MLOps Explained: Machine Learning Operations, Pipeline, Automation & More

The advancement and uptake of Machine Learning (ML) has exploded in recent years, with AI models used across almost every flavor of IT application. But, developing, planning, and maintaining these ML models, known as Machine Learning Operations, is a complex beast that needs to be managed carefully. 

Pulling some data into an ML model is only the start, with governance, processes, metric tracking, and continuous delivery mechanisms all required to keep an MLOps pipeline running smoothly.  

If you’re new to Machine Learning Operations (MLOps) then this article is for you. We’ll look at what MLOps means, the principles that underpin it, and the benefits it can bring to your business. Then to finish, we’ll out four areas to think about to help you get started on your own MLOps journey. 

What is MLOps? A Machine Learning Operations definition

MLOps is the repeatable process of planning, building, automating, deploying, and monitoring new Machine Learning models within your production environment.  

Like other continuous delivery workflows, such as DevOps, MLOps encompasses a range of different disciplines, such as new model design, feature engineering, testing, and new model deployment, as well as the governance controls that sit around the process such as data analysis protocols, security, and cataloging.  

For all of these disciplines to come together, data science teams need to work to a set of principles and standards to ensure model performance and reliability is guaranteed to enable business operations.  

To achieve this performance and stability, most MLOps teams look to achieve the following goals: 

• Achieve the highest level of data quality through stable model architecture and testing 
• Accelerate the model training process to speed up the enhancement of capabilities 
• Automate and streamline the deployment process through continuous integration 
• Enable collaboration between data scientists, ML engineers, and IT professionals 
• Ensure regulatory compliance and responsible AI practices 
• Always striving to improve reliability, reproducibility, auditability, and governance 

Ultimately, MLOps works to leverage the benefits of Machine Learning by putting in place a set of processes and workflows that balance speed, quality, performance, and stability.  

Also read: 

• Fighting Tech Debt with AI 
• Data-driven decision making 

MLOps Principles – pipeline & model development

Before you begin building any ML pipeline components, you need to set the principles you’ll work from. These principles underpin each aspect of MLOps, providing stability on how you’ll go from an initial idea to getting your first ML model in production. 

Best practice MLOps principles include: 

• Continuous flow. Machine Learning isn’t a one and done deployment. Instead, teams should establish a way of working that’s continuous, whether that be continuous design, continuous training, continuous integration, continuous deployment, or continuous monitoring (ideally, all of the above!). 
• Automation by design. Building on from continuous flow, ML teams should strive to use automation to achieve repeatability, consistency, and scalability. Not only will this help improve the effectiveness of ML projects, it also reduces risk across the entire production pipeline.  
• Version Control & Reproducibility: All code, data, models, and configurations should be version controlled for full transparency and reproducibility of experiments. This allows for easy rollbacks and prevents regressions.
• Modular, Reusable Components: The ML codebase should be designed in a modular way with clean separation of assets. This improves code reuse, testability, and enables faster iterations to improve the pace of the ML training pipeline.  
• Strong Data & Model Governance: MLOps platforms help manage the full data pipeline, including data identification, data preparation and transformation, data experimentation, and data testing. The effectiveness of any MI model depends on the data quality, so it needs to be strictly controlled and held to a high standard. 

The benefits of MLOps – best practices, automation & more

Adopting MLOps principles and tooling can unlock significant value for Machine Learning teams and the wider business. Here are just some of the biggest benefits on offer for those who get MLOps right: 

• Faster Delivery. By automating manual ML pipeline processes and hand-offs, MLOps accelerates the entire delivery pipeline from model experimentation to production deployment and monitoring. This means new MI models make it into production faster, enhancing the capabilities available to the business operations teams. 
• Improved Quality & Reliability. End-to-end testing, monitoring, and artifact management ensure models are robust and stable before they enter the production environment. This is especially important in the model training and learning phase, where errors in testing can lead to negative business consequences, especially where an ML model is used to make business-critical decisions. 
• Greater model performance. When combining the speed, automation, consistency, and reliability of ML Ops, ultimately, you get a better ML model at the end of it. This enhances the value of Machine Learning capabilities, making them more effective and increasing their ability to drive better business outcomes.  
• Cost Optimization. Rapid learning, faster deployment, and stronger performance all lead to efficiencies that optimize the overall running costs of MLOps practices. MLOps automation also reduces technical debt, while strategies like multimodel deployment and automated retraining help optimize cloud costs.
• Easier Collaboration. Much like DevOps and other agile-based ways of working, MLOps fosters collaboration between the diverse roles involved in building Machine Learning capabilities. This includes data engineers, data scientists, ML developers, DevOps engineers, testers, business analysts and more, all brought together through common processes and tooling. 
• Scale & Governance. As Machine Learning gets more and more popular, your business use case and production pipeline is only going to get busier. MLOps practices help maintain oversight, traceability, and control over your sprawling landscape, enabling you to scale while still maintaining control.  

MLOps maturity – MLOps Level 0, MLOps Level 1, and MLOps Level 2 explained

As you begin on your MLOps journey, many people will refer to the three levels of MLOps – levels 0, 1, and 2. This MLOps maturity metric helps organizations understand where they are on the journey and the key aspects of MLOps practice they can improve on in the future.  

Let’s look at each level of the MLOps maturity journey, and what they mean.  

