How to Explore AI Safely within Sandboxes

AI sandboxes are a valuable tool for companies looking to incorporate AI. Read more on technical tips and best practices on how to effectively and responsibly introduce AI sandboxes in your organization.

Emil Holmegaard, PhD | Management Consultant at 7N
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AI sandboxes are isolated environments where businesses can develop, test, and deploy AI models without impacting their production systems. This is a valuable tool for businesses that are looking to adopt AI, as it allows them to experiment with new ideas and identify potential risks before deploying them in a live environment. Ethical and responsible AI adoption can also be ensured by isolating AI development from production systems, which allows businesses to reduce the risk of bias, discrimination, and other ethical concerns. Additionally, AI sandboxes can help businesses to comply with emerging AI regulations.  

What can you expect to learn from this article?

  • 1

    How to set up AI Sandboxes.

  • 2

    Technical tips for implementing AI Sandboxes.

  • 3

    Best practices for ethical and responsible AI development.

  • 4

    Solutions available on the market and how to choose the right one.

Setting Up an AI Sandbox

There are a few key steps involved in setting up an AI sandbox:

1. Define your goals. What do you want to achieve with your AI sandbox? Are you looking to develop new AI models, test existing models, or deploy AI models in a controlled environment? Once you have defined your goals, you can tailor your sandbox environment to meet your specific needs.

2. Choose the right hardware and software. AI sandboxes can be deployed on a variety of hardware platforms, including cloud-based servers, on-premises servers, and even laptops. The hardware you choose will depend on the size and complexity of your AI models, as well as your budget. You will also need to select the appropriate software tools for developing, training, and deploying your AI models.

3. Implement security and governance measures. It is important to implement security and governance measures to protect your AI sandbox environment. This includes restricting access to the sandbox, auditing all activity, and monitoring for potential risks. You should also develop policies and procedures for governing the development and deployment of AI models from the sandbox to production.

4. Involve stakeholders early on. It is important to involve all relevant stakeholders in the planning and implementation of your AI sandbox. This includes developers, IT staff, business users, and risk managers. By involving stakeholders early on, you can ensure that the sandbox meets the needs of the business and that it is aligned with your organization’s ethical and values.

5. Monitor and evaluate your sandbox. It is important to monitor and evaluate your AI sandbox on an ongoing basis. This will help you to identify any potential risks or problems early on. You should also make adjustments to your sandbox environment as needed to ensure that it is meeting your business needs.

Technical Tips for AI Sandboxes

  • Use a containerized environment

    Containerization is a way of packaging and deploying software applications in a lightweight and portable way. By containerizing your AI development environment, you can easily scale it up or down as needed and you can deploy it to a variety of different platforms.

  • Use a GPU-accelerated environment

    GPUs are specialized processors that are well-suited for running AI workloads. By using a GPU-accelerated environment, you can train and deploy your AI models more quickly.

  • Monitor and manage your resources

    AI models can be computationally expensive to train and deploy. By monitoring and managing your resources, you can avoid overspending and ensure that your AI sandbox is used efficiently.

Ethical and Responsible AI Development 

It is important to keep the following ethical and responsible AI development best practices in mind when setting up and using an AI sandbox: 

  • Use high-quality data that is representative of the population you are targeting. This will help to mitigate bias and discrimination in your AI models. 
  • Monitor your AI models for bias and discrimination. This can be done using a variety of techniques, such as fairness testing and impact assessments. 
  • Be transparent about how your AI models work and what data they use. This will help to build trust with users and stakeholders. 
  • Give users control over how their data is used and how they interact with your AI models. This can be done by providing users with clear privacy and consent options. 
  • Have a plan for how you will decommission your AI models when they are no longer needed. This is important to avoid the potential for harm from outdated or unused AI models. 

How to choose the right AI sandbox?

