For years, DevOps was a term that was used to describe the software development cycle as it was being introduced into the continuous delivery process. The idea of using DevOps in an IT environment has become more prevalent over the last few years and many companies are now trying to enhance their DevOps strategies.
DevOps – the practice of merging development and operations – has been around for a while now. It was created to help developers collaborate and manage their code deployments in an agile way. While it has helped teams to work more efficiently together, it cannot handle machine learning operations because it lacks the necessary skill sets required to do so.
Machine learning (ML) is about making predictions from data, as opposed to conventional programming where the developer provides a set of instructions for the computer to follow. It can be used to classify, segment and label data. It is also used in prediction, forecasting, optimization and personalization, to mention a few.
Here, MLOps comes into the picture. MLOps is the unification of machine learning and operations. It refers to tools and practices that are used to develop better communications across the organization. MLOps deployment automates the entire machine learning lifecycle to enhance the efficiency of deployment and continuous integration.
Incorporating DevOps ideas into machine learning shortens the development cycle and improves quality control. It even enhances the capacity to acclimatize to altering business requirements. It facilitates faster development cycles, speeds up time to market, augments the development speed and develops better code quality with the help of suitable testing.
What is MLOps?
MLOps is a new type of system administration that is needed in the age of machine learning. MLOps provides tools for monitoring, modeling, optimizing and automating machine learning operations.
Machine learning relies on insights from data to produce models that can be used to make predictions or suggestions. With the exponentially rising number of devices, devices with different kinds of capabilities and diverse applications in our life, these resources will help developers to create more scalable and reliable applications with less effort. All these resources are interconnected in a single platform called MLOps.
ML project lifecycle
Why do we need MLOps?
MLOps is the implementation of DevOps in machine learning project development. It facilitates robust and maintainable development of ML systems. Now, let’s explore some of the benefits and reasons that make MLOps essential for your business:
Continuous value and fewer risks
MLOps minimizes the risks linked with data science, machine learning and AI aspects and helps organizations create long-term value.
Streamlined practices and better customer experience
Machine learning operations support in deploying solutions that help organizations to:
- Discover unrealized income streams
- Save time
- Minimize resource costs
- Streamline business processes
- Make better decisions
- Improve customer experience
Automation and quicker time to market
MLOps automation in software and machine learning development offers faster time-to-market, minimizes operational costs and facilitates businesses to be more responsive and tactical in their decision-making process. Automation also helps several teams to focus on crucial business matters to drive proactive measures.
Collaboration
MLOps facilitates a collaborative process and enables teams to work in association to unite their knowledge and skills for creating more efficient, fast and scalable machine learning models.
Rapid innovation and easy deployment
MLOps improves collaboration and automation that promote faster transformation and development of innovative ML solutions. It also improves the continuous integration, deployment and delivery processes of ML solutions.
Efficient lifecycle management
MLOps improves the efficiency of all the teams and ensures that you complete the development of your machine learning project, right from design to deployment efficiently.
Other benefits of MLOps
- Minimizing technical debt
- Applying agile principles to ML project
- Reproducibility assurance
- Data versioning
- Automated testing of ML artifacts
- Monitoring performance of models in production
- CI/CD support for data and assets
- Continuous training support
- Scalability and security
DevOps vs MLOps
The stages in DevOps are targeted for the development of software applications. It’s up to you how you wish to create, build and test a code, make a release plan and deploy the features of an application. You check and supervise the infrastructure where you deploy your application till it is completely built.
Talking about MLOps, it has fundamental similarities with DevOps since it has been derived from DevOps principles. Yet, DevOps and MLOps are quite different in execution, let’s check how:
Scoping
With MLOps, we classify the project and check if there is any requirement for a machine learning algorithm to solve the issues. We also perform requirement engineering to check if the applicable data is available. Then we verify and confirm if the data fetched is non-biased and shows an actual use case.
Data engineering
At this stage, the developers collect, clean, format, label and organize data as well as establish baselines.
Modelling
Here comes the coding part, where we create an ML model and train it with the processed data. Next, we perform error analysis, error measurement and track the performance of the model.
Deployment
After all the above stages, we package the model and deploy it. Deployment can be done either in the cloud or on edge devices as per the requirement.
Monitoring
After deployment, we depend upon a monitoring infrastructure for maintenance and updating the model. We monitor the infrastructure to check the load, usage, storage, etc. and then we monitor the model for its performance, accuracy and other crucial factors.
Summarizing it up!
Machine learning operation is the latest addition in the world of DevOps. MLOps aims to use machine learning to help automate all operation needs in the cloud.
Machine learning has become an integral part of the enterprise. MLOps deployment is not only powering the apps that we use on our phones and online, but also predictive models that are becoming more important to businesses.
If you’re interested in getting started with machine learning, but aren’t sure where to begin, our developers will help you get your feet wet and enable you to use machine learning to make your company more efficient and competitive. Our team of experts is here to help you get started and ensure business growth. Learn more about the different services we can offer you by getting in touch with us today.
