The Crucial Role of MLOps in Enterprise Machine Learning
Scaling AI Seamlessly: MLOps Explained
Introduction
If we talk about Machine Learning Operations (MLOps) at it’s core, it’s a paradigm that bridges the gap between machine learning (ML) and traditional software development(DevOps). It’s essentially applying DevOps principles and practices to the machine learning workflow.
Once the Data Scientists are done with the development process, the trained model is sent for processing to the IT/Operations team. It’s easy to see why this process is challenging. It’s time consuming, manual, inflexible, not reusable and error prone.
As organizations increase the use of machine learning to drive innovation and efficiency, the need to streamline the end-to-end ML lifecycle becomes paramount. MLOps steps in as the solution, promising to enhance collaboration, efficiency, and reliability in deploying, managing, and scaling machine learning models.
The Goal of MLOps
From a high level point of view the goal of MLOps is rather much simpler.
- Faster Experimentation and Model Development
- Faster Deployment of Updated Models into Production
- Quality Assurance
MLOps aims to provide a seamless handoff between the Data Scientists and the ML Engineers or Developers.
Why do we need MLOps?
As the data increases, so does the automation challenges like monitoring, data & model governance etc.
As shown in the following diagram, only a small fraction of a real-world ML system is composed of the ML code. The required surrounding elements are vast and complex.
Let’s start with a typical machine learning workflow:
Data Collection
The ML Workflow always starts with getting quality data and we get it from different sources. The better the data, the better is the model.
This is the first need for MLOps i.e versioning the source data and attributes in order to kind of track the lineage of the data from which you built the model
Model Building
Building a model is an iterative process. We need to undergo a lot of trial and update in order to achieve the metrics that are well suited for your business model.
All the iterations that don’t make it to the desired results can be considered relatively important as they push the model towards trying new sets of combinations. This is where the next need for MLOps comes into the picture. MLOps can help to track the metrics or experiment runs so that one can narrow it down what attriutes and hyperparameters needs to be tweaked further.
As this requires coding, there needs to be source control on the code for reproducibility purposes. It also gives the option of Checkpoints steps in the lifecycle so we don’t have to build the repetitive code from ground zero.
All these steps from experimentation to optimization is contained in a pipeline as a part of MLOps
Deployment
After the model development, it’s deployed in order for users and applications to leverage it.
But before deployment it’s important to validate it. MLOps can help achieve this staged deployment in an automated and predictible way, where the models are packaged into containers in order to run anywhere.
Monitoring
Once the model is in production, it doesn’t end there. The job of MLOps now shifts to monitoring the model. As a business grows and changes, so does their needs and the data, this can give rise to Data Drift and Model Drift.
For example if the company was detecting credit card fraud, and it evolved it’s thinking to around what is a fraudulent transaction. Apart from models, the data on which the model is trained can be a subject to demographic change, consumer preferences or simply addition of new products, giving rise to Data Drift.
This concept of drift is monitored by MLOps which automates the retraining of the models according to the needs, catering to the new requirements.
What is MLOps and how it accomplishes these tasks?
At it’s core MLOps is a process that enables the Data Scientists and IT/Engineering teams to collaborate and increase the pace of:
- Model Development
- Continous Integration + Deployment (CI/CD)
- Monitoring
- Validation
- Governance
From what we’ve discussed we can say that MLOps is a process that you engage in and it’s depth depends upon the business needs that you’re working on.
Let’s take a look at how we can accomplish these MLOps process-
Creating Reproducible ML Pipelines-
Machine Learning pipelines allows you to produce reusable and reproducible code and hence also has the checkpointing abilities we touched upon earlier. MLOps also enables resuable ML Environments, also targeting the reusability.
Register, Package and Deploy Models-
It helps with maintaining a centralized repository of models
and deployments irrespective of where they were created. A CI/CD pipeline automates the testing and deployment of machine learning models, reducing manual errors and speeding up the development cycle. It ensures that new changes are thoroughly tested before going to production.
MLOps can help you to track the associated metadata required to use the model.
Monitoring and Logging-
Monitoring tracks model performance, data drift, and system health. It helps compare model inputs between training and inference explore model-specific metrics, and provide monitoring and alerts on your ML infrastructure as well.
Logging captures relevant information for debugging and auditing. This log information can help us identify who published the model, why were the changes made etc. Alerting helps identify and respond to issues promptly.
Conclusion
In conclusion, MLOps represents a transformative approach to harnessing the full potential of machine learning within organizations. By seamlessly integrating development, deployment, and monitoring processes, MLOps empowers teams to not only build robust models but also ensure their sustained performance and relevance.
As organizations strive to deliver AI solutions at scale while maintaining agility and reliability, the MLOps revolution is set to redefine how machine learning projects are executed and optimized, ultimately propelling us into a new era of technological innovation.