MLOps Level 0 

MLOps Level 0 is the starting point for most organizations, where: 

• Machine Learning model development and deployment processes are largely manual, ad-hoc, and lacking standardization at every step. 
• There is minimal to no automation, with data scientists and engineers performing manual tasks. 
• Model monitoring and training are often reactive and/or neglected entirely. 

MLOps Level 1 

At MLOps Level 1, organizations begin introducing basic automation by: 

• Scripting and scheduled activities at certain stages of the ML lifecycle, such as data ingestion, model training, or testing. 
• Deployment and monitoring processes typically remain manual. 
• It’s likely the ML pipeline still lacks the end-to-end governance, control, and standardization to make it fully robust. 

MLOps Level 2 

Finally, MLOps Level 2 represents a mature and comprehensive implementation, including:  

• Fully automating the ML pipeline, including data preparation, model training, evaluation, deployment, monitoring, and retraining.  
• Governance, standards, and control are implemented systematically across the pipeline. 
• Manual intervention is minimal, with data science teams focusing on Deep Learning strategies and analyzing monitoring outputs.  
• Level 2 enables reliable, repeatable, and scalable ML deployment processes across the organization. 

How to implement MLOps – build, deploy, monitor, and automate your own ML pipeline

As we’ve seen, MLOps brings together many practices, platforms, and processes to make it a success. While there are many MLOps solutions out there to choose from, there are four, high-level, MLOps areas you need to create to get started. These are: 

1. Build – A phase where you pull together data and ML models that meet your pipeline needs. 
2. Deploy – Taking your newly build ML capability and putting it into production. 
3. Monitor – Tracking key MLOps metrics and monitoring performance to make improvements. 
4. Governance & Automation – With a pipeline in place, automating it to drive maturity, stability, and efficiency.  

Let’s take a look at some points to think of in each area to help you use MLOps in your business right away. 

#1 – Starting building your ML capability 

The build stage of the best MLOps frameworks helps get the foundations in place by pulling together data, selecting a model, and testing that it works for your business use case. Specifically, think about these key things: 

• Data Management. Start by identifying, curating, cleansing, and aligning the training data you’ll use to build models. This will depend on your ML application and may even include data that’s specific to your business. 
• Choosing a Model. Next is model selection, where you’ll select the optimal MLOps architecture and algorithm for your use case. Whether it’s linear regression, decision tree, or k-means, there are many out there to pick from, so take the time to research and make the best decision.  
• Testing & Evaluation. Before deploying, comprehensive testing is critical to avoid any mistakes. This includes data integrity checks, unit/integration tests, model validation, regression testing, and model evaluation. Inadequate testing is where Machine Learning models crash and burn, so take the time to thoroughly test your pipeline.  

#2 – Deploy your first ML application 

Once you have your ML application ready to go, it’s time to deploy it out into the world. To nail the execution of the ML pipeline, you need to package and deploy it safely. This includes: 

• Model Packaging. Winning models must be packaged in a secure and scalable way that enables them to be rolled back if any issues occur. Choose a packaging approach that enables this and fits in with your wider deployment practices. 
• Serving Infrastructure. Serving infrastructure like Kubernetes or a serverless platform needs to be ready to host and scale your package. Before you deploy a whole training pipeline, your infrastructure should support capabilities like automated scaling, rolling updates, canary deployments, and monitoring/logging.  

#3 – Monitor your deployment closely 

Now that your application is deployed in the real world, you must keep a close eye on it to ensure it performs as expected. This includes looking at each model in isolation and across your portfolio of models, if applicable. Specifically, put the following in place: 

• Model Monitoring. Once deployed, it’s critical to monitor models for drift, staleness, bias, and performance degradation. Not only does this help ensure quality and stability, but it also enables you to capture feedback for retraining if required. 
• Multi-Model Management. MLOps platforms can also help manage a portfolio of multiple models, comparing performance baselines across the models while also enabling techniques like canary rollouts and A/B tests. 

#4 – Build in governance and automation to drive best practice 

To help overcome many of the day-to-day MLOps challenges, work on progressing to automated MLOps processes over time. This includes CI/CD pipelines, leveraging MLOps tools, and embedding a strong governance culture.  

• CI/CD Pipelines. As your maturity grows, automated CI/CD pipelines can build, test, package and deploy models triggered by any numbers of events. This enables speed while reducing the chance of human error. 
• MLOps Tools. To maximize the key benefits of MLOps, utilize purpose-built platforms and tools to automate workflows, centralize artifacts and metadata, and enforce governance policies. AzureMl, Amazon SageMaker, and DataBricks are three of the most popular MLOps tools on the market. 
• MLOps Culture. Success from MLOps comes when everyone is fully bought into the process. Promoting a culture of collaboration, breaking down cross-functional silos, creating Data Science empowerment, and a production mindset ultimately help drive the best MLOps results.  

It’s time to get started with MLOps 

DevOps and MLOps share many similarities, and as Machine Learning technologies continue to grow, it’s time to put as much energy into your Machine Learning as you do the rest of your development.  

MLOps is the best way to get the most from your Machine Learning technologies, providing structure and control that turn your use cases into reality. Get your MLOps framework right, and you’ll reap the benefits of faster execution, less risk, and higher performing data models.  

While you can start your MLOps journey alone, like many things in IT, working with an expert partner often drives the best results. At Inetum, our value proposition is built upon helping you work alongside the best 3rd party suppliers and combining their capabilities with expert technical advice.