Why You Need an MLOps Framework for Standardizing AI and Machine Learning Operations
by Khobaib Zaamout, Ph.D., Lead Data Science Architect, Rackspace Technology
Organizations across all industries, and of all sizes have increasingly adopted AI and machine (AI/ML) learning in recent years, and this trend is expected to continue. Several factors are driving this growth, including the increase in data availability and a renewed focus on using data analytics to generate deeper insights in support of better decision-making.
However, the rapid growth ofAI/ML has created chaos in many organizations. Most notably, companies face complications due to outdated business and development processes. Also, machine learning operations are typically not a core competency for most data science teams.
This chaos has created a pressing need for standardized machine learning operations (MLOps) to streamline and scale AI/ML processes.
Creating a robust MLOps solution
In recent years, the market has seen a surge in MLOps solutions. Yet, many of these solutions fall short in that they lack key functionalities that are essential to meeting today's challenges. A predominant limitation is their inability to integrate seamlessly with an organization's existing tech stack.
At Rackspace Technology®, our machine learning teams have observed a consistent architecture pattern while deploying and managing MLOps solutions for an increasing number of companies. In response to the challenges identified, we have developed a comprehensive solution called Rackspace MLOps Foundations.
Rackspace MLOps Foundations is specifically designed to align with Google Cloud infrastructure. It is a highly adaptable and customizable solution for managing the entire machine-learning lifecycle. Its ability to seamlessly integrate with popular automation tools like Jenkins and Airflow helps to ensure smooth collaboration and efficient workflow management.
How Rackspace MLOps Foundations works
Rackspace MLOps Foundations delivers a full-fledged MLOps solution that includes Google Cloud native services and tools, like Cloud Build, Vertex AI Pipelines and others. These tools help address the challenges of taking machine learning models from development to production.
These services and tools can also automate data preprocessing, feature storage, model development, quality assurance, deployment and scaling, and monitoring and explainability. Examples include seamless environment replication, code packaging, experiment and artifact tracking, and versioning.
Rackspace MLOps Foundations unifies the power of these services and tools to create a standardized machine-learning development lifecycle and deliver these advantages:
- Consistent, cost-efficient and rapid time-to-deployment
- Repeatable and reproducible model performance
- Reliable deployment processes
- Well-defined, standardized and modularized processes
Rackspace MLOps Foundations offers a streamlined collaboration experience, enabling data and operations teams to work together seamlessly in machine learning model development. With this solution, you can automate packaging and deployment across multiple environments and eliminate common challenges such as delays and incompatibility.
Rackspace MLOps Foundations use case example
The following architecture diagram depicts a minimalist implementation of Rackspace MLOps Foundations in Google Cloud for one of our clients. It involved a simple inventory forecasting project using training data and forecasts stored in BigQuery. This project required AutoML, code versioning, and experiments and artifact tracking, but did not require model deployment or quality assurance testing.
This implementation consisted of a two-stage machine-learning development cycle that included a development stage with one Google Cloud project.
This project also needed Vertex AI Pipelines and a CI/CD pipeline to run everything. Vertex AI Pipelines performs these functions:
- Retrieve and preprocess data
- Train and deploy models
- Query a model through batch inferencing and store the results
- Notify the pipeline owners of the process completion
- Produce logs
The CI/CD pipeline offers the following capabilities:
- Facilitates the development of a GitHub repository with appropriate branches
- Manages and maintains the code within the branches
- Automatically triggers the pipeline when code is pushed to the development branch
- Enables the direct execution of a Jupyter Notebook
- Stores any artifacts generated during the execution process in a designated bucket
- Supports multiple deployments within the same Google Cloud project
- Adheres to industry-standard security practices
Data scientists and engineers use a Vertex-AI-hosted Jupyter Notebook to conduct experiments using Vertex AI Pipelines, including data preprocessing, model training and batch prediction. Once pushed to a specific branch in a GitHub repository, the Model Factory Framework executes the notebook, creates a pull request for the next branch and waits for human approval.
Next, a designated team member reviews the code, models artifacts and performance, and decides to approve or reject the pull request. If rejected, all produced artifacts and provisioned resources are rolled back. Otherwise, the deployment process proceeds to the next step.
Learn more about Rackspace MLOps Foundations
For more insight into Rackspace MLOps Foundations, our data sheet, “MLOps Foundations on Google Cloud,” overviews the value of our MLOps solution, the architecture, key features, and deliverables.
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