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MLOps: A Comprehensive Guide to Machine Learning Operations

Learn about the world of MLOps - machine learning operations. Automate workflows, deploy ML models, and learn best practices for deploying AI/ML models in production.

Imagine a world where machine learning models can be developed, deployed, and improved with minimal ongoing work. This is the goal of Machine Learning Operations (MLOps). In this article, we delve into the world of MLOps to explore its purpose, general best practices, and useful tools. By the end, you’ll understand how MLOps can improve your organization’s machine learning workflows and bring increased value to your data-driven projects.

What is MLOps?

Machine learning operations (MLOps) is a set of practices that streamline the integration of machine learning models into development, deployment, and maintenance processes. It fosters collaboration between data scientists and operations teams, ensuring that ML models perform optimally and adapt to constantly evolving production environments.

The concept of MLOps comes from the same origin as DevOps. Just like conventional software development, businesses came to realize that specialized skills were needed to efficiently and reliably run ML/AI models in production. The data scientists and researchers creating models have a different skill set than the engineers who have experience deploying products to end users. By working together businesses can iterate and deploy ML/AI models more efficiently to drive real world value.

Key Components of MLOps

MLOps has several key components, including data management, model training, deployment, and monitoring.

Data management involves gathering training data from multiple sources, ensuring its accuracy, and selecting the optimal features for the model to predict through data analysis. An essential part of this process is data preparation, which ensures that the data is ready for analysis.

Model development focuses on creating and refining ML models, while deployment establishes processes for communication, system integration, and pipeline interactions. In the deployment process, a model registry plays a crucial role in managing and tracking these ML models.

The final component of MLOps is monitoring, which helps data science teams observe the performance of the model and data pipeline, enabling the success of the project by making sure the model is producing accurate results with adequate performance.

MLOps vs DevOps: Similarities and Differences

While MLOps and DevOps share principles like continuous integration and continuous delivery, MLOps specifically addresses the unique challenges encountered in ML model development and deployment.

Both methodologies emphasize automation, collaboration, and iterative improvement as essential components for implementation.

Shared Principles

In the realm of MLOps and DevOps, the following factors contribute to building robust and efficient systems:

  • Process automation
  • Continuous integration and deployment
  • Collaboration and communication
  • Scalability and reliability

Monitoring and feedback are also crucial in both methodologies, as they allow for performance evaluation and continuous improvement.

Unique Challenges for MLOps

There are several challenges encountered with MLOps that are not seen in typical DevOps situations. Here are a few of the major ones that need to be taken into consideration:

  • Model result reproducibility - With traditional software, given the same codebase and the same inputs, software always produces the same outputs. In contrast, machine learning models may produce different results even with the same input data. Ensuring model reproducibility requires not just versioning the code, but also tracking data, random seeds, hyperparameters, and the environment.
  • Scaling model training and inference - Training modern ML/AI models requires large amounts of specialized hardware like GPUs, which requires specialized talent to manage. And once trained these models need to perform inference in a fast and scalable manner to be practical for real world use.
  • Model drift - Performance of ML models can degrade over time if the incoming data changes or if there’s a shift in the patterns the model was trained on. Monitoring and managing this drift is crucial for maintaining model performance.
  • Team composition - The teams building ML models are typically researchers and data scientists who aren’t necessarily experienced in deploying production systems compared to software engineers. The other members of the team need to account for this when helping bring models to production. Team members coming from different backgrounds will also need to be able to work together towards a common goal, an ML researcher with academic research experience will need to learn to work in a faster pace environment that focuses more on driving business value than research innovation.
  • Complex deployment pipelines - In addition to the standard requirements you would need for a deployment pipeline, with MLOps you also need to test and validate data, data schema, and models. it can get even more complicated when trying to implement things like automating the process of retraining the model with fresh data and deploying the new model.

Implementing MLOps in Your Organization

Properly implementing an MLOps program is no easy task. Here is a high level overview of the process that can be used as a roadmap.

Establish current state and define objectives

As a first step you will need to evaluate how things are currently being done in your organization. Figure out current ML/AI practices like data management, model deployment, and monitoring. Establish what the current baseline metrics are for things like deployment time, model accuracy, and anything else relevant.

Once this has been done you can define objectives for your MLOps program so you can determine if you are moving in the right direction as you implement your MLOps system. Some common goals would be things like faster deployment times, improved model reliability and accuracy, and more frequent deployments. Having these objectives established will help guide future work.

