You will get I will clean your dataset and train custom Machine Learning models
Project details
High-quality data is the single most critical factor for deep learning success. I build production-ready data cleaning, preprocessing, and engineering pipelines designed to maximize your model’s accuracy and minimize training convergence times. Whether you are dealing with computer vision datasets, unstructured text, or complex tabular data, I turn messy data into optimized training tensors.
What this project includes:
Data Auditing & Cleaning: Handling missing variables, detecting anomalies, and stripping corrupt data/images.
Feature Engineering & Transformation: Standardizing, normalizing, and encoding variables for deep learning architectures.
Pipeline Optimization: Implementing efficient data loading practices (such as PyTorch Dataset/DataLoader or TensorFlow tf.data) to eliminate GPU starvation bottlenecks.
Augmentation Strategy: Setting up robust, domain-specific data augmentation workflows to prevent model overfitting.
What this project includes:
Data Auditing & Cleaning: Handling missing variables, detecting anomalies, and stripping corrupt data/images.
Feature Engineering & Transformation: Standardizing, normalizing, and encoding variables for deep learning architectures.
Pipeline Optimization: Implementing efficient data loading practices (such as PyTorch Dataset/DataLoader or TensorFlow tf.data) to eliminate GPU starvation bottlenecks.
Augmentation Strategy: Setting up robust, domain-specific data augmentation workflows to prevent model overfitting.
Machine Learning Tools
Keras, MLflow, pandas, Python, Python Scikit-Learn, SciPy, SQLWhat's included
| Service Tiers |
Starter
$45
|
Standard
$95
|
Advanced
$195
|
|---|---|---|---|
| Delivery Time | 2 days | 3 days | 4 days |
Number of Revisions | 2 | 2 | 3 |
Number of Model Variations | 1 | 2 | 3 |
Model Validation/Testing | - | ||
Model Documentation | - | - | |
Data Source Connectivity | - | - | - |
Source Code |
About Jaime
Python ML Engineer | Data Pipeline Specialist
Panama, Panama - 7:17 pm local time
If your deep learning model is failing to converge, your dataset has severe class imbalances, or you need a robust ETL pipeline built to parse complex sequential data, I can deliver production-ready code this week.
My Core Technical Execution Matrix:
• Deep Learning: Custom PyTorch model development, 1D-CNN architectures, training pipeline debugging, and performance optimization.
• Model Tuning: Implementing Optuna hyperparameter tuning, Learning Rate (LR) range testing, and custom loss functions (Focal Loss, Weighted Cross-Entropy) to resolve data imbalances.
• Data & Infrastructure: Building automated ETL pipelines via Pandas/NumPy, structuring relational PostgreSQL schemas, and containerizing environments using Docker for instant local replication.
Let's hop on a brief text chat or call to review your script requirements, data schema, or model constraints today.
Steps for completing your project
After purchasing the project, send requirements so Jaime can start the project.
Delivery time starts when Jaime receives requirements from you.
Jaime works on your project following the steps below.
Revisions may occur after the delivery date.
Data Audit & Requirements Alignment
I analyze your sample raw data, evaluate your downstream deep learning goals, and identify specific data quality risks (e.g., class imbalances, label noise, corrupt files).
Pipeline Architecture & Baseline Setup
I build the structural code foundation for the cleaning script, integrating robust error handling to flag or automatically repair corrupt samples.