Machine Learning Operations(MLOps), is a multidisciplinary field that merges software engineering with data science, aiming to streamline and automate the lifecycle of machine learning (ML) models. It focuses on the practical implementation of ML models, bridging the gap between experimental models and production-level solutions. This field has become increasingly relevant as more organizations integrate AI and ML into their operational processes.

Inspired and developed from DevOps, MLOps and DevOps share common principles, such as automation, continuous integration (CI), and continuous delivery (CD), their focuses and challenges are distinct. Both MLOps and DevOps stem from the need to streamline processes:

  1. Automation: Both methodologies emphasize automating processes to reduce human error and accelerate workflows.
  2. CI/CD(Continuous Integration and Delivery): CI/CD is central to both DevOps and MLOps. In DevOps, it pertains to software development, while in MLOps, it extends to the integration and deployment of machine learning models.
  3. Collaboration and Communication: Enhancing collaboration across teams—developers, operations, and now data scientists—is a key element of both approaches.

Nevertheless, MLOps and DevOps focus on different aspects of the technology landscape; DevOps focuses on software development processes, while MLOps concentrates on the end-to-end lifecycle of machine learning models, from data collection and model training to deployment and monitoring in production.

Now, we are going to give more details on MLOps and how could it benefit the machine learning algorithms application in real scenarios.

As mentioned early, Automation of series procedures is important in MLOps. MLOps emphasizes the automation of various stages in the machine learning lifecycle, including data collection, data pre-processing, model training, testing, deployment, serving, and monitoring. Automation helps reduce manual errors and speeds up processes.

Under the principle of automation, MLOps introduces Reproducibility and Version Control to ensure any machine learning tasks can be reproduced at any time. Rigorous version control for data, code and models can be implemented through various tools such at Git, staging, or snapshot.

MLOps integrates CI/CD pipelines similar to those in software development, enabling continuous testing, integration, and deployment of machine learning models. This approach helps in rapidly iterating and refining models based on continuous feedback.

Monitoring is vital in MLOps to track the performance of models in production, detect drifts in data or degradation in model performance, and trigger retraining processes as needed. Governance includes ensuring that models comply with ethical guidelines and regulatory requirements. For instance, an event-based trigger can be registered to monitor the performance of the operation; when the criteria are met, e.g., a degradation event is reported, corresponding actions will be triggered to address the performance issue.

MLOps also fosters better collaboration between data scientists, ML engineers, and operations teams. This synergy is essential for translating models from prototypes to production-ready solutions effectively.

MLOps practices help organizations scale their machine learning efforts from a few models to potentially thousands in production, handling infrastructure and dependencies systematically. When training a machine learning/deep learning model on a large dataset, it can be necessary to distribute the training task into distributed computing/storage nodes. However, manually scaling can be tedious and error-prone. For example, TensorFlow provides Distributed Strategy API to simplify distributed training, which is a good practice of MLOps.

Above includes an overview of MLOps. I might give more introduction on MLOps in my future posts. From the aspect of a developer, why not get some professional, practical and structural training from a big tech? Intel Certified Developer - MLOps Professional is the convenient way to fullfill that. Simply registering an Intel account, you can have access to the training courses of MLOps as well as a preparing examination section! It also provides 8 hands-on labs and a capstone project to help you practice MLOps ability under a real scenario. But don’t be afraid if you do not have a strong computer science background - those labs and project only require basic coding skills. Furthermore, enrolling this course can bring your more surprise, discover yourself!

The certification exam is administered by Pearson VUE, which means you can schedule an exam in one of their global exam centers. Or, you can even take an online proctored as long as you have a stable network connection and meet other requirements by Pearson VUE! Schedule you MLOps Certification Exam here!.

For more introduction on the course, please refer to Intel’s blog.