Build the MLOps team

MLOps requires a blend of skills—data science, engineering, operations, and sometimes industry specific domain expertise. Assemble a team that combines these capabilities and have a plan for recruiting the talent needed if it isn’t available internally. This team will collaborate on designing, developing, deploying, and monitoring ML solutions, ensuring that different perspectives and skills are represented.

Determine data management and governance processes

Setting the requirements and standards for how data will be managed and the level of data governance needed to comply with regulatory requirements will impact your architecture significantly, so this is a key step in the MLOps implementation process. Things to consider here are data collection, storage, processing, and versioning. You will need processes in place for ensuring data quality and consistency and how missing or corrupted data is handled. For data governance you will need to ensure that you are following security and privacy best practices and following all regulations applicable in the jurisdictions you will be operating.

Select your MLOps tools and platforms

Once you have built your team, determined the objectives for your MLOps project, and have established the requirements and features needed to achieve those objectives, now you can start selecting the tools and platforms you will use to build your MLOps system. Here are a few key things to consider during this step:

  • Build vs buy - This is a classic dilemma faced by engineering teams, when to use an off the shelf service versus building a more bespoke solution. This choice will be different for each organization, but comes down to a balancing act of factors like cost, performance, desired system flexibility, and vendor lock-in risk. The high level summary is that platforms will generally let you get started faster but will be harder to customize for your specific use case and at scale may end up being more expensive in the long term.
  • Team experience - If your team has significant experience with certain tools, they will be productive faster and implementation will take less time. On the other hand, in some cases it might be worth the learning curve of adopting different tools.
  • Community - Using tools that already have a large community will make hiring and onboarding new team members easier and also makes finding solutions to problems easier if the community is active online.

Create automated deployment pipeline

Once all the planning and decision making is done, it’s time to start building. A typical starting point will be implementing things like CI/CD for testing new models in production, tracking performance, and gradually automating these tasks. The types of tools that can be used to make creating these features easier will be covered later in the article.

Iterate and improve

MLOps is an ongoing process, not a one time thing. The key here is to track your current status in relation to the objectives set at the beginning of the implementation process. This will help you prioritize effort on the parts of your MLOps system that still need to be improved. Once your initial objectives have been achieved you can set new goals and adjust as needed.

MLOps Best Practices

Maximizing the benefits of your MLOps implementation is made easier by following best practices in data management, model development and evaluation, as well as monitoring and maintenance. These techniques will help to ensure that your machine learning models are accurate, efficient, and aligned with your organizational objectives.

Exploratory data analysis

Exploratory Data Analysis (EDA) refers to the initial stage of analyzing data by visualizing, summarizing, and inspecting it to uncover characteristics and patterns. EDA helps in understanding the nature of data, identifying anomalies, discovering patterns, and making informed decisions about modeling strategies. It reduces the risk of making incorrect assumptions, which will help prevent your team from running in the wrong direction and wasting time.

An example of how exploratory data analysis can help a business would be how a data science team at a retail chain can look at sales data across different stores. By looking at things like seasonality, outliers, missing data, data volume, and sales distribution, the team can make an educated decision on the best modeling technique to use.

Feature engineering

Feature engineering involves transforming raw data into meaningful features that can be used to improve the performance of machine learning models. Feature engineering generally requires some domain expertise to help determine what data is most useful as model inputs. When used properly, feature engineering will improve model accuracy, reduce training time, and make model results easier to interpret.

Data Management

Creating a streamlined and reliable process for data labeling ensures high quality data for training models. This reduces the potential of incorporating biases or inaccuracy into the model. Model validation, on the other hand, ensures that the data used for training and testing is accurate and reliable, ultimately leading to better model performance.

Model training and Evaluation

Best practices in model development involve writing reusable code, simple metrics, and automated hyperparameter optimization to streamline the development process.

Model governance

Model governance refers to the set of practices used to manage and oversee machine learning models throughout their lifecycle. This includes things like version control, auditing, monitoring, and validation. The goal of model governance is to ensure ML models are effective and ethical in how they are being used

Model inference

Inference is when a model is used on previously unseen data to make predictions. This is where an ML model is expected to deliver real world value by producing accurate predictions. In addition to pure accuracy, model inference is a balancing act between cost and performance. Accurate results aren’t useful if the model takes too long to generate them or they cost more in computing resources than the value of the prediction.

Monitoring and Maintenance

Regular monitoring and maintenance of your ML models is essential to ensure their performance, fairness, and privacy in production environments. By keeping a close eye on your machine learning model’s performance and addressing any issues as they arise, you can ensure that your machine learning models continue to deliver accurate and reliable results over time.

