6 Reasons You Should Adopt MLOps
Artificial Intelligence adoption in enterprises is growing steadily. According to a recent survey, 35% of companies reported using AI in their business, and 42% are reportedly experimenting. Increasing AI adoption requires maintenance and monitoring of the Machine Learning models.
General Overview
What Is MLOps?
Machine Learning Operations (MLOps) is a set of processes that aim to track, deploy, and monitor Machine Learning models in production.
The core principles of MLOps include: Experiment Tracking, Monitoring, Versioning, Automation, and Reproducibility.
In this article, we will get familiar with the challenges that we face in our enterprise and how this has led us to adopt MLOps as a practice.
Why Use MLOps?
Machine Learning models in production need to go through a cycle involving steps like data preparation, model training, testing, deployment, and monitoring.
In our Machine learning team, there are lots of people: Research Scientists, Data Scientists, Data Engineers, and Data Analysts. This requires constant collaboration between different stakeholders and much more to produce better results. Thus, to deploy a model it is necessary to standardize the process and automate most of the steps.
Machine Learning development is an iterative process with lots of research. So, standardizing this process using MLOps is significant. In contrast, Software Development has standardized the use of DevOps to run, monitor, and improve the quality of SaaS products.
The same holds true for enterprises developing AI solutions that should progress towards MLOps adoption to improve Machine Learning models. MLOps enables seamless integration of the above processes aiming to continuously improve the ML cycle.
Reasons to adopt MLOps
1. Data Preparation
This is the first step to training any Machine Learning Model. The data on which the model is trained can have a significant impact on the model's performance.
“Data is the new oil. Like oil, data is valuable, but if unrefined, it cannot really be used.” — Clive Humby
This holds true because Data Preparation involves a many steps like: Data Ingestion, Data Cleaning, Data Transformation, and Data Analysis. Automating these steps makes things easier.
Data plays a major role in the way a model behaves in production. So, automating the data pipeline is our primary goal. Our data pipelines process thousands of customer documents every day which helps us maintain a clean training database.
2. Model Training
Model training seems like a straightforward process when there is a single pipeline performing multiple tasks. DiliTrust has Machine Learning models tailored to fit various business needs. For instance, the Contract Lifecycle Management (CLM) module extracts different types of information (clauses, entities, document type, etc..,) from each contract. This results in multiple pipelines, performing multiple tasks, which adds to the complexity of the system. So, tracking the parameters used for model training is indispensable.
Some notable parameters include the model configuration (epochs, model type, pipeline) as well as the code and dataset versions.
3. Model Testing
Developing a model is an iterative process and Data Scientists can spend a lot of time testing it. Reproducibility and interpretation of results would be a great chaos. Tracking the model configuration, pipeline, and results would take a big chunk of the Data Scientist's productive time. Thankfully, MLOps solves this as we centralize the results of each experiment run, providing better accessibility.
4. Deployment
Deployment is a process where the ML model trained on production data is served using API to users. Automating model deployment is key as manual deployments are prone to errors. This version of the models, hyper-parameters, train, and test data version, and ML artifacts.
CI/CD pipeline automation ensures reliable deployment. It launches the automated model training pipeline and runs some tests before deploying the ML Model and related artifacts.
The deployed model might not deliver the expected performance every time. So, implementing a rollback mechanism is as important as having an automated deployment.
5. Monitoring
Production models are at risk of degradation because of changing data. Experimental and production model monitoring is crucial to achieving continuous improvement. Every week the Machine Learning team trains different models which are then deployed to production. So, keeping track of each model is important.
Production models are associated with multiple business metrics which evolve over time. Monitoring the changing metrics and their performance helps meet customer expectations.
6. Scalability
In the early stages only a limited number of people worked on a model at once but as the team grew the collaboration between different stakeholders of the model became complex.
Besides, as discussed earlier, model tracking and benchmarking across different teams need centralized storage.
MLOps Metrics
As a bonus, here are a few metrics that are relevant when doing MLOps.
Deployment Frequency
MLOps Automates the data pipelines and replaces the manual deployment with CI/CD pipelines. This should reduce the time taken to deploy a new model in production.
Model Training Duration
ML model training includes data preparation, running experiments, and inferring results. With the experiment tracking and reproducibility in place, Data Scientists spend less time in model training as most manual steps are eliminated.
Rollback Time
Monitoring the ML model performance helps to detect possible performance degradation. In that case, versioning provides the flexibility to roll back to the previous version.
Model Quality
Model quality can be measured by comparing the metrics between the current production model and newly trained models. This ensures there is no model drift introduced by the new version.
Conclusion
Machine learning aids businesses to develop solutions to problems that were once impossible; thereby saving time, and improving efficiency by leveraging the data for decision-making, to improve customer experience. So, having MLOps is essential as faster model deployment helps enterprises stay ahead of the competition in the growing market.
Now that we have discussed the reasons for adopting MLOps. There are many tools available in the market, which we will discuss in future articles.
So, thank you for reading 👀 and stay tuned for the next one!
Author: Subaandh V K
