Creating a custom AI solution can be a complex process, but with the right expertise, it is possible to break through existing development. The key is to start small and simple. Identify one or two problems to address with ready-to-use machine learning methods and use the AiComptia use case library for practical information on emerging business opportunities for AI. There are many AI solutions available today that can meet 80% of your requirements, but you will still need to customize the remaining 20%.
Examples of AI being used in companies include chatbots in customer service scenarios, physician assistants in hospitals, legal research assistants in the legal field, assistants to marketing managers in the field of marketing and face detection applications in the field of security. Google offers AutoML, Vertex AI Forecasting and BigQuery ML as pre-designed routine training alternatives to custom-trained Vertex AI model solutions. To create a customized solution, research the relevant AI tools and libraries and invest in training data. You can also customize the handling and format of input (request) and output (response) to and from your model server using custom prediction routines. The next step is to containerize, package and deploy the AI model in production. This is often the most time-consuming part of creating scalable and consumable AI models.
To ensure successful deployment, it is important to have a clear understanding of the environment where the model will be deployed. This includes understanding the hardware requirements, software dependencies, security protocols, data storage requirements and other factors that may affect performance. Once you have a good understanding of the environment, you can begin to develop a deployment strategy. This should include an assessment of the current infrastructure, an analysis of potential risks and a plan for testing and monitoring performance. It is also important to consider scalability when deploying an AI solution.
You should be able to easily scale up or down depending on demand. Finally, it is important to ensure that your AI solution is secure. This includes protecting data from unauthorized access or manipulation. It is also important to ensure that your model is not vulnerable to malicious attacks or data breaches. Security protocols should be implemented throughout the entire deployment process.