Automated model retraining

Automated model retraining is the process of retraining machine learning models with fresh data, ensuring that the models remain accurate over time. While some models may not need frequent retraining, in certain domains with the world constantly changing around them, a model can rapidly become obsolete. By automating the retraining process, it becomes possible to deploy many ML models without worrying about them losing accuracy.

An example where model retraining has value would be fraud detection, where criminals are constantly developing new techniques as old techniques are blocked. If your ML model isn’t frequently updated with data showing new patterns, it will lose effectiveness over time.

MLOps Tools

A wide range of tools and technologies exist to support MLOps, from open-source solutions to commercial platforms. Let’s look at some of the most popular tools used for MLOps.

Jupyter Notebook

Jupyter is an open source interactive programming tool that allows developers to easily create and share documents that contain code as well as text, visualizations, or equations. For MLOps, Jupyter can be used for data analysis, prototyping machine learning models, sharing results, and making collaboration easier during development.


TensorFlow is an open source framework for building machine learning models created by Google. Once a model has been created TensorFlow has a wide ecosystem of integrations and extensions to help make running your model in production easier like the following:

  • TensorFlow Lite - Allows TensorFlow models to be run on mobile and embedded devices like smartphones or IoT devices
  • TensorFlowJS - Deploy machine learning models on the web using Javascript
  • TensorBoard - Tool for visualizing and tracking results during the model development process
  • TFX - TFX is a full platform for deploying TensorFlow models in production using pipelines


Databricks is a data analytics platform that provides cloud based environments for data engineering, collaborative data science, and business analytics. In MLOps, Databricks can be used to facilitate the full machine learning lifecycle, from data preparation to model deployment, with integrated tools for monitoring and governance.


PyTorch is an open source ML/AI library created by Facebook for building models. PyTorch is similar to TensorFlow, but has rapidly gained adoption in the research community due to a number of features that make it more developer friendly for experimentation. Within 4 years of release 75% of published research papers were using PyTorch and about 90% of published models on HuggingFace use PyTorch.


MLFlow is an open source platform that manages the complete machine learning lifecycle, including experimentation, reproducibility, and deployment. MLFlow provides a centralized place to track experiments, package code into reproducible runs, and share and deploy models.

AWS Sagemaker

SageMaker is a cloud service provided by AWS that allows users to build, train, and deploy machine learning models at scale. SageMaker offers capabilities for training on large datasets, automatic hyperparameter tuning, and seamless deployment to production with versioning and monitoring.


Feast (Feature Store for Machine Learning) is an operational data system for managing and serving machine learning features to models in production. Feast can help ensure that models in production are using consistent and up-to-date feature data, bridging the gap between data engineering and model deployment.


Prefect is a workflow management system designed for modern infrastructure and data workflows. For MLOps use cases, Prefect can be used to orchestrate complex data workflows, ensuring that data pipelines, preprocessing steps, and model deployments run reliably and in the correct order.


Pachyderm provides a data versioning and pipeline system built on top of Docker and Kubernetes. Pachyderm can be used to maintain data lineage and reproducibility, ensuring that models can be retrained and redeployed with consistent data sources, and any changes in data or pipelines can be tracked over time.


Kubeflow is an open source platform designed to run end-to-end machine learning workflows on Kubernetes. Kubeflow provides a unified environment for building, deploying, and managing scalable machine learning models. This helps to ensure seamless orchestration, scalability, and portability across different infrastructure.

Apache Airflow

Apache Airflow is an open source platform designed to programmatically schedule and monitor workflows. Airflow can be used to automate machine learning pipelines, ensuring that data extraction, preprocessing, training, and deployment processes run smoothly and on schedule.

Weights and Biases

Weights & Biases provides tools for tracking and visualizing machine learning experiments. For MLOps use cases, it offers capabilities to log hyperparameters, output metrics, and visualizations, ensuring that data scientists can quickly iterate and collaborate on model development.

Frequently Asked Questions

How is MLOps different from DevOps?

MLOps focuses on data management and model versioning, while DevOps emphasizes on the overall application performance, reliability, testing, and deployment automation. MLOps encompasses tasks such as data collection, preprocessing, modeling, evaluation, product deployment, and retraining into a unified process.

Is MLOps data engineering?

MLOps is a newer practice than Data Engineering, focusing on the deployment, monitoring, and maintenance of machine learning models in production environments. It emerged as a response to the unique needs of ML systems in data infrastructure management.

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