How do you deploy your AI project?

Deploying an AI project: First, please ensure your project meets the ethical and regulatory standards. Please document your data sources, methods, and limitations to maintain transparency. Define your deployment strategy, considering infrastructure, scalability, and security factors. Implement best practices for designing and deploying AI and machine learning solutions. Solicit user feedback to improve your project's value and relevance continuously. Lastly, consider the specific requirements of deploying AI solutions in information systems, such as defining the problem and scoping the project. By following these practices, you can successfully deploy your AI project and deliver value to your target audience. 🚀💡🔬

The Aii Methodology (Artificial Intelligence Integration and Deployment Methodology) is a guide for businesses to navigate the complex journey of AI implementation: Assess Needs: Define requirements. Evaluate Platforms: Compare options. Test Compatibility: Ensure system integration. Optimize Performance: Enhance models and workflows. Monitor & Iterate: Continuously improve.

To deploy your AI project, you can choose from various deployment platforms such as AWS SageMaker, Google Cloud AI Platform, Microsoft Azure Machine Learning, and Docker/Kubernetes. Each platform has its own set of features and capabilities. AWS SageMaker offers a comprehensive suite of tools for building, training, and deploying machine learning models. Google Cloud AI Platform allows you to train your machine learning models at scale, host your trained model in the cloud, and use your model to make predictions about new data. It supports online prediction, batch prediction, and provides tools for managing your models and model versions.

You need to also ensure that data doesn't introduce bias. Vet the data to make sure it covers all aspects of intended use case. If you don't own the data, you also need to be able to track the source, to avoid downstream regulatory and ethical questions.

Data preparation is indeed a critical step in any AI project, and it significantly impacts the performance and reliability of the model. Data Cleaning: Thoroughly clean and preprocess your data to remove noise, handle missing values, and ensure consistency. Data Validation: Validate your dataset to ensure it meets quality standards and is representative of the problem you're addressing. Data Augmentation: Augment your data when necessary to increase the diversity and robustness of your model, especially for tasks like image recognition and natural language processing.

If your project allows it, embrace a deployment model that will support both publicly and privately-operated Generative AI models. Publicly-operated models like GPT or Bard will give you maximum ease of use, while privately-operated models deployed on platforms like AWS SageMaker will give you enhanced privacy and protection of your Intellectual Property. Tools like LangChain make it easier to implement your Generative AI application while supporting multiple models. For example, the same application could use GPT hosted on OpenAI (public) and Llama 2 hosted on SageMaker (private). Of course, you will need to properly test your application to make sure that it returns consistent results across all the models that you want to support.

Models you can use to build the model could be Train- Test- fine tune - Train OR Train- fine tune - Test You need to effectively partition your data to make sure your application is tested holistically.

Monitoring AI models is key! AI models' performance are known to start drifting after a while. You need to monitor the model performance and make sure is up to date with latest data, failure to do so may results in significant impact! One example of impact of lack of monitoring AI models is Zillow AI. The model has not been updated with latest market estate info and performance started to drift causing the company losing many millions because the AI estate valuations did not reflect market conditions.

Seriously consider using Software as a Service (SAAS) providers to simplify deployment, at lower cost, and with automatic software upgrades as AI develops.

There was a retail company that created an AI tool to help their online customers. They planned to release a big, fancy version of the tool, but then a competitor made a simpler version first. This could hurt the company's business. So, they changed their plan and used a different method called Kanban. They only focused on making the most important parts of the tool first. Then, they tested it with real users and made changes based on their feedback. They also made some parts of the process automatic to make it faster. This way, they could release a basic version of their tool in just a few weeks instead of several months. This helped them keep their customers and stay competitive.

Agile methods also help with deliver highest market value in every iteration. Tools like WSJF calculator helps prioritize the features. AI projects can particularly take advantage as the model grows/changes with each iteration, uncovering newer ideas or bugs.

DevOps plays a crucial role in the successful deployment of AI projects, especially when it comes to ensuring scalability and reliability. For instance, automating the CI/CD pipeline for machine learning models can significantly accelerate deployment, reducing time to market from weeks to just hours. Leveraging tools like Docker and Kubernetes provides flexibility in infrastructure management and ensures smooth updates to models in production without downtime. This approach not only speeds up processes but also enhances reliability through continuous monitoring and automated testing at every stage.

For AI deployments the area is very fluid, as on day (Sep 2023) i am advising the below combination for in-context. data pipelines -- Databricks, Airflow embedding model -- Hugging Face vector database -- Pine cone Playground/exp -- Open AI Orchestration -- Langchain, LlamaIndex API's -- Zapier (Customer call) I see AI become more of a data engineering problem and a compute $ + RD driver based play. Comment up if you have any further questions.

Cost and performance are critical aspect of any model deployment. The objective is to deploy a model with low latency, high throughout and within budget. To find a balance between all these you have to choose a deployment runtime that offers the best performance. For example, the DeepSparse runtime offers GPU class performance on CPU. Next, you need to deploy optimized models, so that they are cheaper and easier to deploy. You can optimize the model for deployment using open source tools such as SparseML which provides various recipes for model optimization.