What data preparation steps are required to build enterprise-level AI applications?

Building enterprise-level AI applications requires comprehensive data preparation to ensure high-quality, reliable, and scalable outcomes. Here are the key steps, along with explanations and examples:

1. Data Collection

   • Gather data from diverse sources such as databases, APIs, logs, IoT devices, or third-party providers.
   • Example: A retail enterprise collects transaction data from POS systems, customer reviews from social media, and inventory logs from warehouses.

2. Data Cleaning

   • Handle missing values, remove duplicates, correct inconsistencies, and standardize formats.
   • Example: In a financial dataset, null values in transaction amounts might be filled with median values, and inconsistent date formats (e.g., MM/DD/YYYY vs. DD-MM-YYYY) are unified.

3. Data Integration

   • Combine data from multiple sources into a unified view, resolving schema conflicts.
   • Example: Merging customer data from CRM systems with sales data from ERP systems using a common customer ID.

4. Data Transformation

   • Normalize, aggregate, or encode data to make it suitable for AI models.
   • Example: Converting categorical variables (e.g., "product category") into numerical values using one-hot encoding for a recommendation system.

5. Data Labeling (for Supervised Learning)

   • Annotate data with correct labels or outcomes, often requiring domain experts.
   • Example: Labeling medical images (e.g., tumor vs. non-tumor) for a diagnostic AI model.

6. Data Validation & Quality Assurance

   • Ensure accuracy, completeness, and consistency through automated checks.
   • Example: Verifying that sensor data in a manufacturing AI application falls within expected ranges.

7. Data Storage & Governance

   • Store data in scalable, secure systems with proper access controls and compliance (e.g., GDPR).
   • Example: Using a Tencent Cloud COS (Cloud Object Storage) for structured and unstructured data, with Tencent Cloud Data Governance tools for compliance.

8. Data Versioning & Lineage Tracking

   • Track changes in datasets and maintain audit trails for reproducibility.
   • Example: Logging dataset versions used for training different AI model iterations in Tencent Cloud TI-Platform.

9. Feature Engineering

   • Create meaningful features that improve model performance.
   • Example: Deriving "customer lifetime value" from transaction history for a churn prediction model.

10. Scalable Data Pipelines

• Automate data flow from collection to model input using robust pipelines.
• Example: Building a real-time data pipeline with Tencent Cloud EMR (Elastic MapReduce) and Tencent Cloud TDSQL for a fraud detection system.

For enterprise AI, leveraging Tencent Cloud’s AI and Big Data services (e.g., TI-Platform for model training, COS for storage, and EMR for processing) ensures efficiency and scalability.