MLOps refers to a set of practices that aims to design production frameworks that can make developing and maintaining ML models fast, seamless and efficient. MLOps is a relatively new term that answers questions like:

• How do we move from the exploration stage to a fully-functional solution that is stable enough to deploy in production?
• How do we make the solution scalable and easy to support?
• What should we do after deploying our solution?
• How do we keep track of the predictions and feed them back into the model for improvement?

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MLOps comes from the necessity to create machine learning models that will be used extensively and, therefore, need a framework to keep them functioning and up to date. For this, MLOps uses a set of practices at the intersection of data engineering, data science and DevOps.

Data engineering practices are aimed to make data easily accessible and clean for machine learning models. Data science skill sets help in creating models, all along the process from design to test. Finally, DevOps practices are used to deploy and monitor these models in an automated manner, so that the model can be improved with newer results or retrained when results deviate from ideal.

However, putting all these practices together in a big bowl and mixing them will not create a good framework. These skills are better explained if we follow the life cycle of a machine learning model.

MLOps comes from the necessity of creating machine learning models that will be used extensively, and, as a result, presents a set of practices that helps productionizing them in a context of system development.

Using MLOps will give you advantages such as:

• Implementing this framework into the development of machine learning models gives everyone a well-maintained, reproducible and scalable model, alongside the governability and regulatory compliance at the same time.
• The monitoring part of MLOps helps keep track of every single process, from data acquisition through model creation to deployment. This help in automating updates of the model following new data trends. Especially since all these steps are set automatically, the development process takes a shorter time.
• MLOps framework gives a business advantage, since the developed machine learning model will be used and maintained automatically, and the money and time invested in it will pay back fast.

Xomnia's experts created an MLOps canvas used for describing and visualizing production systems with machine learning. It covers a wide range of aspects that have to be taken into account to sustainably run machine learning applications in production. You can read more about it by clicking here.

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As DevOps defines the systems development life cycle and gives guidelines on how to join development and operations, MLOps defines the machine learning life cycle and builds on top of the DevOps guidelines by adding steps related to ML models. This requires a normative, problem-solving perspective, and a focus on automation and scalability.

The machine learning life cycle can be broken down per the following:

• Data: The cycle starts with the data, which needs to be preprocessed and ready to be used by the model.
• Machine learning model: The next step is creating a machine learning model that is optimized while keeping track of its performance for reproducibility.
• Deployment: After accepting the model, it is deployed into production for usage.
• Monitoring: Lastly comes monitoring the model. This step is fundamental for MLOps and is what differentiates a successful ML model from others.

In the following section, we will dig deeper into each of these steps and explain the best practices that will make your ML model shine. Additionally, we will mention tools and frameworks that can help you perform them. However, some of these tools have many more features that may spread throughout the life cycle. Furthermore, there are frameworks that try to incorporate the whole life cycle in one place, such as H2O, ZenML, Databricks, AWS Sagemaker and Azure ML.

All good machine learning models start with good data. To prepare data for an ML model, a series of pipelines needs to be created. These pipelines ensure not only the constant ingestion of the data and its proper cleaning and wrangling, but also, depending on the model, the data can be automatically labeled and split into training, validation and test sets.

Moreover, exploration and validation of the data needs to be performed. Exploration will show you the data structure and its content, while validation is important to ensure the data quality. This can be done by applying specific tests that the data needs to pass. This is further explained in the monitoring step, since it is the closing step of the machine learning life cycle.

Once the data is ready, a machine learning model needs to be selected and optimized. This requires experimentation on different models and on different hyperparameters within a model. To ensure reproducibility, these experiments are stored in a registry where tags are allowed in order to select the desired ML model that will be used for the next step.