Which AI sandbox is right for you will depend on your specific needs and requirements. If you are new to AI development, I recommend starting with a cloud-based AI sandbox, such as the Google Cloud AI Platform Sandbox or Amazon SageMaker Studio. These sandboxes are easy to use and provide access to a variety of resources, such as pre-configured VMs and data sets.

Once you have more experience with AI development, you may want to consider using an open source AI sandbox, such as Kubeflow or MLflow. These sandboxes offer more flexibility and control, but they require more technical expertise to set up and use.

No matter which AI sandbox you choose, it is important to use it responsibly and ethically. AI models can have a significant impact on society, so it is important to carefully consider the potential risks and benefits before deploying them in production. By following the tips above, you can set up AI sandboxes that will help you maximize the adoption of AI without compromising the ethics and values of your business.

 

Solutions on the market

Here are some examples of cloud-based and open source AI sandboxes that you could use in your business:

Cloud-based sandboxes

  • Google Cloud AI Platform Sandbox

    The Google Cloud AI Platform Sandbox is a free environment where you can develop and train AI models using Google Cloud Platform services. The sandbox includes pre-configured VMs with popular AI frameworks and libraries, as well as access to a variety of data sets.

  • Amazon SageMaker Studio

    Amazon SageMaker Studio is a cloud-based IDE that provides a variety of tools for developing, training, and deploying AI models. SageMaker Studio includes a built-in AI sandbox that allows you to experiment with new ideas and test your models without impacting your production systems.

  • Azure Machine Learning Studio

    Azure Machine Learning Studio is a cloud-based service that makes it easy to create and deploy machine learning models without having to write code. Azure Machine Learning Studio includes a built-in AI sandbox that allows you to experiment with different algorithms and data sets.

  • Azure AutoML

    Azure AutoML is an automated machine learning service that simplifies model development. Here’s how it can benefit AI sandboxes; Automated Model Selection: AutoML automatically selects the best-performing model based on the dataset and evaluation metrics. Hyperparameter Tuning: It fine-tunes hyperparameters to optimize model performance. Fairness and Explainability: AutoML provides fairness and interpretability metrics, ensuring ethical AI.

  • IBM Watson Studio

    IBM Watson Studio is a cloud-based platform that provides a variety of tools for developing, training, and deploying AI models. Watson Studio includes a built-in AI sandbox that allows you to experiment with different models and data sets without impacting your production systems.

  • H2O Driverless AI

    H2O Driverless AI is a cloud-based platform that automates the machine learning process, making it easy to develop and deploy AI models without having to write code. H2O Driverless AI includes a built-in AI sandbox that allows you to experiment with different models and data sets without impacting your production systems.

  • Azure Copilot Studio
    Azure Copilot Studio is a low-code tool for creating custom AI copilots. It offers a graphical interface, GPT-based capabilities, and ethical AI practices. Build and deploy copilots efficiently without extensive coding. Explore it in the official documentation. 

Open source sandboxes

  • Kubeflow

    Kubeflow is an open source machine learning platform that can be deployed on a variety of platforms, including on-premises servers and cloud-based servers. Kubeflow includes a built-in AI sandbox that allows you to experiment with different models and data sets without impacting your production systems.

  • MLflow

    MLflow is an open source machine learning platform that provides a variety of tools for developing, training, and deploying AI models. MLflow includes a built-in AI sandbox that allows you to experiment with different models and data sets without impacting your production systems.

  • TensorFlow Serving

    TensorFlow Serving is an open source platform for serving machine learning models. TensorFlow Serving includes a built-in AI sandbox that allows you to experiment with different models and data sets without impacting your production systems.

About the author

Emil Holmegaard, Ph.D.

Emil has a Ph.D. in Software Engineering and over ten years of experience in software development, architecture, and governance of IT projects. He is a software quality and architecture specialist, a management consultant, and a TOGAF certified architect. His passion for analysing and exploring challenges areas between advanced technologies and business allows him to solve technical issues and help businesses be more agile and profitable.