AI Architecture on IBM Cloud

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Sequence diagram for AI architecture on IBM Cloud

AI architecture components on IBM Cloud

Sequence Explanation of AI Architecture on IBM Cloud

The flowchart visualizing the AI architecture on IBM Cloud outlines a comprehensive process for developing and deploying an AI solution within the IBM Cloud ecosystem.

Here’s an explanation of each step in the flowchart:

1. Start: This marks the beginning of the AI workflow on IBM Cloud.
2. Upload Data to Cloud Object Storage: The first operational step involves uploading the raw data, which will be used to train the machine learning model, to IBM Cloud Object Storage. This cloud storage service offers scalable and secure data storage capabilities, making it ideal for handling large datasets required for AI training.
3. Use Watson Studio for Data Processing: Once the data is stored in Cloud Object Storage, IBM Watson Studio is employed for data processing and analysis. Watson Studio provides a collaborative environment with various tools for data scientists and developers to visualize, analyze, and prepare data for modeling. This includes tasks such as data cleaning, feature extraction, and exploratory data analysis (EDA).
4. Train Model with Watson Machine Learning: After processing the data, the next step is to train the machine learning model using Watson Machine Learning (WML). WML facilitates the training of models at scale, offering various machine learning and deep learning frameworks and libraries. It allows for the automation of the training process, model evaluation, and hyperparameter tuning to develop highly accurate models.
5. Deploy Model with Watson Machine Learning: Following the training, the model is deployed using the same Watson Machine Learning service. This step makes the model accessible via APIs, allowing it to serve predictions. WML supports different deployment options, including online (real-time), batch, and streaming deployments, to suit different application needs.
6. Integrate Model with Watson Assistant: With the model deployed, it can be integrated into applications or services using Watson Assistant. This step is crucial for creating AI-powered applications, such as virtual agents or chatbots, that can interact with users in a natural language. Watson Assistant provides the tools to build conversational interfaces that leverage the deployed AI models for delivering intelligent responses.
7. Execute Logic with Cloud Functions: IBM Cloud Functions, a serverless computing platform, is used to execute application logic or data processing tasks triggered by model predictions. This allows for the building of event-driven architectures where specific actions are performed in response to model outputs without provisioning or managing servers.
8. Store Results in Db2: The inference results or any processed data generated by the application logic in Cloud Functions are stored in Db2. Db2 is IBM’s database solution that offers robust data management and analytics capabilities. Storing results in Db2 enables further analysis, reporting, and data-driven decision-making.
9. End: This marks the completion of the AI workflow on IBM Cloud.

Each step in this flowchart demonstrates the seamless integration and interaction between various IBM Cloud services to develop and deploy AI solutions. This architecture leverages IBM’s powerful cloud and AI capabilities to facilitate the entire lifecycle of AI application development, from data storage and processing to model training, deployment, and integration into applications.