Each experiment has 3 main components fundamental to the data science development:

• The data: This section defines everything that the model needs in order to be trained and tested. It can have either a copy of the data set that was used, or some kind of reference to it.
• The model: The final model needs to be stored. This can be done, for example, by storing the SKLearn model or pipeline. The hyperparameter selection is also stored alongside.
• Testing: Not only testing the model is important, but also testing incoming data to ensure good performance. The results of all these tests should be stored for future reference.

Keeping the registry of experiments not only ensures reproducibility and scalability, but also ensures governance and regulatory compliance. When using ML models, it is important to be able to show which code was used for the model, when it was trained, with which data set and parameters it was trained, which tests did it pass or fail and who is responsible for it. This registry can be kept at frameworks such as ML Flow, Neptune and Weights & Biases.

Once the model passes all the tests, it can be taken into production. For this, MLOps uses similar guidelines than DevOps, since the model can be seen as a system or application. During this stage, the model will be configured and prepared so the end-user can access it.

To deploy a machine learning model, it needs to be first properly packaged for usage. It is important to save within the package all necessary information of the model, such as the code version, model version, data that was used to train the model, and additional metadata necessary for governance and legal requirements. After obtaining the package, it can be deployed so it reaches the end-user. The specifics of the packaging and deployment depend on each use-case and for it there are a variety of frameworks available such as BentoML, Seldon or Kubeflow.

After deploying an ML model, a good MLOps framework should be able to constantly monitor how the model and system performs. Monitoring is embedded in all engineering practices, and MLOps is no different. Not only monitoring the expected behavior, but also setting standards that models should adhere to, is fundamental to MLOps.

Some metrics that need to be checked when monitoring a machine learning model include:

• Data metrics: They monitor data drift, which occurs when the input data changes distribution for example due to a natural process or a global dynamic. Additionally, they measure the quality of the data, both as input and output of the model, and monitor the preprocessing of data according to standards.
• Technical metrics: They refer to the boundaries of an ML model, such as the model’s latency (the time taken to predict one data unit) or data throughput (the number of data units processed in one unit of time).
• Performance metrics: These are measurable aspects, such as the model’s accuracy, against model drift. Model drift occurs when the concepts change, for example when the definition of spam email changes over time. Additionally they create a reference for future progress and improvement.
• Functional metrics: Also known as application or infrastructure metrics, they measure, for instance, the infrastructure health, application connections, and others affecting operational tasks.

Moreover, a detection system can be built on top of the metrics in order to automate model retraining. For example, if the model starts to drift, the system can trigger a retrain process. Furthermore, if the system or application changes its need regarding storage capacity or computational power, the model can be scaled up or down. Some tools that can be used to monitor both the model and the data are Arthur, Arize, Comet, Evidently and WhyLabs.

The monitoring step closes the life cycle of machine learning by reincorporating feedback into the model and deployment.

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Continuous Integration and Continuous Delivery (CI/CD) are among the core pillars of DevOps and thus also MLOps development. With MLOps, one can implement new features and upgrades into the solution while at the same time having checks if those modifications are compatible with the running model to avoid its disruption.

To implement CI/CD in MLOps, the following tests are useful:

• Code quality: It should be assessed every time the code is modified. There should be an embedded mechanism checking the code quality and code coverage before it is pushed onward to the production servers.
• Functional testing: The main goal is to test if a new feature of the model performs as desired before it is actually deployed to production.
• Integration testing: Deployment involves integrating new functionalities to a working production model on a constant basis. It’s absolutely critical in order to avoid issues that could disrupt the model within the deployment system.

Though version control for code is standard procedure in software development, machine learning solutions require a more involved process. This entails versioning the following artifacts:

• Code versioning: It is the best practice for periodically saving and labeling code to make the development process reliable and reproducible.
• Data versioning: It is important for storing and labeling data which the model uses. Training the same algorithm with the same configuration on a different data will result in a different model. Hence it’s crucial for reproducibility.
• Model versioning: It means saving configuration files and model artifacts. It should be done every time the model is modified. Besides the reproducibility purposes, model versions are often used as reference for debugging or governability.

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