In one of our previous articles it was highlighted how DevOps manages the End-to-End application cycle, leveraging agility and automation. CI/CD pipelines, collaboration and transparency, monitoring and automation are part of the list on how DevOps leverages and facilitates agility. What if then we bring those to support ML? That is how MLOps comes to the table and starts making sense!
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| Lego Alike data assembly - Generated with Gemini |
A big tech corporation, or a startup, nowadays will see how it is becoming a requirement to incorporate AI and Machine learning (ML) in their operations. ML components are key parts of the ecosystem, supporting the solutions provided to clients. As a result, DevOps and MLOps have become part of the "secret sauce" for success.
What is MLOps
Just to bring the definition of what you probably know (or put together based on the above) MLOps focuses on the life-cycle management of machine learning models. It combines machine learning with traditional data engineering and DevOps practices for building, deploying, and maintaining ML models.
MLOps makes sure that models are:
- Reliable.
- Scalable.
- Efficient.
To accomplish this, a pipeline is established which includes tasks such as:
- Data collection.
- Model training.
- Deployment of the models.
- Monitoring of the models.
- Updates and enhancements.
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| MLOps |
How do you get started for setting an MLOps Practice?
You need:
- A framework.
- A team (multi-disciplinary) with the know-how.
- Technology.
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A framework:
- Establish Clear Goals. The need of defining the business problems (use cases) that ML is meant to solve for your company. You need to be able to measure success, therefore you need to also define the OKRs and KPIs.
- A culture of collaboration. In discussions about DevOps, MLOps, and agility, collaboration is the “glue” that serves as an integral component that binds these concepts together.
- Ensure model governance and security. Proper audits to mitigate bias in the data, data processing consistency, and to ensure security (sensitive vs confidential vs PII vs public and accessible).
- Model development pipeline. Having the right toolkit to set the proper automate data processing. CI/CD (Continuous Integration and Continuous Deployment) will keep the rhythm of the pipeline, which includes the unceasing iterative and improvement of the models’ refinement and its data.
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| MLOps building blocks and enablement |
Who do you need?
Building a diverse, multidisciplinary team is crucial for the success of AI and ML projects, as it ensures the right combination of skills to achieve business objectives effectively. In MLOps, various roles play a key part in shaping and maintaining AI practices, though the specific requirements and team size may influence their composition. While these roles are not exhaustive, assembling a team with complementary expertise fosters innovation and enhances operational efficiency, ultimately driving successful AI initiatives that will improve the business outcomes and will contribute to the effectiveness and collaboration of the different business units and teams within the organization, such as data scientists, software engineers, and IT personnel, ensuring that ML models are continuously improved and updated.
Here are some of those roles that you would be needing:
- AI Architects. They are responsible for designing the system’s architecture and ensuring that it is scalable and efficient.
- Data Scientists. They are responsible for the models and algorithms.
- ML Engineers. They are responsible for developing and deploying machine learning models and algorithms.
- Data Engineers. They are responsible for designing, building, and maintaining the data infrastructure.
- Software Engineers. They act as the bridge between machine learning models and production systems, ensuring smooth integration and long-term reliability. They do so by developing and maintaining the software components.
- Product Designers. They lead the UI and UX.
- Domain Experts. They provide domain-specific knowledge and expertise in order for the crew mentioned above to come together, take the requirements and design the solution.
- AI Ethics and SMEs. This part of the team are the ones making sure the AI system has the guardrails in place, keeping it ethical and socially responsible.
Difference between MLOps and AIOps
AIOps and MLOps are two distinct yet complementary paradigms that both leverage Artificial Intelligence (AI) and Machine Learning (ML).
MLOps is a set of practices and framework designed to streamline and automate the lifecycle of machine learning models; making sure they run smoothly, they are scalable, and reliably in production environments.
While AIOps, on the other hand, leverages AI-driven insights to provide solutions at a faster pace. AIOps, focuses on applying AI/ML technologies to enhance and automate IT operations, performing tasks such as anomaly detection, event correlation, predictive analytics, risk management, automated root cause analysis, etc.
Assembling an MLOps Practice - Part 2
In part 2 of the article, we move forward by exploring part of the technical stack that could be an option for implementing MLOps...
