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What is MLOps?
A collection of methods, tools, and approaches are considered for a machine learning project to be successful and MLOps is a wide phrase that encompasses these approaches. Machine Learning Operations (MLOps) is a set of methods where data scientists and operations experts come together to collaborate and communicate.
It's a machine-learning version of DevOps that's been tweaked to meet various ML components, such as changing data and the addition of new development jobs, such as ML engineers and data scientists. It's gradually becoming a stand-alone method for ML lifecycle management.
Data collection, model generation, continuous integration/continuous delivery, orchestration, deployment, diagnostics, model serving, and business KPIs all fall under this umbrella.
What problem does MLOps solve
MLOps is a collection of tools and strategies aimed at making the life of data scientists and machine learning practitioners easier. It acts as a road map for individuals, small teams, and even corporations to achieve their objectives regardless of their constraints, such as sensitive data, limited resources, or a limited budget.
Version control can be applied to data used in model training as well as other model artifacts. Models and data are versioned to guarantee that machine learning experiments are reliable.
Models that have been put into production have the potential to degrade over time due to discrepancies between training and testing data. Data drift is the term used to describe this phenomenon. These flaws can be promptly discovered and remedied by monitoring a model's performance.
Adding features is a time-consuming and computationally intensive process. When MLOps approaches are implemented correctly, features that are created once can be reused as many times as needed. This allows the data scientist to concentrate on the model's design and testing.
Phases in MLOps
MLOps is made up of several components that work together to keep the ML model development lifecycle running smoothly.
Data and Model Versioning − The process of naming numerous iterations of a model that are developed at different stages of ML development is known as versioning. It primarily helps in re-tracking a model to a previous iteration when faced with an adversary.
Model Registry − A model registry is a central repository where model developers can easily submit production-ready models. Developers can use the registry to collaborate with other teams and stakeholders to manage the lifespan of all models in the company.
Model Serving − Model serving simply implies hosting machine-learning models (on-premises or in the cloud) and making their functions available via API so that applications can integrate AI into their systems. You can use the machine learning model with just a few clicks with this activity.
Model Monitoring − It's crucial to keep an eye on your model once it's been put into production. It enables you to detect and resolve issues like as low prediction power, parameter changes, and inadequate generalization, resulting in a high-quality solution with remarkable performance.
Continuous Integration/Continuous Delivery (CI/CD) − In machine learning, continuous integration and continuous deployment ensure that high-quality models are developed and released often. Continuous delivery ensures that code is often merged in a central repository with automated builds and tests.
Model Deployment − The task of exposing an ML model to real-world use is known as model deployment. The word is frequently used interchangeably with the concept of making a model available via real-time APIs.
How to implement MLOps
MLOps can be implemented in three different ways. The best option depends on the size of the company and the number of ML models it needs to run.
They are as follows −
Manual process (MLOps level 0) − ML workflows are purely manual here, as the name implies. This is a common approach among businesses that have recently begun to use machine learning. This method of adopting MLOps is suitable for non-technical businesses such as insurance companies and banks, who upgrade their models once a year or during a financial crisis.
ML pipeline automation (MLOps level 1) − At this level, machine learning pipelines are automated to achieve continuous training of ML models. This procedure entails automating new data training, model retraining in production, data and model validation automation, pipeline triggers, and the storage of machine learning model metadata.
CI/CD pipeline automation (MLOps level 2)− A strong automated CI/CD pipeline that allows data scientists to explore new ideas related to hyper parameter tuning, feature engineering, and model architecture is required for quick and accurate updates on the ML pipeline. This level is appropriate for businesses that retrain their models daily and re-deploy them on several servers at the same time. Without the use of MLOps, it will be difficult for these businesses to thrive.
Benefits of using MLOps
Following are the advantages of using MLOps −
MLOps allows teams to do rapid innovation.
Teams can have better control and administration of machine learning resources.
The complete machine learning lifecycle gets effectively managed using MLOps.
It lets teams deploy high-precision models quickly and easily in any place.
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