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Ruslan Kh.
Ruslan Kh.

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The Evolution and Impact of MLOps: understanding MLOps

Machine Learning Operations, or MLOps, is the missing bridge between machine learning, data science, and data engineering. It has emerged as the link that unifies these functions more seamlessly than ever before. So what is MLOps, and why does it matter? In this article, we'll dive into the MLOps Hierarchy of Needs, a fundamental concept that illustrates how MLOps supports, enhances, and optimizes the machine learning workflow within an organization.

MLOps structure hierarchy

DevOps: The Foundation of MLOps

To understand MLOps, we must first understand the structure of this hierarchy, which can be visualized as a pyramid. At the base of this pyramid is DevOps, the practice that enables continuous delivery and automated provisioning of environments. Without a solid DevOps foundation, there's no way an organization can successfully implement MLOps. Key components of DevOps include infrastructure as code and a robust build system that allows services to be deployed in the staging environment and automatically propagated to production.

DevOps, a combination of "development" and "operations," is a collaborative approach that streamlines the software development process. It bridges the gap between development and operations teams, facilitating continuous integration and delivery. This methodology is essential for an organization to dive into MLOps.

Key components of DevOps include Infrastructure as Code (IaC), continuous delivery, and a design based on a robust build system. IaC enables automated provisioning of environments that can be integrated with your build system, providing significant flexibility and reproducibility. Similarly, continuous delivery ensures that microservices are systematically updated in the staging environment and propagated to production, increasing speed and efficiency.

Data Operations: The Next Level

Once the foundational level of DevOps is established, the next step is to focus on data operations. This involves setting up data management platforms such as Google BigQuery, Databricks, Snowflake, or Amazon Athena. These platforms facilitate serverless query and visualization workflows, making data processing easier. They also support data jobs and tasks to ensure efficient data operations.

MLOps Platforms: Essential tools for machine learning

The third layer is the implementation of an MLOps platform. This layer emphasizes the use of specialized tools rather than building everything from scratch, which can take a lot of time away from core business objectives. Building on the data operations layer, it's time to integrate MLOps platforms. These are specialized tools designed to streamline the machine learning process, including feature stores, model serving platforms, and experiment tracking tools. Feature stores store pre-curated features for reuse, while model serving platforms manage the deployment of ML models. Experiment tracking tools help track various metrics and explanatory techniques, and also monitor data drift, allowing you to measure the impact of a model in production and observe how the underlying data changes over time. They help monitor different metrics, training techniques, and data drift - a concept that describes how the performance of an ML model can degrade over time as the underlying data changes.

The MLOps layer: Workflow Automation

MLOps tasks

Finally, we reach the top of the pyramid - the MLOps layer itself. Here, the focus is on business value. It's important to ensure that the machine learning models being created are delivering value to the organization. This value can be tracked and quantified, providing visibility that is useful for the health, security, and reputation of the organizations using the models. Getting the problem right is also critical at this stage - solving the wrong problem can lead to wasted resources and missed opportunities.

MLOps is not just about process, it is also about transforming people and technology. Successful MLOps implementation requires the participation of people from different levels and departments in an organization, creating a culture of best practices.

There are several trends and predictions for MLOps in 2023. One key trend is the continued investment in machine learning, driven by the rapid evolution of MLOps and the machine learning industry. However, the integration of MLOps continues to present challenges. The onboarding and deployment of AI and ML algorithms can be complex, requiring careful workload orchestration and server balancing.

A growing number of organizations are adopting tools like Metaflow, open-sourced by Netflix and AWS, to design, run, and deploy their workflows at scale, automatically versioning and tracking all experiments and data. There is no consensus on a single MLOps tool or application, and the growing number of libraries and packages in MLOps is expected to have a significant impact on enterprises.


The MLOps Hierarchy of Needs is a structured approach to implementing MLOps in an organization. By starting with DevOps and gradually building up to data operations, MLOps platforms, and finally the MLOps layer itself, organizations can streamline their ML workflows and maximize their business value. The goal of MLOps isn't just about automation; it's about driving efficiency and accelerating business outcomes.

Implementing MLOps can enable capabilities such as accurate inventory forecasting and the discovery of new patterns through unsupervised machine learning. But remember, the journey to MLOps is not a sprint; it's a marathon. It requires consistent effort, constant learning, and the right resources to successfully implement and manage MLOps in an organization.

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