MLOps: Everything You Need to Know

The demand for data-related roles has been on a constant rise in recent years. The percentage of people shifting to the data industry from different technical backgrounds is growing constantly. Data Science and Analytics jobs are the highly competitive job roles that most people dream of.

However, other areas in the data industry are less known. Such is Machine Learning Operations (MLOps). Read the article till the end to learn about What is MLOps and to whom it is for.

What is MLOps?

MLOps stands for Machine Learning Operations, is a collaborative work focussing on enhancing the Machine Learning Model’s efficiency, integrating the ML model with the production environment with ease, and adding improvements through feedback obtained from real-time monitoring.

The DevOps-inspired concept of MLOPs was born to solve the challenges faced by the traditional deployment methods of ML models which led to difficulty in collaborating and proven to be less effective in real-time. It mainly focuses on collaboration, automation, and continuous improvement.

Due to the drastic transformation caused by ML in recent years, all industries and businesses are attracted to the revolution of AI/ML because of its operational efficiency. To know how this works, let’s understand the Evolution of Machine Learning Operations.

What are the Challenges in Traditional ML Workflows?

Machine Learning Operations evolved from dealing with the challenges faced in traditional ML workflows. Here are some of the challenges faced in ML workflow:

• Collaboration: In traditional methodology, teams of different expertise work separately which restricts communication among them and can cause a lack of collaboration.
• Version Control: Version controlling becomes troublesome because it gets difficult to manage different versions of models and it becomes a hassle to keep track of changes that took place in a traditional workflow.
• Manual processes might be very time-consuming and prone to human errors.
• Automation is limited in traditional workflows.
• A lack of robust and efficient mechanisms can cause difficulty in ensuring the model’s performance.
• Security of data in the workflow is limited.

Emergence of MLOps as a Solution:

In the continuous process of evolution from the challenges faced in traditional ML workflows, we end up finding solutions that enhance MLOps, which are:

• Adopted DevOps Principles – The first step in the evolution of MLOps was incorporating the principles of DevOps and applying them to machine learning workflows that eventually increased the efficiency of the model and collaboration teams.
• CI/CD Deployment – Mainly CI/CD pipelines concept is borrowed to automate the machine learning workflow to reduce the time consumption of the Machine Learning Life Cycle right from development to production.
• Automation and Orchestration – Automation is the main focus as it is applied to all the repetitive tasks like training the models, testing, production, and monitoring whereas Orchestration makes sure that the workflow is smooth.
• Containerization – Running the production-level models in a normal environment may have high computational costs and can be a burden on the system. Containerization is a best practice to deal with this issue as it provides an isolated environment to run and deploy the applications across different environments.
• Version Control for Models and Data – Version Controls in MLOps is similar to the one in DevOps. This is applied to the Machine Learning models to observe the changes and automate the process accordingly to the change and then be released into production. One can roll back to these models wherever it is necessary.
• Monitoring and Logging – MLOps has robust monitoring and logging tools that help in detecting issues and rectifying them if required without any manual intervention.

What are the Benefits of MLOps?

Machine Learning Operations’s target is to bridge the gap between collaborating teams which can save time and help everyone align with the business objectives. Let’s discuss the benefits of MLOps that bridge this gap:

1. MLOps provides a team collaborative environment and cross-functional communication that facilitates a smooth transition from development to deployment.
2. Practices keeping track of changes done in the model allows rolling back to previous versions if necessary and applies to the ML models which is similarly done in software development.
3. Continuous monitoring helps in detecting issues like a decrease in performance and provides solutions and responses to the issue automatically with less human interference.
4. Machine Learning models, their training, and deployment can consume huge amounts of computational resources whereas MLOps optimizes these computational resources and makes them cost-effective, by automating the tasks, and scaling infrastructure.
5. In the machine learning life cycle, to eradicate or reduce the number of errors and inconsistencies, some standardized processes are introduced by MLOps for development, testing, deployment, and monitoring, by adhering to some regulatory requirements and governance standards.

What is the Difference between MLOps and DevOps?

The key difference between MLOps and DevOps is that MLOps was designed for Machine Learning Models and DevOps was designed to deal with the overall Software Development. Let’s dive in and understand the key differences between MLOps and DevOps.

