Machine learning operations, commonly referred to as MLOps or DevOps for machine learning, is a practice for streamlining and integrating the workflows involved in building, testing, and deploying machine learning models. This machine learning tutorial for developers and project managers provides an overview of MLOps, including its pros and cons.
Before continuing, you might want to read our tutorial: Understanding Machine Learning.
What is MLOps?
MLOps, or DevOps for machine learning, is the practice of combining software development (Dev) and operations (Ops) to streamline the process of building, testing, and deploying machine learning models.
MLOps can help organizations improve the quality of their machine learning models by providing a more reliable and automated way to develop, test, deploy, and maintain them. Additionally, MLOps can help organizations automate the process of training and tuning machine learning models, which can save time and resources.
The goal of MLOps is to help organizations scale faster and be more agile in the face of rapidly changing data and business demands. By automating and orchestrating the workflows involved in ML development, MLOps can help reduce cycle time from idea to production, and make it easier to track experiments and keep models up to date.
Additionally, MLOps can help improve the quality of machine learning models by making it easier to monitor model performance and identify problems early. By integrating ML into the overall DevOps process, organizations can also benefit in more ways than one.
Implementing MLOps can be challenging, but the benefits are clear. Organizations that adopt MLOps can improve the quality of their machine learning models, save time and resources, and gain a competitive advantage. MLOps can help you automate and streamline the process of creating and maintaining machine learning models.
We have a great tutorial that serves as an introduction to DevOps and DevSecOps if you want to learn more about the DevOps methodology.
What are the fundamentals of MLOps?
The fundamental principles of MLOps are:
- Continuous integration and delivery: Changes to the codebase are automatically built and tested, then deployed to production on a regular basis.
- Infrastructure as code: The infrastructure that runs machine learning models is treated as code, which can be versioned, audited, and automated.
- Monitoring and logging: The performance of machine learning models is monitored in production and logs are collected to aid in debugging and performance tuning.
What are MLOP pipelines?
MLOPs pipelines are a crucial part of the MLOps process. They help automate the entire machine learning workflow, from data preprocessing and model training to deployment and monitoring. By doing so, they free up valuable time and resources that can be used to focus on other areas of the business. Additionally, MLOP pipelines help ensure models are always up-to-date and compliant with company policies.
What are the benefits of MLOps?
There are many benefits to using MLOps, but some of the most notable include:
- Improved project quality: By automating processes and setting clear standards, companies can reduce the number of errors in their machine learning projects.
- Faster time to market: By deploying models at a faster rate and reducing the time spent testing models, you can deliver software faster.
- Increased speed: Businesses can take advantage of automation to reduce the time needed for manual tasks.
- Reduced costs: Additionally, automation reduces the need for repetitive manual labor, which can result in significant cost savings for businesses.
MLOps: challenges and solutions
As machine learning models become more complex and data intensive, the challenges of MLOps become more apparent. Traditional software development practices are not well suited to handling the complexities of building, training, and deploying machine learning models.
One of the biggest challenges of MLOps is data processing. The data we collect changes continuously and it can be difficult to keep track of all the changes. Additionally, the data can be stored in different formats, which can make it difficult to use for machine learning models.
Another challenge is managing different types of machine learning models. Finally, there is the challenge of deployment. After training, a machine learning model should be deployed to a production environment. This can be tricky because there are usually many different types of environments that need to be supported.
It’s hard to scale your team fast enough. In a field like machine learning, companies can struggle to find people with the skills to meet their demands. And since many MLOps engineers have little experience in data science or software development (or both), building a strong team takes time and patience, two things that might not be available if deadlines are approaching.
The tools are not there yet. Even if you build an in-house team ready to take on all of these responsibilities, they will still need access to cutting-edge tools and technology to get it right, but even now there are few options in this area. . space beyond open source technologies like TensorFlow and Keras.
Fortunately, there are ways to fix these problems. Version control technologies, such as Git, are commonly used to manage data. You can take advantage of the tools available to manage various machine learning models.
To be effective in adapting a solid MLOps approach, you’ll want to be sure to use some of the best MLOps tools. Our partner site, IT Business Edge, offers an excellent overview of Best MLOps Tools and Platforms.
What are best practices for MLOps
The increased use of machine learning (ML) models in software development has necessitated the need for a new branch of DevOps known as MLOps. MLOps is a set of best practices that aim to streamline the process of developing, training, and deploying ML models.
There are many ways to approach MLOps, but some common practices include automating data preprocessing and model training, using containerization to package dependencies, and using cloud services to scalability. Adopting these practices can help teams move faster and improve the quality of their machine learning models.
Some of the best practices for MLOps include those listed in the next section.
Automate data preprocessing and model training
One of the most time-consuming aspects of ML development is data preprocessing and model training. When performed manually, each of these processes can be time-consuming and error-prone. You can save a lot of time and effort by automating these activities. Several tools and frameworks are available to automate data preparation and model training, such as Apache Airflow, Prefect, and Kubeflow.
Use containerization to package dependencies
Another best practice in MLOps is to use containers to package dependencies. This approach can be very useful when team members are working on different parts of the same project but need to use different versions of dependencies.
Containers allow each team member to work with an isolated environment that has its own set of dependencies. This isolation facilitates code sharing between team members and also facilitates the deployment of applications in different environments.
Use cloud services for scalability
Another common best practice in MLOps is to use cloud services for scalability. Cloud services provide a great way to scale up or down based on project needs. They also make it easier to share resources among team members and deploy applications to different environments.
Implement continuous integration and delivery
Continuous integration (CI) and continuous delivery (CD) are two key DevOps ideas that can also be used for MLOps. You can leverage CI/CD to automate the development, testing, and deployment of your applications.
By implementing CI/CD, teams can save a lot of time and effort when developing machine learning models. Many different tools and frameworks can be used for CI/CD, but some popular options include Jenkins, CircleCI, and Travis CI.
Final Thoughts on MLOps
MLOps is a new area of operations that is emerging to support the growth of ML and AI. It’s a way of thinking about DevOps, data engineering, and data science that combines these disciplines into a single team focused on building and maintaining ML models.
MLOps is a process for applying DevOps principles to machine learning projects to streamline and automate the entire workflow, from data preparation to model training to deployment. MLOps can help reduce the cycle time of machine learning projects and improve the overall quality of the models produced.
Additionally, MLOps can help teams better collaborate on machine learning projects and make it easier to track progress and experiment with different approaches. If you work on machine learning projects, then MLOps is definitely something you should consider adopting.
Read more tutorials on project management and software development methodology and tool reviews.