MLOps: The Epoch of Productionizing ML Models
If You have Similar questions like “What is MLOps?”, “Why One Should adopt MLOps?”, “DevOps vs MLOps What are Differences and when to use which?”, “How to Adopt MLOps?”.
Then you are landed at the right place.
This Blog I am breaking into two parts
So let’s Start with Part 1: Originate with MLOps.
MLOps (a compound of Machine Learning and “information technology OPerationS”) is new discipline/focus/practice for collaboration and communication between data scientists and information technology (IT) professionals while automating and productizing machine learning algorithms.
What is MLOps?
In simple terms, MLOps(DevOps for ML) is the Machine learning equivalent of DevOps. While DevOps helped optimize the production lifecycle of Big Data project, MLOps seeks to solve the problems associated with the implementation of ML in production.
From DevOps to MLOps:
Why adopt MLOps?
For businesses implementing ML, MLOps should be a top priority. It can streamline business processes in the following ways:
- MLOps can take care of regulatory and compliance issues. While it introduces the Operations team to the latest regulations and best industry practices, it allows the Data Science team to focus on building and deploying creative ML models.
- Machine Learning Operalization helps reduce the gap between discovering insights and turning those insights into actionable business plans.
- By allowing Data Scientists and Operations teams to work together, MLOps combines the power of business knowledge with data. This encourages data-driven business operations.
How is MLOps different from DevOps?
- Data/model versioning != code versioning
- Retraining ability requires on-demand as the models decay over time.
- Model reuse entirely has different case than software reuse, as models need tuning based on scenarios and data.
- Fine-tuning is needed when to reuse a model. Transfer learning on it, and it leads to a training pipeline.
How to Adopt MLOps?
For fault tolerance and iterative filtration of ML models is essential, and for that reproducibility is needed. Repeatability required to illuminate the source of variation like –
- CPU multi-threading
- Non-deterministic GPU-floating point calculation
- Inconsistent hyper parameters
- Shuffling of datasets
- Noisy hidden layers
- Change to model architecture
- Random initialization of layers weights
- Change in ML frameworks
ML Operations:
Controllability
When it proceeds to establish the production updates it’s really difficult in ML pipelines as there is not only the origin code that changes in the pipeline but when a new retrained model is selected by human approval or some advance auto-selection method, The new changes should be done with proper control of not having any instability or downtime of ML applications.
Automation
ML pipelines are code, and DevOps toolchain pipeline plays an essential role in MLOps. The source code repository automation facilitated by Jenkins and orchestrators such as AirFlow is a classic example. But when it comes to ML pipelines, There are interdependencies like Model Approval and Drift Detection. These additional dependencies need to be integrated with the DevOps toolchain pipeline.
Model Supervision
Model Management is the core of MLOps. When it comes to managing complex pipelines where a large number of models, objects, and training pipelines are generated MLOps needed.
Model versioning
Making changes reversibly is necessary for production for stability and fault tolerance. Unlike source code versioning of ML models is an additional step from traditional pipeline.
Model tracking
Complex pipelines of models lead to many models runs in the pipeline like in case of ensembles models runs. Creating this many experiments to select the best champion or challenger model requires model tracking. There are different kinds of tools like MLflow available for tracking models, or one can build a custom pipeline according to the use case.
ML Monitoring
Monitoring in ML systems is not just checking the uptime of different services and resources/compute its taking. As in production, ML brings things to monitor that is directly related to the success of business outcomes.
ML data drift
The change in the relationship between input and output data with time called data to drift. The data drift analysis should be in the ML monitoring. The observability of data drift is essential to analyze if retraining requires any changes in the configuration of ML models. That is the reason why inference monitoring is needed. Let’s discuss inference monitoring in brief.
Inference Monitoring
Continuously monitor the inference and observe if it is behaving according to the expected bounds. This monitoring tells about mismatching of input and output data which detects data drift, and it also provides the performance of ML model using which performance analysis and comparison of the models is possible.
Benefits of Enabling MLOps:
- It provides the advantage to strap and increase investments in existing machine learning and data science tools and technology to the maximum and create a consolidates, a system of maintaining the records in between different teams and different projects
- It helps to manage and maintain the partnership of data science and IT/Ops teams to work together to provide ML-powered applications which can provide some values in results.
- It helps to minimize the hazard to the organization by regularizing and importing in place hefty governance checklist and balances, and by enabling the use of best practices for machine learning models at the production level.
- Scale ML Models initiatives broadly by swiftly and flawlessly converting the true potential of machine learning into business processes (which already exist) and systems (which exist across the enterprise)
That’s all about this part in part 2: MLOps in Azure
- Introduction: MLOps in Azure
- Overview
- Azure MLOps features
- Create a project in Azure DevOps with CI/CD for MLOps
Lets catch up at part 2
Thank You
