on model creation and training, MLOps personnel take charge of deploying models in production and ensuring their reliability.
Both roles face unique challenges, with ML Engineers grappling with time-consuming tasks like pipeline development and API creation, and MLOps personas dealing with the complexities of monitoring, drift detection, and resource management in production environments. Understanding these distinctions is vital for organizations to effectively streamline their AI/ML operations.
Refract offers valuable solutions to address these challenges, aiding ML Engineers with automation, version control, and performance metrics, while also empowering MLOps professionals with comprehensive model monitoring and automated alerts. With the right tools and a clear understanding of their roles, both ML Engineers and MLOps personnel can contribute to the successful deployment and maintenance of machine learning models in today’s AI-driven landscape.
