AI Model Versioning: Managing the Evolution of Artificial Intelligence
The world of artificial intelligence (AI) is rapidly developing, and with it comes an evolution of machine learning models. As AI models grow and change, it is essential to track their versioning to ensure defensible, effective, and accurate results. The act of model versioning is crucial in governing models’ consistency and managing access to outdated or inaccurate models. Model versioning evolved as a subpart of software versioning, which enabled the software developers to track the changes to software over time and reliably restore software that was functional and known. In the world of AI, version control has a comparably important role in today’s applications, testing, and development. In this article, we will explore the benefits of AI model versioning, the challenges that may arise, and the tools and technologies that can help ensure versioning efficiency.
## How to Get AI Model Versioning?
To get started with AI model versioning, first, it is important to understand the concept of version control. Version control is a process in which changes to files or software are meticulously tracked over time. Every modification is cataloged and documented with a date and other essentials, including responsible parties and the change nature.
In the same vein, AI model versioning provides the ability to track changes to artificial intelligence models over time. This means that all changes that affect the model are documented, leading to a historical log of the model’s changes over time. To start, it is crucial to choose the model versioning system that fits your workflow and aligns with your team’s style and best practices. Many different ways can be used to control AI model versioning:
1. Git is an open-source, top-rated version control system popular for its ability to track changes to source code repositories. Git has been modified to work on several different industries requiring version control, including markup languages and AI model versioning.
2. DVC is an open-source version control system dedicated explicitly to data versioning; it makes the process of building machine learning pipelines more robust and more comfortable. It allows the team to track and manage data files, configurations, and machine learning models work in parallel in large-scale projects.
3. Kubeflow is an open-source machine learning toolkit assembled on top of Kubernetes, a leading cloud container orchestration system. It helps in streamlining the management of big data and AI workflows across hybrid environments.
6. Amazon SageMaker ML workflow is a fully-managed service that provides developers and data scientist to construct, train, and deploy machine learning models quickly.
## How to Succeed in AI Model Versioning
For efficient AI model versioning, there is a need to leverage the right tools and establish best practices. There are specific techniques for controlling and tracking AI model versions, including creating unique identifiers for each model, labeling and logging for clear tracking of changes over time, and documenting who made what change, when they made it, and why.
The other aspect is to explore the challenges you might face when working with AI models, and there is a need to address bottlenecks in your workflows. One of the significant issues while working with these systems is working cooperatively with increasing complex data, code, and models. There is always pressure to launch new models quickly, and there is a risk of making unauthorized modifications to the data. The solution is to establish safeguards that protect the integrity of the model in the version control process.
## The Benefits of AI Model Versioning
By carefully tracking the changes that occur to AI models over time, organizations can better manage their in-production AI systems. Documenting each change makes it possible to avoid errors and discrepancies significantly. Below are some of the benefits of AI model versioning:
### Accurate Reproducible Results
Without version control, many analyses of data sources, algorithms, parameters, techniques, and other factors lead to varying outcomes. Versioning ensures that your model’s performance results remain consistent between iterations over time.
### Transparency
Versioning enhances project transparency by giving all stakeholders access to model development history, which can help build trust in accuracy, improve ethical considerations, ensure repeatability, and promote team collaborations.
### Improvement of Compliance
In regulated industries such as pharma, finance, or the government, the model versioning’s documentation can help compliance teams validate that the AI system is certified both in terms of its build and its accuracy. This promotes better compliance transparency, auditability, and defensibility
## Challenges of AI Model Versioning and How to Overcome Them.
AI model versioning comes with many challenges, including protecting data privacy and ensuring the model’s consistency across multiple environments. The following are some of the challenges and how to overcome them:
### Protecting data privacy
When data is added or modified, privacy must be taken into account. Data privacy protection can be accomplished by communicating requirements and security procedures to staff regularly.
### Compatibility
Compatibility issues can arise when sharing code, progress, and tools with other peers. Collaborating on improvements with other modelers can prevent compatibility issues.
### Introducing Risk-Driven Development Principles
Remediation and patching isn’t just the responsibility of developers or IT anymore; risk management of software development is essential. A checkpoint list will ensure accountability, documentation, and automation throughout the software development process.
## Tools and Technologies for Effective AI Model Versioning
There are various tools and technologies available that can help organizations manage AI model versioning:
### DVC
Data Version Control (DVC) is a data-centric version control system that efficiently tracks data input, code, environment, configuration files, and metrics of your business-machine learning projects. This management of machine learning workflows simplifies iterative development, algorithm tuning, and the production process.
### GitHub
GitHub is another tool that helps teams maintain codebases and supports distributed teams perform these tasks collaboratively. It is also used by AI/ML developers as a collaboration tool for version control.
### Docker
Docker is a tool used for the management of applications, built on top of containers. These containers can be ideal for testing AI models as it could provide a consistent deployment environment that resists alteration.
## Best Practices for Managing AI Model Versioning
Managing effective AI model versioning requires institutions to follow best practices that provide for compliance, risk management, and version control. Below are some of the best practices to follow:
### Documentation
Ensure that a clear and concise record of relevant past versions of the AI model are kept.
### Consistent Build Process
Ensure that you have a procedure to manage versions across various operating systems, software, and hardware requirements.
### Regular Review Policy
Make sure the development and production teams review models regularly to ensure optimal performance and any issues resolved.
### Automated Deployment
Having manual processes can introduce human error; therefore, automation should be used wherever possible.
### Set a Standard Naming Convention
Establishing a standard across the organization will reduce confusion when working with large teams or when code is shared across groups.
### Data Backup Policy
Ensure data backup policy is in place and maintained for all versions and builds of the model. Ensure backups can be restored according to their specific versions.
Conclusion
AI model versioning has become increasingly important due to the fast-paced evolution of artificial intelligence. Proper tracking of changes to models over time is essential for effective modeling and testing, stakeholder collaborations, and promoting transparency and risk management. We have explored the benefits, tools, and technologies used to manage AI model versioning and the best practices required for efficient version control. By implementing these guidelines, your company can remain current in the fast-evolving world of AI and ensure that they are employing accurate, reliable, and defensible models.