As every small, medium and large organisation are willing to become data-driven, the application of machine learning and artificial intelligence is increasing rapidly. Also, when we look at the market, we find that AI and ML market is one of the prominent and challenging markets nowadays. However, with these high values, this area also shows us a new source of risk. There can be various reasons, like an inadequately trained data model can lead to bad data-driven decisions, breaking the laws and many more.
So it becomes a compulsion to define governance in AI/ML development to minimise the risk and improve the development quality. So Let’s look at the broader picture of AI/ML model governance.
What is model governance?
When an organisation starts controlling the model development process, usage, and validation or assigns the model’s restrictions, responsibilities and roles, this process can be considered model governance.
We can compare model governance as a framework where this framework includes a set of strategies that help decide or specify how an organisation manages models within it. These strategies can be of the following type:
- Strategies to control models in production
- Strategies for versioning the models.
- Documentation reaction strategies.
- Model post-production monitoring
- Models comply with existing IT policies.
If any organisation can implement such a framework effectively, then they can get a high level of controllability and visibility into the model’s behaviour in production. At the same time, they get access to operational efficiencies and this help in achieving more benefits from AI investments.
Increased visibility also allows you to easily identify and resolve the problems and risks of ML and AI, such as models being biased. It also makes us increase the performance of the model in production because enhanced visibility allows you to spot issues that degrade models performance over time, such as data decay, model drift etc.
Importance of AI/ML Model Governance
We know that artificial intelligence and machine learning are relatively new areas, and many inefficiencies must be resolved. Model governance not only helps solve many of these problems but also improves every aspect of development and the potential value of any AI project.
We have already discussed that model governance helps in risk assessment, which is a major importance of AI governance as it ensures that the model stays out of risk with us. Many models are programmed to learn continuously after running them, and they can be biased because of biased data, which affects the decision-making of the model.
A set of rules in the governance framework allows us to audit and test for the model’s speed, accuracy and drift while in production to prevent further difficulties. Since various clauses can be applied to Ai governance, we can easily find out the ownership and accessibility of the model.
Such a governance framework can answer the following important questions:
- Who is the model’s owner?
- Do relevant rules and regulations restrict a model?
- Data on which model is trained?
- What sets of rules and regulations need to comply between the development stages?
- What are the steps required to monitor models after post-production?
Who is the model’s owner?
In an organisation, we can find that various people are arranged to complete various work of any project. So it becomes an important task to keep track of the work of every person involved in the project. This tracking helps improve collaboration, lesser duplication, quality improvement, and improve problem-solving. It always becomes necessary to keep this in the rule book so that well-catalogued inventory can allow people to build on the work together more easily.
Do relevant rules and regulations restrict a model?
Often models require following the local or domain rules and laws, such as a recommendation system developed to find relationships between different goods in a supermarket and representing a strong relationship between cigarettes and chewing gum. Most countries don’t allow to advertising of cigarettes, so this kind of business recommendation needs to be dropped. So before deploying a model into production, we should consider the following things:
- What local government defines regulations and laws relevant to any model’s functionality?
- What are the ways to test the model’s functionality are complying with defined laws?
- After making it into production, what will be the ways to monitor the model?
Data on which model is trained?
One very important thing about the machine learning model is that their results are indivisibly attached to the training data. So if there is any problem occurs in the development line, it becomes important to find the precise bad data points to replicate the issue. This is an ability in machine learning, and planning based on tracing the issues is crucial to avoid bigger failures.
Keeping track of the data source is a worthy task because this helps in measuring model drift frequency and stability of the models on old data. So it is always suggested to train the model on a high data range to get different results, but for stability, we should consider a low data range.
What sets of rules and regulations need to comply between the development stages?
There are various model development stages involved in the process, and one should have approval at every stage and keep records to ensure a high-quality standard. And it also reduces the chances of failure making their way through the production. This set of rules can tell us about the following things:
- Data quality
- Feature engineering
- Train/Test/Validation or cross-validation
- Compliance testing
- Code quality
- Version control
- Documentation
It is highly suggested to get the development thoroughly checked by a qualified team or person outside the development team.
What are the steps required to monitor models after post-production?
One of the most important things about model governance is that it gets complete after we become capable of regularly monitoring our deployed model’s performance using various aspects like model drift, data decay and failure in the development pipeline.
However, these aspects are internally connected, but they all represent their story differently. When things come into the post-production stages, it becomes necessary to maintain the system we have created and the new updates we are trying to give in the system. Early capturing of the likelihood of failure makes the system more accurate and reliable.
Final words
In the recent scenario, we have seen that every organisation are willing to become data-driven, or some are already data-driven, where machine learning models are helping them to complete various tasks. To maintain their high performance, effectiveness and quality, it is necessary to care about the model governance, which can lead your model to great success.
Data Science Wizards (DSW) is an Artificial Intelligence and Data Science start-up that primarily offers platforms, solutions, and services for making use of data as a strategy through AI and data analytics solutions and consulting services to help enterprises in data-driven decisions.
DSW’s flagship platform UnifyAI is an end-to-end AI-enabled platform for enterprise customers to build, deploy, manage, and publish their AI models. UnifyAI helps you to build your business use case by leveraging AI capabilities and improving analytics outcomes.
Connect us at contact@datasciencewizards.ai and visit us at www.datasciencewizards.ai