Chandler Talley

Chandler Talley designs and implements scalable machine learning systems, from model development to production deployment. Expertise in supervised and unsupervised learning algorithms.

Chandler Talley builds robust data pipelines and ETL processes that handle large-scale data processing. Specializes in cloud-native architectures and real-time data streaming.

Chandler Talley applies advanced statistical methods to extract insights from complex datasets. Expert in hypothesis testing, regression analysis, and time series forecasting.

Chandler Talley develops cutting-edge deep learning models for computer vision, NLP, and predictive analytics. Experience with transformer architectures and neural network optimization.

Chandler Talley translates complex data insights into actionable business strategies. Proven ability to communicate technical findings to stakeholders and drive data-driven decisions.

Chandler Talley implements MLOps best practices for model deployment, monitoring, and maintenance. Ensures reliable and scalable machine learning systems in production environments.

Automated machine learning pipeline for model training, validation, and deployment using MLflow and Apache Airflow. Includes automated hyperparameter tuning and model versioning.

Advanced sentiment analysis using transformer models (BERT, RoBERTa) with real-time processing capabilities for social media data. Includes custom preprocessing and evaluation metrics.

Comprehensive time series forecasting framework with multiple algorithms (ARIMA, LSTM, Prophet) and automated model selection based on performance metrics.

Object detection and classification system using YOLO and custom CNN architectures for industrial applications. Optimized for real-time processing and edge deployment.

Collection of utility functions and tools for data science workflows, including data preprocessing, feature engineering, and visualization helpers. Designed for rapid prototyping.

Scalable recommendation engine using collaborative filtering and deep learning for e-commerce applications. Supports both implicit and explicit feedback with A/B testing framework.