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Introduction to MLOps

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Most data scientists are not expert programmers. While they are adept at choosing or creating the best model to solve a machine learning problem, they don’t necessarily have the expertise to package, test, deploy, and maintain this model in a production environment. That’s exactly where MLOps comes to the rescue.

MLOps creates a bridge between data scientists and production teams. It is a practice that combines DevOps with Machine Learning. This article talks about MLOps, why it is needed, the challenges of MLOps, the tools available, and how MLOps pipelines work.

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What is MLOps?

Machine Learning Operations (also known as MLOps) is a collection of tools and best practices for improving communication across teams and automating the end-to-end machine learning life cycle to improve continuous integration and deployment efficiency. It is a concept that refers to the merger of long-established DevOps methods with the growing science of Machine Learning.

MLOps encompasses more than model construction and design. It includes data management, automated model development, code generation, model training and retraining, continuous model development, deployment, and model monitoring. Incorporating DevOps ideas into machine learning offers a shorter development cycle, improved quality control, and the ability to adapt to changing business needs.



DevOps vs. MLOps

DevOps and MLOps are both methodologies aimed at improving the software development and deployment process but differ in focus and objectives. Here’s a comparison of DevOps and MLOps:



  • DevOps: DevOps focuses on improving collaboration and integration between development (Dev) and operations (Ops) teams to automate and streamline the software delivery pipeline.


  • MLOps: MLOps, or Machine Learning Operations, focuses specifically on the deployment, management, and optimization of machine learning models in production environments.



  • DevOps: The primary objective of DevOps is to accelerate the software development lifecycle, improve agility, and enhance the quality and reliability of software releases.


  • MLOps: The primary objective of MLOps is to operationalize machine learning models, ensuring they are deployed, monitored, and maintained effectively in production environments to deliver business value.



  • DevOps: DevOps encompasses a set of practices, including continuous integration, continuous delivery (CI/CD), infrastructure as code (IaC), and automated testing, to automate and optimize software development and deployment processes.


  • MLOps: MLOps extends DevOps principles to the machine learning lifecycle, incorporating practices such as version control for models and data, automated testing, model deployment automation, and monitoring of model performance in production.


To understand MLOPs better, it helps to emphasize the core differences between MLOps and DevOps.


  • Team composition

DevOps teams are populated mainly by software engineers and system administrators. In contrast, MLOps teams are more diverse—they must include:


  • Data scientists and ML researchers in charge of developing machine learning models
  • Data and software engineer teams providing production-ready solutions
  • Team members dedicated to communication with stakeholders and business developers. They make sure machine learning products meet business expectations and provide value to their customers.
  • Data annotators or annotation workforce managers who label training datasets and review their quality


  • Scoping

Scoping entails the preparation for the project. Before starting, you must decide if a given problem requires a machine learning solution—and if it does, what kind of machine learning models are suitable. Are datasets available or need to be gathered? Are they representative of reality or biased? What tradeoffs need to be respected (e.g., precision vs. inference speed)? Which deployment method fits the best? A machine learning operations team needs to address these issues and plan a project’s roadmap accordingly.


  • Versioning and Reproducibility

Being able to reproduce models, results, and even bugs is essential in any software development project. Using code versioning tools is a must.

However, in machine learning and data science, versioning of datasets and models is also essential. You must ensure your datasets and models are tied to specific code versions.

Open source data versioning tools such as DVC or MLOps platforms are crucial to any machine learning operations pipeline. In contrast, DevOps pipelines rarely need to deal with data or models.


  • Testing

The MLOps community adopted all the basic principles of the unit and integration testing from DevOps.

However, the MLOps pipeline must also include tests for both model and data validation. Ensuring the training and serving data are in the correct state to be processed is essential. Moreover, model tests guarantee that deployed models meet the expected criteria for success.


  • Deployment

Deploying offline-trained models as a prediction service is rarely suitable for most ML products. Multi-step ML pipelines responsible for retraining and deployment must be deployed instead. This complexity requires the automation of previously manual tasks performed by data scientists.


  • Monitoring

Model evaluation needs to be a continuous process. Not unlike food and other products, machine learning models have expiration dates. Seasonality and data drifts can degrade the performance of a live model. Ensuring production models are up-to-date and on par with the anticipated performance is crucial.


Why do we need MLOps?

Implementing MLOps benefits your organization’s machine learning system in the following ways:


  • Reproducibility: You can accurately reproduce results and bugs.


  • Debugging: ML pipelines are notoriously challenging to debug. Many components may fail, and bugs are often extremely subtle. MLOps makes debugging more straightforward.


  • Efficiency: ML needs plenty of iterations. With MLOps present, iterations are swifter due to the lack of manual processes.


  • Deployment: Automated, rapid, fault-tolerant model and pipeline deployment.


  • Model quality: In addition to swifter training cycles, data drift monitoring and model retraining aid the improvement of machine learning models while in production.


  • Scalability: With most processes automated and well documented, training more models and serving more requests is easier.


  • Fault tolerance: CI/CD pipelines as well as unit and model testing, decrease the probability of a failing product/service reaching production.


  • Research: With most menial tasks taken care of by software, data scientists and machine learning researchers have more time to model R&D. Moreover, CI/CD pipelines foster innovation due to quicker experimentation.


