Custom AI Model Training

Custom AI Model Overview

While pre-built AI solutions address many common needs, some business challenges require custom models trained on your specific data. Custom AI models can provide competitive advantages by solving problems unique to your organisation.

Xharvoc's data science team specialises in developing custom machine learning models for classification, prediction, natural language processing, computer vision, and more.

Data Preparation

Quality data is the foundation of effective AI. Key steps include:

1. 1Data Collection: Identify and gather relevant data sources
2. 2Data Cleaning: Remove duplicates, handle missing values, correct errors
3. 3Data Labelling: Create training labels (often the most time-consuming step)
4. 4Data Augmentation: Expand dataset size and diversity when needed
5. 5Feature Engineering: Transform raw data into meaningful model inputs

Model Development

Our iterative model development process includes:

• Algorithm Selection: Choose appropriate techniques for the problem
• Experimentation: Test multiple approaches and architectures
• Hyperparameter Tuning: Optimise model configuration
• Validation: Evaluate performance on held-out test data
• Bias Assessment: Check for and mitigate unwanted biases

Deployment & Monitoring

Deploying models to production requires careful planning:

• Model Serving: Set up infrastructure for real-time or batch predictions
• A/B Testing: Compare new models against existing solutions
• Performance Monitoring: Track accuracy and latency in production
• Model Drift Detection: Identify when retraining is needed
• Continuous Improvement: Regularly update models with new data