AI / Machine learning (ML) based solutions / applications have become increasingly important in business and industry. However, with the power to make decisions that can impact people’s lives comes a responsibility to use those tools ethically and responsibly. The machine learning model governance framework is designed to help businesses do just that. In this blog, you will learn about the AI / Machine Learning Model Governance framework, its benefits, and how you can implement it in your organization.
What is AI / Machine learning (ML) model governance and why its important?
Machine learning model governance is a set of process and related tools & frameworks that the businesses need to adopt to ensure that data scientists adhere to best practices when building and using machine learning models.ML model governance helps to ensure that models’ predictions are fairly accurate and reliable, and that any data bias is minimized. The following are some of the key components of AI / Machine learning model governance framework:
- AI / ML Playbook (Standards and guidelines): Machine learning model governance typically includes standards and guidelines for data scientists to follow when building models. This can include things such as requirements for documentation, testing, and validation. For a well-managed and governed ML model development & deployment practices, it is recommended to come up with a playbook which can list down different areas / zones (where to play) and plays (in these zones) along with roles & responsibilities. Standards & guidelines can be made part of this playbook.
- Oversight and accountability for model & data quality: Machine learning model governance should include mechanisms for oversight and accountability. This could include things such as reviews by senior management, audits, or tracking of model performance over time. Accountability is a key component of any machine learning model governance framework. Machine learning models can have a significant impact on business outcomes, so it is important that businesses have mechanisms in place to ensure that models’ predictions are accurate and reliable. One way to do this is through regular value realization reviews by key stakeholders including business users and senior management. Business stakeholders can provide oversight on the aspect of value realization from machine learning models with the help of value metrics, and can help to ensure that models are meeting the business’s expectations. Another way to ensure accuracy and reliability is through regular model & data quality audits. Audits can be used to assess the quality of data used to build models, as well as the accuracy of the models themselves. By auditing models, businesses can identify any potential issues and correct them before they cause any damage.
- Tools & infrastructure governance: One important component of the model governance framework is tools & infrastructure governance. This refers to the management of the tools and infrastructure used to build and deploy machine learning models. It includes things like the selection of tools, the setting of standards for tool usage, and the management of tool dependencies. Tools & infrastructure governance is key to ensuring that machine learning models are accurate and reliable. By implementing a framework for model governance, you can be sure that your data scientists are using the right tools and following best practices when building models. This helps to minimize bias in data, and ensures that models are accurate and reliable.
- Talent management (training / education): Data scientists need to be trained on how to build and use machine learning models responsibly. They need to understand the risks associated with these models, and be aware of how to avoid bias in their data. One of the key components of a successful machine learning model governance framework is the tracking of data scientists’ expertise. This allows businesses to ensure that their data scientists are constantly up-to-date on the latest best practices for building and using machine learning models. Businesses can track their data scientists’ expertise in a number of ways. One popular method is through certification programs. Data scientists can be certified in specific areas of machine learning, such as deep learning or natural language processing. Certifications show that a data scientist has demonstrated a high level of proficiency in a particular area of machine learning. Another way to track data scientists’ expertise is through continuing education programs. Data scientists can attend conferences and workshops where they can learn about new techniques and approaches for using machine learning models. Online courses and MOOCs (massive open online courses) are also becoming increasingly popular, and can be a great way for data scientists to keep up with the latest trends in machine learning. Businesses should also consider establishing a Machine Learning Lab where data scientists can experiment with new techniques and approaches. The lab could also be used to test new models before they are deployed into production. This would help to ensure that models are accurate and reliable, and that any data bias is minimized. By tracking their data scientists’ expertise, businesses can ensure that they are using the latest, most accurate machine learning models possible. This will help them to make better decisions, faster, and with less risk.
The following are some of the benefits of having AI / ML model governance framework in place:
- Ensuring that data scientists adhere to best practices when building models: The importance of data scientists adhering to best practices cannot be overstated. Machine learning models are only as good as the data they are based on, and if the data is not properly cleaned and curated, the model may be inaccurate or unreliable. Furthermore, if the data is biased, the model may not be effective at predicting outcomes or trends. To ensure that data scientists adhere to best practices, it is important to have a governance framework in place. The framework should specify which techniques and methods can be used when building models, and it should also require that models are tested and validated before being put into production. Additionally, the framework should include provisions for dealing with data bias.
- Reducing the risk of bias in data: When it comes to leveraging data for building models, there is always a risk of data bias. This is especially true large amounts of data is used for training the models. If that data is not representative of the real world, then the model may be biased in its predictions. This can have serious consequences. For example, if a machine learning model is used to make decisions about who to hire or loan money to, it could lead to unfairness and discrimination. That’s where the model governance framework comes in. It helps to ensure that data scientists adhere to best practices when building and using machine learning models. This includes ensuring that data is unbiased and representative of the real world. The framework also helps to ensure that models are accurate and reliable, and that any data bias is minimized. This can help reduce the risk of costly mistakes being made based on inaccurate predictions.
