This article discusses Machine Learning Model Operationalization Management (MLOps) tools to develop and deploy a food recipe application. It lists the tools that are framework-, platform-, and infrastructure-agnostic using Python for development. To find the ideal tool for the MLOps task, a decision-making and analysis process is employed. The goal is to design an end-to-end food recipe application using MLOps. And by the end of this article, we will be able to:
- Have value proposition of the application for our customers
- Find out how MLOps can benefit us
- Visualize the end-to-end workflow for MLOps
- Collate MLOps Tools in one picture
- Understand reasoning behind selection of MLOps tool for individual tasks
The end goal is to build an end-to-end pipeline. Starting from gathering the data through to training, evaluation, and inference processes. The final step involves deploying the model.
Business Use-Case
Business Model Canvas (BMC) provides an overview of the project. BMC offers a strategic management tool to define and visualize the project. It includes key business activities and their relationship to our value proposition.
Business Model Canvas for Food Recipe Application
Benefits of MLOps
MLOps combines Machine Learning (ML), DevOps, and Data Engineering into one discipline. The aim is to standardize and streamline the continuous delivery of high-performing models in production by deploying and maintaining machine learning systems in production. It helps to:
- Reduce maintenance costs while increasing automation.
- Establish consistent terminology, best practices, and standards across data science and engineering teams.
- Oversee the infrastructure, deployment, and maintenance of ML services from the initial development through deployment.
- Automation of ML model validation, model integration testing, and data validation.
- ML and software application teams to work together on the release cycle.
End-to-End MLOps Workflow
Before beginning any project, we usually evaluate the scope, the data needs, modeling, and deployment strategies. Following that, a framework for developing models is created. A basic model serves as a starting point. In addition, it helps to address the drift concept and provide a prototype of how to develop, deploy and continually improve and deploy models. Detailed in the following diagram and text is a ML pipeline that uses continuous integration and continuous delivery. We are going to use this approach to develop and deploy our food recipe application.
The pipeline consists of the following stages:
- Development and experimentation: includes trying out new ML algorithms iteratively. This stage builds ML pipelines that then push the source code to a repository.
- Pipeline continuous integration (CI): Source code and various tests are the pipeline components (packages, executables, and artifacts) to be deployed in a later stage.
- Pipeline continuous delivery: The artifacts produced by the CI stage are deployed to the target environment with the new implementation of the model.
- Automated triggering: An automated model execution process occurs in response to a trigger or a schedule to push a trained model to the model registry.
- Model continuous delivery: Prediction service is a trained model. It is used for predictions.
- Monitoring: Using data from the live platform, collect statistics on the model’s performance.
MLOps Tools in One Picture
The MLOps process has been managed using a wide array of MLOps tools. The current landscape is categorized into three categories: data, training and evaluation, and deployment.
Considerations for choosing MLOps tools
Since so many tools are available, it becomes impossible to evaluate each tool without considering the task at hand. Also, each tool takes a different approach. When choosing a tool, ensure that only the relevant tasks are taken into account. A critical criterion when we selected the tool for food recipe application is that they should be multi-platform, cross-framework, and infrastructure-agnostic for Python programming language.
One of the best ways to select MLOps tools is to base them on the tasks that need to be accomplished. Following are the broad categories:
- Data and Pipeline Versioning gives us control over the data and features as well as transformations.
- Data Analysis and Machine Learning Model Development. The Data Analysis workflow is intended to provide insight into data more quickly, more deeply, and more comprehensively. Model development is an iterative process, in which many models are derived, tested, and improved until a model that satisfies the desired criteria is built.
- Experiment tracking and model organization come after models are built. It is easier to track model architectures and training runs. Different types of data can be used in conjunction with model training and testing with the code.
- Hyperparameter tuning or optimization is the practice of choosing optimal parameters used to direct the learning process.
- A model deployment approach utilize a model as a service model. We wrap ML model inference that is considered stateless, lightweight, and idempotent within a container. The REST API exposes the functionality of the ML model.
- Monitoring lets us monitor a model’s performance after deployment and track changes over time.
- The continuous integration/continuous deployment process ensures it is ready for staging and production environments while it continues to spin up services based on its model artifact.
| Task | Criteria of Selection | Tool |
| Data and pipeline versioning | 1. Enables a greater focus on data quality 2. Maintain data organization and versioning 3. Keep all data organized and accessible | DVC is designed to make machine learning models shareable and reproducible and works with large files, data sets, machine learning models, and metrics as well as code. |
| Data Analysis and Model Development | 1. ML experimentation platform for group collaboration. 2. Access data sources securely Smooth transition from ML training to deployment. 3. Validate the model’s integrity whenever it is retrained. Debug machine learning models and make informed decisions about their improvement | Jupyter Notebooks We can create and share documents with live code, equations, graphs, and narrative text. |
| Experiment tracking and organization | 1. Managing the whole machine learning lifecycle, including experimentation, reproducibility, deployment, and a central model registry. 2. Ability to go back in time to examine everything relating to a specific model. Manage and deploy models from various machine learning libraries to a range of model serving and inference platforms 3. A breakdown of deployed & decommissioned ML models, their history and the stage of deployment of each model should be available. 4. Training related to a given model should only be accessible to certain people in the model registry. | MLflow helps modelers experiment, reproduce, deploy and keep track of models. |
| Hyperparameter tuning | 1. Build and run distributed applications using simple parameters. 2. Enable end users to parallelize single machine code with little or no code changes. 3. Enable complex applications through a large ecosystem of applications, libraries, and tools. | Optuna can be used to automate hyperparameter search using open source components. Several hyperparameter search algorithms and frameworks are integrated with Ray Tune, including Optuna. |
| Model Deployment and Serving | 1. Deploy with the least amount of engineering efforts. 2. Models are trained on historical data, but they predict using current data. 3. Models remain constant until they are re-trained and deployed in the production system. | FastAPI is a modern web framework designed for API development that is quick and high-performing. |
| Continuous integration and continuous delivery pipelines | 1. Delivering a new version of a model is performed by following the CI/CD pipeline. 2. Merge changes back into the main branch as often as possible. 3. Integrate changes early instead of waiting for release day. 4. Tests are run against the build to verify changes. 5. Deploys changes to a testing and/or production environment after the build stage. | CircleCI speeds up builds, eliminates feedback loops, and simplifies pipeline maintenance. |
Final Words
We have a shortlisted list of MLOps tools that are framework-, platform-, and infrastructure-agnostic. We will use these tools in actions using Python in a series of posts. We can start with discussing how to save a trained machine learning model.
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References
- https://towardsdatascience.com/get-started-with-mlops-fd7062cab018
- https://fullstackdeeplearning.com/spring2021/
- https://neptune.ai/blog/best-mlops-tools
- https://medium.com/optuna/scaling-up-optuna-with-ray-tune-88f6ca87b8c7
- https://ml-ops.org/content/state-of-mlops
- https://www.atlassian.com/continuous-delivery/principles/continuous-integration-vs-delivery-vs-deployment