MLOps: What It Is, Why it Matters, and How To Implement It

Until not so long ago, we could manually manage the data available within our company. Data governance and curation was an almost artisanal process that was entrusted to expert hands within our organization. Now, however, we find ourselves in a data-driven world, an environment of automated captures and full of growing and changing information flows that flood our data warehouses relentlessly.

We have been forced to face this situation: creating Data Lakes, facing streaming data processing, and creating new profiles in our company such as ML Engineer and Data Engineer. However, there is another fundamental element in our process that needs to be taken into account: AI decision-making.

Decision automation (ML Model Assisted) has been embracing more and more applications and levels of our business model. It has proven to be an important tool to reduce problems, time, and costs in our operations. In short, AI has made us more effective and efficient. However, the fuel of these automatic elements is information; its quality and updating depend on how well it works. As we have already mentioned, the current paradigm imposes growing and changing data and these decision systems, vital for our company’s competitiveness, need to adapt just as quickly to these changes.

During the information revolution at the end of the last century, large companies found themselves in a very similar situation with the rise of IT solutions. Faced with the need to adapt themselves and their software products, agile philosophies emerged. New products and their versions began to be produced at a speed that was too fast for the traditional operations paradigm, and DevOps practices emerged as a solution. Corresponding to this situation, ML lifecycles and MLOps (Machine Learning Operations) practices have emerged to overcome the new context and take advantage of the business value that arises from it. So what is MLOps?

What is MLOps?

MLOps, or Machine Learning Operations, is an extension of the DevOps methodology that seeks to include machine learning and data science processes in the development and operations chain to make ML development more reliable and productive.

What is the goal of MLOps?

The objective of MLOps is to develop, train and deploy ML models with automated procedures that integrate Data, developers, security, and infrastructure teams.

Why is it important to implement MLOps?

We are in a data-driven world. The importance of MLOps models is that they are vital to streamlining the maturation process of AI and ML projects within an organization.

The use of trained models has changed dramatically in the last decade, and now the policy of managing ML models as ‘black boxes’ that they were in traditional operations is no longer sufficient. Models are updated and maintained. It is therefore vital that the effort and time to deploy and maintain them is reduced as much as possible.

What challenges does MLOps solve?

Managing these systems at scale is not an easy task, and there are numerous gaps that need to be addressed. A good MLOps policy solves challenges such as:

• Shortage of data scientists to develop and deploy scalable tools.
• Rapidly accept changes to objectives allowing to deliver value as quickly as possible.
• Bridging communication gaps between technical, data, and business teams due to a known workflow.
• Facilitate risk management in the implementation of ML Models.

Benefits of MLOps

There are many benefits that good MLOPs practices bring to a project. Moreover, they are universal to all types of models, whether they are statistical, rule-based, or machine learning models. They all address the same deployment, validation, monitoring, and maintenance needs throughout their useful life.

Good MLOps practices bring many benefits:

• Accelerate the value creation process.
• Risk reduction during model validation (reduce investment in time and money in models that are not going to be useful).
• Simplify the implementation of more complex solutions (with more models, with more training, with conditional training…).
• CI and CD processes, faster and with fewer errors thanks to the automation of automated processes.
• Continuously monitor and update models to evolve with data, improving the efficiency of AI systems.

DevOps and MLOps

MLOps is based on the DevOps discipline, acquiring its way of writing, deploying, and running enterprise applications efficiently. However, there are differences between them:

• MLOps is more experimental in nature.
• In Machine Learning projects, the team is hybrid, where data scientists or Machine Learning engineers are included.
• Specific testing is needed to validate the Machine Learning model.
• Monitoring of production models
• Continuous Integration (CI) to test and validate data, data schemas, and models
• Continuous Deployment (CD) focused on one system automatically deploying another service
• Continuous Testing (CT) to automatically retrain and service models

How to implement MLOps

In order to increase the efficiency of workflows in Machine Learning projects, you can watch the following session with our colleagues Francisco José Pérez and Kevin Albes (Software Development Engineers at Plain Concepts): “MLOps. Software best practices for building Machine Learning solutions”.

The session addresses topics such as how to get a faster experimentation and model development, get a more efficient deployment of production models, quality control, integration, and continuous delivery through Azure DevOps, Azure Databricks, MLFlow…

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