Introduction to MLOps
The era that undoubtedly belongs to Artificial Intelligence results in machine learning in almost every field. Whether it is healthcare, business, or technical, it is everywhere. The availability of the latest ML tools, techniques, algorithms, and platforms to develop Machine Learning Models to solve a problem is not a challenge. The real challenge lies in the maintenance of these models at a massive scale. This blog will give an insight into Productionizing Machine learning models with MLOps Solutions. The new chasm of the development process of ml involves the collaboration of four major disciplines - Data Science, Data Engineering, Software Engineering, and traditional DevOps. These four disciplines have their level of operations and their requirements with different constraints and velocities.
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What is MLOps?
It is the communication between data scientists and operations teams. It has mixed data scientists and services designed to automate ML pipelines and gain more valuable insights into production systems. It provides reproducibility, visibility-managed access control, and computing resources to test, train, and deploy AI algorithms to Data engineers, business analysts, and operations teams.
Why is it important?
It is pretty clear from the above content that there is a need for it, which led to the rise of this hybrid approach in the modern era of artificial intelligence. Now, moving forward from ‘What’ to ‘Why.’ Let us shed some light on the reasons that led to its use in the first place.
Orchestration of multiple pipelines
The development of machine learning models is not a single code file task. Instead, it involves combining different pipelines, each with its own role. Pipelines for the primary process, such as pre-processing, feature engineering model training, model inference, etc., are involved in the big picture of the ML model's development. Orchestrating these multiple pipelines to automatically update the model is essential.
Manage Full Life Cycle
The ML lifecycle model comprises different sub-parts that should be considered software entities individually. These sub-parts have their own need for management and maintenance, which DevOps often handles, but managing them using traditional DevOps methods is challenging. A newly emerged technique that includes people, processes, and technology gives an edge to swiftly and safely optimize and deploy ML models.
Scale ML Applications
As said earlier in the topic, the development of models is not an issue to be worried about, and the real problem lies in managing the models at scale. Managing thousands of models at once is a very cumbersome and challenging task that tests the performance of the models at scale. With its use, it is a natural scale that manages thousands of pipelines of production models.
Maintain ML Health
Maintaining ML health after deploying ML models is the most critical part of the post-process. It is vital that ML models can be operated and managed flawlessly. It provides the latest ML health methods by enabling the automated detection of different drifts (model drift, data drift). It can also allow the system to use the latest edge-cutting algorithms to detect these drifts so that they can be avoided before they start to affect ML health.
Continuous Integration and Deployment
Continuous Integration and Deployment is one of the sole purposes, which led to DevOps' use in any software product development procedures. But due to the scale of the operability of ML models, it is difficult to use the same methods of continuous integration and deployment, which are used for other software products. It can provide the hands to use different dedicated tools and techniques that are specialized to ensure continuous integration and deployment services in ML models.
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Model Governance
Under Model Governance, rich model performance data can be provided by applying it to monitor attributes on a massive scale. It can also allow snapshots of the pipelines to analyze critical moments. The logging facilities and audit trails under it can also be used for reporting and continuity of compliance.
What are the Challenges of Productionizing ML Models?
The common challenges organizations face while productionizing the Machine Learning model into active business gains are listed below.Dataset Dependency
Feeding the data to training and steps done at the evaluation stage in the data scientist sandbox can dramatically vary in real-world scenarios. Depending on the use case, data changes with time and lack of regularity cause poor performance of ML models.Simple to complex pipelines
Training a simple model, putting it into inference, and generating predictions are simple ways of getting business insights. This is usually a manual offline training, which uses the trained model to generate inference. But mostly in business problems, this is not sufficient. In real-world cases, regularity is needed; with time, models need retraining on new data. A retraining pipeline must be added to the system to get the latest data from the Data Lake frequently. Many models will be in the retraining pipeline, and human approval is needed to decide which model to choose for production. In other cases where ensemble models are used to improve accuracy, multiple training pipelines are used, and in the Federated pipeline, it becomes even more challenging to maintain.Scalability Issues
There are scaling issues at different development levels, and Even if the data pipeline is developed in a scaled way, issues come while feeding the data to ML models. Because ML models are built in a Data scientist sandbox, it was not developed to take scalability in mind; rather, it was developed to get good accuracy and the right algorithm. Building different types of ML frameworks to use, and each has its scaling and opportunities issues. On the hardware side, Training a complex neural network requires a powerful GPU and simple ML models can be processed on a cluster of CPUs.
Production ML Risk
The risk of ML models not doing well is continuous and needs continuous monitoring and evaluation if they are performing within the expected bound. On live data, metrics like Accuracy, Precision, recall, etc., cannot be used as live data does not have labels. Different methods, such as data deviation detection, drift detection, canary pipelines, and production A/B tests, should be used to ensure the health of ML models.In 2022, Organizations expect to have an average of 35 AI or ML projects in place. Source: Gartner Inc
Process and Collaboration
ML requires multiple abilities to handle production-grade ML systems, such as data scientists, data engineers, business analysts, and operations in production. Different teams will focus on various outcomes. A data scientist will improve accuracy and detect data deviations, and business analysts want to enhance KPIs. The operations team wishes to see uptime and resources. Unlike the data scientist sandbox, the production environment has many objects like models, algorithms, pipelines, etc., which are difficult to handle, and versioning these is another issue. Object storage is needed to store the ML models; a source control repository is not the best option.
How is it different from DevOps?
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Data/model versioning != code versioning
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Model reuse has a different case than software reuse, as models need tuning based on scenarios and data.
