Smart Solutions: Tailored Machine Learning Engineering

Aditya Jangra

(0)

Starting at

$

1,500

About this service

Summary

I offer bespoke machine learning solutions tailored to your specific needs, including custom model development, detailed documentation, and comprehensive performance reports. What sets me apart is my blend of deep technical expertise and proactive problem-solving approach, ensuring that each solution is not only technically robust but also aligned with your strategic goals.

Process

1. Requirement Analysis: Gather and understand client needs, project goals, and data specifics to tailor the solution effectively.
2. Data Collection & Preparation: Acquire and pre-process data, including cleaning, normalization, and splitting into training and validation sets.
3. Model Selection & Development: Choose appropriate algorithms and build machine learning models using state-of-the-art techniques.
4. Training & Optimization: Train the model with the prepared data, fine-tune hyperparameters, and optimize performance.
5. Evaluation & Validation: Assess the model’s accuracy and effectiveness using performance metrics and validation data.
6. Deployment & Integration: Export the trained model to a suitable format, integrate it into the client’s environment, and ensure it functions as expected.
7. Documentation & Reporting: Provide detailed documentation of the model, including architecture and usage instructions, along with a performance report.
8. Revisions & Support: Offer revisions based on feedback and provide ongoing support to address any issues or additional needs.

What's included

  • Custom Machine Learning Model

    -Description: A fully trained, optimized machine learning model tailored to the client's specific needs. -Format: Model file in .h5, .pkl, or .onnx format. -Quantity: 1 model. -Revisions: Up to 2 rounds of revisions based on feedback.

  • Comprehensive Model Documentation

    -Description: Detailed documentation outlining the model architecture, data pre-processing steps, hyperparameters, and usage instructions. -Format: PDF document and Markdown file. -Quantity: 1 document. -Revisions: 1 round of revisions for clarity and completeness.

  • Performance Report & Analysis

    -Description: A thorough report on the model’s performance metrics, including accuracy, precision, recall, and any other relevant metrics, along with insights for potential improvements. -Format: PDF report and Excel file with raw metrics. -Quantity: 1 report. -Revisions: 1 round of revisions for additional analysis or clarification.

Duration

2 weeks

Skills and tools

ML Engineer
AI Model Developer
AI Developer
Python
PyTorch
scikit-learn
TensorFlow
Variational Autoencoders (VAEs)

Industries

Artificial Intelligence (AI)
Aquaculture
Facial Recognition

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