What are some of the MLOps challenges?

Since the field is relatively young and best practices are still being developed, organizations face many challenges in implementing MLOps. Let’s go through three main challenge verticals.


Organizational Challenges

The current state of ML culture is model-driven. The research revolves around devising intricate models and topping benchmark datasets, while education focuses on mathematics and model training. However, the ML community should devote some of its attention to training on up-to-date open-source production technologies.

Adopting a product-oriented culture in industrial ML is still an ongoing process that meets resistance, which might make it more difficult to adopt it into an organization seamlessly.

Moreover, the multi-disciplinary nature of MLOps teams creates friction. Highly specialized terminology across different IT fields and differing levels of knowledge make communication inside hybrid teams difficult. Additionally, forming hybrid teams of data scientists, MLEs, DevOps, and SWEs is costly and time-consuming.


Architectural and System Design Challenges

Most machine learning models are served on the cloud with requests by users. Demand may be high during certain periods and fall back drastically during others.

Dealing with a fluctuating demand in the most cost-efficient way is an ongoing challenge. Architecture and system designers also have to deal with developing infrastructure solutions that offer flexibility and the potential for fast scaling.


Operational Challenges

Machine learning operations lifecycles generate many artifacts, metadata, and logs. Managing all these artifacts with efficiency and structure is a difficult task.

The reproducibility of operations is still an ongoing challenge. Better practices and tools are being continuously invented.


MLOps levels

MLOps, which stands for Machine Learning Operations, is all about streamlining the process of developing, deploying, and maintaining machine learning models. There are different levels of MLOps maturity, indicating how automated and efficient your ML lifecycle is. Here’s a breakdown of some common MLOps levels:



Level 0: Manual Process

This is the starting point, where everything is done manually. Data scientists handle data preparation, model training, and deployment, often using tools like Jupyter Notebooks. It’s slow, error-prone, and difficult to reproduce results.


Level 1: ML Pipeline Automation

Here, data scientists focus on automating the ML pipeline itself. They use tools to automate data processing, model training, and evaluation. This improves efficiency and reduces errors, but deployment is still manual.


Level 2: CI/CD Pipeline Automation

This level integrates MLOps with CI/CD (Continuous Integration/Continuous Delivery) practices. Changes in code trigger automatic testing, training, and deployment. This ensures consistent and reliable deployments.


Level 3: Automated Monitoring and Feedback

At this level, models are continuously monitored in production. Metrics are collected to track performance and detect drift. Alerts are generated if issues arise, and retraining pipelines can be automated based on these.


Level 4: Full MLOps Automated Retraining

This is the most advanced level, where the entire ML lifecycle is automated. Monitoring and feedback are automated, and models are automatically retrained based on new data or performance degradation.


Best Practices For MLOps

Let’s go through a few of the MLOPs best practices, sorted by the stages of the pipeline.


  • Version Control: Use version control systems like Git to manage code, data, and model versions. This ensures reproducibility and allows for collaboration among data scientists and engineers.


  • Automated Testing: Implement automated testing for models to validate their performance and behavior across different datasets and environments. This includes unit tests, integration tests, and validation tests.


  • Continuous Integration and Continuous Deployment (CI/CD): Set up CI/CD pipelines to automate the process of building, testing, and deploying machine learning models. This ensures rapid and reliable deployment of models into production.


  • Monitoring and Logging: Monitor model performance and behavior in real-time using logging and monitoring tools. Track accuracy, latency, and throughput metrics to detect anomalies and ensure models perform as expected.


  • Model Versioning: Maintain a central repository of model artifacts and metadata, including trained models, configuration files, and performance metrics. This allows for easy tracking and retrieval of model versions.


  • Scalability and Resource Management: Design models and infrastructure to scale horizontally and vertically based on workload demands. Utilize containerization and orchestration tools like Docker and Kubernetes to manage resources efficiently.


  • Model Governance and Compliance: Establish policies and procedures for model governance, including data privacy, security, and regulatory compliance. Implement access controls and audit trails to ensure accountability and transparency.



MLOps offers a structured approach to developing, deploying, and monitoring machine learning models. By incorporating DevOps principles, MLOps aims to bridge the gap between data science and production teams, ultimately leading to more efficient and reliable machine learning systems.




  1. What are the core differences between DevOps and MLOps?

While DevOps focuses on software systems as a whole, MLOps places particular emphasis on machine learning models, requiring specialized treatment due to the data and models involved. MLOps teams are also more diverse, encompassing data scientists, software engineers, and business development specialists. Additionally, MLOps incorporates data versioning, model testing, and continuous monitoring, aspects not typically required in traditional DevOps pipelines.


  1. Why is monitoring crucial in MLOps?

Machine learning models can degrade over time due to factors like data drift and seasonality.  Monitoring helps ensure models are performing as expected and enables proactive retraining when necessary.


  1. What are some of the challenges of implementing MLOps?

MLOps faces challenges on multiple fronts.  Organizationally, fostering a product-oriented culture and bridging the communication gap between different disciplines can be difficult. Architecturally, designing systems to handle fluctuating demand requires careful consideration. Operationally, managing artifacts, metadata, and logs efficiently and ensuring reproducibility of operations are ongoing areas of focus.

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