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Fine-tuning is needed when reusing a model. Transfer learning on it, and it leads to a training pipeline.
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Retraining ability is in demand as the models decay over time.
MLOps in Azure
Azure MLOps for ML enables data science and IT teams to collaborate and increase model development and deployment speed while monitoring, validating, and governing machine learning models.-
Training model for reproducibility with advanced tracking of datasets, experiments, and code.
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Autoscaling, no-code deployment, powerful managed-to-computer, and tools for quick model deployment and training.
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Efficient workflows with scheduling and management capabilities to build and deploy with CI/CD.
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Advanced capabilities for governance and control objectives and promote model transparency.
MLOps in AWS
AWS MLOps (Machine Learning Operations) helps streamline and enforce architecture best practices for ML model production. The extendable framework provides a standard interface for managing ML pipelines for AWS ML services and other services. AWS template allows customers to upload their trained models, configure the pipeline, and monitor their operations. This increases the team's agility and efficiency by enabling them to repeat successful processes at a large scale.
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Initiates a pre-configured pipeline through an API call or a Git repository
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Automatically deploys a trained model and provides an inference endpoint.
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Supports running integration tests to ensure the deployed model meets expectations
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Allows multiple environments to keep the Machine Learning model's life cycle.
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Notifies users about the pipeline outcome via email.
MLOps in GCP
Data scientists and ML engineers are trying to apply DevOps principles to ML systems. It is an ML engineering practice that aims to unite Machin Learning system development and ML system operation. It helps automate and monitor all ML system construction steps, including integration, release, deployment, infrastructure management, and testing.
Characteristics of MLOps GCP (Google Cloud platform):
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Rapid experiment: ML experiment steps are orchestrated, which automates the transition between steps and leads to the rapid iteration of experiments and better production readiness.
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Experimental-operational symmetry: The critical aspect of its practice for uniting DevOps is implementing a pipeline used in the development or experiment environment or the preproduction and production environment.
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Continuous delivery of models: An ML pipeline in production continuously delivers services to new models trained on new data. The model deployment step is automated, which serves the trained and validated model as a prediction service.
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Pipeline deployment: This helps deploy a trained model as a prediction service for production. The trained pipeline is deployed automatically and recurrently to serve the trained model.
What are the Workflows of Machine Learning?
The workflow of an ML project includes all the steps below to build the proper ML project from scratch.
Reproducibility in ML models
For fault tolerance and iterative filtration of ML models, reproducibility is essential. Repeatability required to illuminate the source of variation like --
Inconsistent hyperparameters
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Change to model architecture
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Random initialization of layer weights
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Shuffling of datasets
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Noisy hidden layers
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Change in ML frameworks
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Cpu multi-threading
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Non-deterministic GPU-floating point calculation
This capability becomes very important as distributed training occurs on a cluster of GPUs with advanced models and live data streams. Start packaging ML models to support reproducibility. Several tools are available, or a custom tool can be developed according to the use case. This tool should package the model and then be ready to deploy it on a platform according to the use case — the best way to package it is by using docker in a containerized environment.
Feedback
The model monitoring system should analyze a feedback loop to generate feedback on the model's performance. It should check whether a model is performing poorly due to data drift.The system needs continuous learning and training from the real world. Click to explore about, DevOps for Machine Learning , Tensor Flow and PyTorch
ML Operations
Controllability
Controlling production updates is difficult in ML pipelines as not only the source code changes in the pipeline but when a new retrained model is selected by human approval or some advanced auto-selection method, the new changes should be made with proper control to prevent any instability or downtime of ML applications.Automation
ML pipelines are code, and the DevOps toolchain pipeline plays an essential role. A classic example is the source code repository automation facilitated by Jenkins and orchestrators such as AirFlow. However, when it comes to ML pipelines, there are additional challenges that the typical traditional toolchain cannot address. Like ML pipelines, which can run parallel multiple pipelines, there are interdependencies like Model Approval and Drift Detection. These additional dependencies need to be integrated with the DevOps toolchain pipeline.
Model Management
Model Management is its core. MLOps are needed to manage complex pipelines that generate a large number of models, objects, and training pipelines.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 the traditional pipeline.Model tracking
Complex pipelines of models lead to many model runs in the pipeline, like in the ensemble models run. Creating this many experiments to select the best champion or challenger model requires model tracking. Different kinds of tools, like MLflow, are 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 it's using. ML brings things to monitor that are directly related to the success of business outcomes, as in production.ML data drift
Data drift is the change in the relationship between input and output data with time. The data drift analysis should be done using 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
Monitor the inference and observe if it behaves according to the expected bounds. This monitoring detects mismatching of input and output data, which is data drift. It also provides the performance of the ML model, using which performance analysis and comparison of the models are possible.
Best Practices for Productionizing ML Models
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Model Versioning:
Track different versions of models to ensure reproducibility and easy rollback if needed. Use tools like DVC or MLflow for version control.
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Automation of Model Deployment:
Automate the deployment pipeline to minimize manual errors and ensure smooth updates. CI/CD pipelines should be implemented for model testing, validation, and deployment.
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Scalability and Performance:
Ensure that the model can scale with growing data and handle production traffic. Leveraging cloud infrastructure or containerization to scale the model efficiently (e.g., Kubernetes, Docker).
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Monitoring and Logging:
Continuously monitor model performance in production. Track metrics such as accuracy, latency, resource usage, and drift over time. Set up alerts for abnormal behaviours.
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Model Explainability:
Incorporate explainability tools (like SHAP or LIME) to ensure that model predictions are interpretable, especially for sensitive applications (e.g., finance or healthcare).
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