In contemplation of finally realizing the true value of Machine Learning, ML models must run in production and support efforts to make better decisions or improve efficiency in business applications.
MLOps (Machine Learning Operations) is the collection of techniques and tools used to deploy ML Models in production. MLOps is a core function of Machine Learning engineering, focused on the smooth running or well-organized process of taking machine learning models to production and maintaining and monitoring them. MLOps comprises data scientists, DevOps engineers, and IT.
Why Do We Need MLOps?
Machine Learning Operations (MLOps) allows organizations to reduce or ease numerous issues on the track to AI with ROI by providing a high-tech and professional foundation for controlling the machine learning lifecycle through instrumentation/hardware and robustness. Productionizing machine learning is difficult.
The machine learning lifecycle consists of many complex components such as data ingest, data prep, model training, model tuning, model deployment, model monitoring, explainability, and much more.
It also requires joint effort and hand-offs across teams, from Data Engineering to Data Science to ML Engineering. Naturally, it requires stringent operational rigor to keep all these processes synchronous and working in tandem. MLOps encompasses the experimentation, iteration, and continuous improvement of the machine learning lifecycle.
Benefits of MLOps
The primary benefits of MLOps are efficiency, scalability, and risk reduction.
Efficiency:
MLOps allows data teams to achieve faster model development, deliver higher quality ML models, and faster deployment and production.
Scalability:
MLOps also enables vast scalability and management where thousands of models can be overseen, controlled, managed, and monitored for continuous integration, delivery, and deployment.
Aims and Functions Of MLOps
● MLOps aims to unify the release cycle for machine learning and software application releases.
● It enables automated testing of machine learning artifacts (e.g. data validation, ML model testing, and ML model integration testing)
● It enables the application of agile principles to machine learning projects.
● It enables supporting machine learning models and datasets to build these models as first-class citizens within CI/CD systems.
● It reduces technical debt across machine learning models.
● It must be a language-, framework-, platform-, and infrastructure-agnostic practice.
MLOps Challenges
Over the years a lot of research is focused and concentrated on the full growth of the levels of MLOps and its adaptation to complete and fully automated pipelines. Several challenges have been identified in this regard and it is not always easy to overcome them.
A low maturity level system depends on the classical machine learning tools and techniques and demands the need of an exceptional team of data scientists, ML engineers, and front-end engineers.
Many tech-related problems spring up from this deviation and the lack of like-mindedness from one stage to another. The first and foremost challenge is the establishment of well-regulated pipelines with strong compatibility.
MLOPS Infrastructure Stack
The MLOps technology stack should include tooling for the following tasks:
● Data engineering,
● Version control of data, ML models, and code
● Continuous integration and continuous delivery pipelines
● Automating deployments and experiments
● Model performance assessment
● Model monitoring in production.
Components Of MLOps
The span of MLOps in machine learning projects can be as focused or expansive as the project demands. MLOps can encompass everything from the data pipeline to model production, while other projects may require MLOps implementation of only the model deployment process. A majority of enterprises deploy MLOps principles across the following:
● Exploratory data analysis (EDA)
● Data Prep and Feature Engineering
● Model training and tuning
● Model review and governance
● Model inference and serving
● Model monitoring
● Automated model retraining
Conclusion
MLOPS is a very expansive area. The key takeaways are as follows:
➔ MLOps People and their roles and responsibilities.
➔ Workflow of Development, Preparation, and Deploying of the Model and its challenges.
➔ Importance of Model Monitoring and Feedback Loop in MLOps.
➔ The strategy of Governance in MLOps.
➔ Overall understanding of MLOps Culture End-to-End.
On top of that, The ML implementation is something similar to working nicely in demo or test sites, and no guarantee it will work fine in production because in the real-time scenario models have to play with unseen data. So, making hands dirty on the green field is the real challenge here. And in recent years many more open sources have evolved and the list is growing.
Written by Syed Sumam Zaidi