Factors	MLOps	DevOps
Scope	MLOps extends the principles of DevOps and deals with the deployment of Machine Learning Models.	DevOps is a collaboration between software and IT to deliver development to the production environment efficiently.
Objective	To speed up the deployment of Machine Learning models and continuous improvement in its real-time monitoring.	To speed up the development and delivery process of products/applications with fast and reliable releases.
Workflow	Only focuses on deploying machine learning models, creating suitable pipelines, automating the models, and real-time monitoring for model performance.	Circumscribes the Software Development Life Cycle, from start to end until product/application reaches the production environment and even after it.
Tools & Tech	Model Development : Jupyter Notebook, Keras, Scikit-learn, Fastai, PyTorch. Model Deployment Tools: TensorFlow, Docker, Kubernetes, MLfolw, kubeFlow. Monitoring Tools: TensorBoard, Kibana, Grafana, Datadog.	Version Control Systems: Git, Apache, AWSCodeCommit. CI/CD tools: Jenkins, Travis CI, Bamboo, Team City. Configuration Management tools: Ansible, Salt, Puppet, CFEngine Containerization: AWS, Kubernetes, Azure, Docker
Skill Sets	Fundamentals of Machine Learning, Data Engineering, Model Development, Model Deployment, Containerization, Model Versioning, Monitoring ML models, and A/B testing.	Version Controlling. CI/CD, Automation, Containerization, Orchestration, Infrastructure as Code, Monitoring and Logging, Cloud Platforms.
Data-Centric Focus	Enhancing the model’s workflow when a large dataset is used is the main focus.	The main concerns of DevOps are the development and maintenance of configurations and infrastructure.

What are the Components of MLOps?

Machine Learning Operations have a set amount of core components which they use in order to perform Machine Learning operations. Some of the components are:

• Data Mining/input – The necessary data is taken from a database or mined by Data Miners.
• Data Preprocessing – The data mined is preprocessed, that is having null/incompatible data removed, rearranging data and so on.
• Training the Model – The preprocessed data is split into train and test batches and then fed to the model, training it.
• Parameter Tuning – The trained model’s accuracy is determined by running it with the testing batch. With the feedback/accuracy, the model’s parameters are tuned even more for higher accuracy values.
• Model Deployment – After satisfactory accuracy has been achieved, the model is then deployed to the cloud and its actions are observed.
• Redefining the model parameters – After a set amount of time, the model is again retrained over and over in order to make it susceptible to the data trends.

What is MLOps WorkFlow?

The workflow of MLOps uses the components of MLOps to deliver a streamlined workflow and provide help for updating models post-deployment. Using the data analysed, this analysis can be implemented into the code repository and be revamped into the ML pipelines. This will be implemented into the metadata or, the core structure of the particular data repository you are working on.

Post-deployment, the data is constantly analyzed after some time and the emerging differences (comparing the loss functions) for example will trigger the whole cycle again. This is completely automated and takes this work off human hands which in turn, means fewer human resources.

Who is a MLOps Engineer?

The MLOps engineer is a bridge between data scientists and IT Operations who ensure the efficModel DevelopmentModel DevelopmentModel DevelopmentModel DevelopmentModel DevelopmentModel Developmentiency of the machine learning life cycle from development to the production environment. Let us look at the key responsibilities of an MLOps engineer.

What are the Roles and Responsibilities of MLOps Engineer?

The role of an MLOps Engineer is to build workable solutions for the world’s problems using Machine Learning models. They also have the express responsibility to tune the parameters once a while for data that changes in real-time. They also have the responsibility to find common ground between different platforms such as SaaS, DevOps, Cloud Computing and so on.

The MLOps Engineer also has the express responsibility to handle the model, train the model, analyze the model and draft detailed summary reports describing the Machine Learning model’s effect on the current data. Not only that, they also have to work on finding a process to consistently update and upgrade the deployed model so that it doesn’t become outdated.

Conclusion

In conclusion, MLOps can reduce most of the manual work and initiate automation processes. Suggesting all the data science and ML enthusiasts that do not solely depend on learning the data science and ML concepts to become a data scientist. To be a good data scientist, one must definitely know how to work with MLOps because implementing the machine learning models and making those models reach the production is a sign of good data scientist.

FAQs

1. Which languages should you know to work with MLOps?

MLOps is a tool which is used for developing and deploying Machine learning models that require the programming knowledge of Python, R language, or Java.

2. DevOps vs MLOps. Which one is better?

We cannot tell which one is better because both of them are designed for different use cases. DevOps is for Software Development Lifecycle whereas MLOps is for Machine Learning models.

3. Qualifications of an MLOps Engineer?

The necessary qualifications of an MLOps Engineer are fluency in Python, experience with building ML models, strong software engineering skills, and experience with database management systems.

4. Will there be any future trends in MLOps?

MLOps is a promising career as there is room for further advancements to improve the automation in ML workflows. With the advent of technology, there is quite some demand for convergence with DevOps and MLOps. There is also quite some room when it comes to development of hybrid AI (Artificial Intelligence) applications which use AI-based chatbots to interact with users.