- Ensuring accuracy and reliability of models: Building accurate and reliable machine learning models is essential for business success. However, ensuring the accuracy and reliability of models can be a daunting task, especially when there are multiple data scientists working on different models. That’s where the machine learning model governance framework comes in.The framework is designed to help data scientists adhere to best practices when building and using machine learning models. This includes things like verifying that data is clean and unbiased, testing models for accuracy, and documenting how the model was built and how it works.The framework also helps to ensure that models are used effectively and efficiently. For example, if a model is found to be inaccurate, the framework can help to determine why it’s inaccurate and how it can be fixed.
How to implement machine learning model governance framework in your organization
Like any other governance framework, the steps to implement machine learning model governance framework are fairly similar. There are a few things you need to do in order to implement the AI / ML model governance framework in your organization:
- Define the scope and purpose of the framework: The first step is to define the scope and purpose of the framework. This includes specifying which business problems it will be used to solve, and what tasks it will be used to accomplish. The importance of identifying the right business problems to solve using machine learning cannot be overstated. If the wrong problems are chosen, the models will not be effective and the time and money invested in building them will be wasted. That’s why it’s important to carefully identify the business problems that can be solved using machine learning. The process of identifying these problems should involve a lot of brainstorming and consultation with stakeholders from all parts of the organization. It’s also important to make sure that data is available to support the models being built. Once the right business problems have been identified, data scientists can get to work on building models that can help solve them.
- Establish governance board: The governance board is responsible for setting the policies and procedures that the data scientists must follow when building and using machine learning models. The board is also responsible for enforcing policies governing the use of machine learning models. These policies should include things like data quality requirements, model validation procedures, and acceptable levels of risk. It’s important that the board includes representatives from all parts of the organization, so that they can get a broad perspective on how the models will be used. Apart from experts in machine learning and data science, the board should also include business analysts / product managers, customer stakeholders, etc so that they can provide guidance to the data scientists on how to build accurate and reliable models.
- Define roles and responsibilities: The roles and responsibilities of the data scientists, as well as other stakeholders such as product managers, IT Ops professionals, etc must be clearly defined. This includes specifying who is responsible for what, and how decisions will be made. For example, the data scientists are responsible for designing and building the machine learning models, while the product managers are responsible for understanding how the models will be used and communicating that to the rest of the organization. The IT Ops professionals are responsible for ensuring that the infrastructure is in place to support the models, and that they are working correctly.
- Create process templates: The framework should include templates for creating processes, such as data management, model development, validation, deployment, and monitoring. These templates can help to ensure that everyone follows the same best practices when working with machine learning models.
- Educate / Train staff: Staff must be trained on how to use the framework, and they must understand the importance of adhering to best practices when building models.
- Implement monitoring and reporting mechanisms: A system must be put in place to track the use of machine learning models, and to report on any issues or problems that may arise. This system should track things like how many models are being used, what types of models are being used, where they are being used, and how well they are performing. It should also identify any issues or problems that may arise with the use of the models. This information can be used to help improve the framework and make sure that it is working effectively. One of the most important aspects of using machine learning models is monitoring their performance. The models need to be monitored to make sure that they are working correctly, and that they are providing the desired results. The framework should include a system for monitoring the performance of the models, and for taking corrective action if necessary.
By following these steps, you can implement a machine learning model governance framework in your organization that will help to ensure accuracy, reliability, and bias-free data while reducing the risk of costly mistakes being made based on inaccurate predictions
Conclusion
Machine learning model governance framework is a system that helps to ensure accuracy, reliability and bias-free data while reducing the risk of costly mistakes being made based on inaccurate predictions. The steps to implementing the machine learning model governance framework are fairly similar and include defining the scope and purpose of the framework, establishing a governance board, defining roles and responsibilities, creating process templates, educating / training staff, and implementing monitoring and reporting mechanisms. By following these steps you can implement a machine learning model governance framework in your organization that will help to ensure accuracy, reliability, and bias-free data while reducing the risk of costly mistakes being made based on inaccurate predictions.
I have been recently working in the area of Data analytics including Data Science and Machine Learning / Deep Learning. I am also passionate about different technologies including programming languages such as Java/JEE, Javascript, Python, R, Julia, etc, and technologies such as Blockchain, mobile computing, cloud-native technologies, application security, cloud computing platforms, big data, etc. I would love to connect with you on Linkedin.
Check out my latest book titled as First Principles Thinking: Building winning products using first principles thinking.
Check out my latest book titled as First Principles Thinking: Building winning products using first principles thinking.
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I found it very helpful. However the differences are not too understandable for me