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.
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.
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.
DevOps:
MLOps:
DevOps:
MLOps:
DevOps:
MLOps:
Monitoring is essential for both DevOps and MLOps, but for slightly different reasons.
DevOps:
MLOps:
DevOps and MLOps both rely heavily on cloud technology, but have different operational requirements.
DevOps relies on infrastructure such as:
MLOps relies on infrastructure such as:
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:
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:
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:
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:
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.
The data used to train AI models needs to reflect the production environments where applications are deployed.
Looking for a DevOps job? Look at these openings at NBC Universal, BAE, UBS, and other companies with three-letter abbreviations.
Tricentis is adding AI assistants to make it simpler for DevOps teams to create tests.
Redis is taking it in the chops, as both maintainers and customers move to the Valkey Redis fork.
GitLab Duo Chat is a natural language interface which helps generate code, create tests and access code summarizations.
Expect attacks on the open source software supply chain to accelerate, with attackers automating attacks in common open source software…