Machine learning operations, or MLOps for short, is a key aspect of machine learning (ML) engineering that focuses on simplifying and accelerating the process of delivering ML models to production and maintaining and monitoring them. MLOps involves collaboration between different teams including data scientists, DevOps engineers, IT specialists and others.
MLOps can help organizations create and improve the quality of their AI and machine learning solutions. Adopting MLOps allows machine learning engineers and data scientists to collaborate to improve model performance by implementing continuous integration and continuous deployment (CI/CD) practices. It accelerates the ML model development process by incorporating the appropriate monitoring, governance, and validation of ML models.
What Is DevOps?
DevOps combines the concepts of development and operations to describe a collaborative approach to performing the tasks usually associated with separate application development and IT operations teams. DevOps, in its broadest sense, is a philosophy that encourages improved communication and cooperation between these (and other) teams within an organization.
In its narrowest sense, DevOps refers to adopting practices that enable the deployment and maintenance of iterative application development, automation, and programmable infrastructure. It also includes changes in the workplace culture, like trust-building and bonding between developers, system administrators and other team members. DevOps aligns technology with business objectives and can transform the software delivery chain, job functions, services, tools and best practices.
MLOps Vs. DevOps: Key Differences
Here are some of the main differences between MLOps and traditional DevOps.
The concept of development refers to different things in each model, with a slightly different CI/CD pipeline.
- Usually, the code creates an interface or application.
- The code is wrapped into an executable or artifact before being deployed and tested with a set of checks.
- Ideally, this automated cycle will continue until the final product is ready.
- The code enables the team to build or train machine learning models.
- The output artifacts include serialized files that can receive data inputs to generate inferences.
- Validation involves checking the trained model’s performance based on the test data.
- This cycle should also continue until the model reaches a specified performance threshold.
- Version control typically only tracks changes to code and artifacts.
- There are few metrics to track.
- MLOps pipelines usually have more factors to track. Building and training an ML model involves an iterative experimentation cycle, requiring tracking of various metrics and components for each experiment (essential for later audits).
- Additional components to track include training datasets, model building code and model artifacts.
- Metrics include hyperparameters and model performance indicators, such as error rates.
- DevOps pipelines focus on repeatable processes.
- Teams can mix and match processes without following a specific workflow.
- MLOps pipelines repeatedly apply the same workflows. The common framework across projects helps improve consistency and allows teams to progress faster because they start with familiar processes.
- Project templates offer structure, enabling customization to address the unique requirements of each use case.
- Uses centralized data management to consolidate the organization’s data to accelerate the discovery and training processes. Common approaches to centralization include a single source of truth and data warehouses.
Monitoring is essential for both DevOps and MLOps, but for slightly different reasons.
- Site reliability engineering (SRE) has been trending over the past few years, emphasizing the need for monitoring software from development through to production deployment.
- The software does not degrade in the way an ML model does.
- Machine learning models can degrade quickly, requiring constant monitoring and updating.
- Conditions in the production environment affect the model’s accuracy. After deployment to production, the model starts generating predictions based on new data from the real world. This data is constantly changing and adapting, reducing model performance.
- MLOps ensures that algorithms remain production-ready by incorporating procedures to facilitate continuous monitoring and model retraining.
DevOps and MLOps both rely heavily on cloud technology, but have different operational requirements.
DevOps relies on infrastructure such as:
- Infrastructure-as-code (IaC)
- Build servers
- CI/CD automation tools
MLOps relies on infrastructure such as:
- Deep learning and machine learning frameworks
- Cloud storage for large datasets
- GPUs for deep learning and computationally-intensive ML models
DevOps and MLOps Trends
Here are some of the major trends driving the development of DevOps and MLOps.
A new evolution of the DevOps workflow, GitOps is a new paradigm for controlling and automating infrastructure. A Kubernetes-oriented paradigm enables developers and operations teams to manage Kubernetes clusters and deliver containerized applications using Git. Implementing Git workflows for operations and development teams allows developers to leverage Git pull requests to manage software deployments and infrastructure.
GitOps incorporates existing development tools to manage cloud-native and cluster-based applications with CI/CD. It automatically deploys, monitors, and maintains cloud-native applications using a Git repository as the single source of truth.
GitOps is a way to implement and maintain clusters in Kubernetes. Continuous delivery and deployment allow developers to build, test, and deploy software faster through incremental releases. Kubernetes continuous integration and runtime pipelines must be able to read and write files, update container repositories, and load containers from Git. GitOps helps businesses manage their infrastructure with version control, real-time monitoring, and alerting of configuration changes.
Synthetic data is any information generated artificially instead of collected from real events. Algorithms generate synthetic data for use as a replacement for operational and production test datasets. Synthetic datasets are also useful for validating mathematical models and training machine learning models.
Benefits of synthetic data include:
- Minimizing the constraints associated with using sensitive and regulated data.
- Customizing data to specific requirements and conditions not available in real-world data.
- Generating data for testing software quality and performance for DevOps teams.
Codeless Machine Learning and AI
Machine learning often involves computer code to set up and process model training, but this is not always the case. Codeless machine learning is a programming approach that eliminates the need for ML applications to go through time-consuming processes.
Codeless ML eliminates the need for experts to develop system software. It is also simpler and cheaper to deploy and implement. Using drag-and-drop input during machine learning processes can simplify training efforts in the following ways:
- Evaluating results.
- Dragging and dropping training data.
- Creating predictive reports.
- Using plain-text queries.
Codeless ML gives developers easy access to machine learning applications, but it is no substitute for an advanced, nuanced project. This approach is suitable for small businesses that lack the capital to maintain an in-house data science team.
TinyML is a new approach to machine learning and AI model development. It involves running models on devices with hardware restraints, such as microcontrollers powering smart vehicles, refrigerators and electric meters. This strategy is the best fit for these use cases because it speeds up the algorithm—there is no need for the data to go back and forth to a server. It is especially important on large servers and can speed up the entire ML development process.
Running TinyML programs on an IoT edge device has many benefits:
- Lower energy consumption.
- Reduced latency.
- User privacy guarantees.
- Reduced bandwidth requirements.
Using TinyML offers greater privacy because the computation process is entirely local. It consumes less power and bandwidth, resulting in lower latency because it doesn’t require sending data to a central location for processing. Industries that are taking advantage of this innovation include agriculture and healthcare. They typically use IoT devices embedded with a TinyML algorithm to monitor and predict real-world events using collected data.
In this article I covered the key differences between MLOps and DevOps:
- Development—DevOps pipelines focus on developing a new version of a software product, while MLOps focuses on delivering a working machine learning model.
- Version control—DevOps is mainly concerned with tracking binaries and software artifacts, while MLOps tracks additional factors like hyperparameters and model performance.
- Reusability—DevOps and MLOps both strive to create reusable processes and pipelines, but use different strategies to achieve repeatability.
- Continuous monitoring—Monitoring is important to DevOps, but even more important in MLOps because models degrade in performance due to model and data drift.
Finally, I covered a few key trends that will transform DevOps and MLOps in the near future. I hope this will be useful as you discover your place in the new and exciting development ecosystem.