You will get a real-time defect detection system.
Project details
You will get production-ready deliverables and documentation regarding the work completed, model statistics, and implementation details.
Machine Learning Tools
Microsoft Excel, NumPy, pandas, Python, Python Scikit-Learn, PyTorch, scikit-learn, TensorFlow, Tesseract OCR, XGBoostWhat's included
| Service Tiers |
Starter
$5,000
|
Standard
$10,000
|
Advanced
$20,000
|
|---|---|---|---|
| Delivery Time | 7 days | 14 days | 30 days |
Number of Revisions | 0 | 1 | 3 |
Number of Model Variations | 1 | 5 | 5 |
Number of Scenarios | 1 | 5 | 20 |
Number of Graphs/Charts | 1 | 5 | 20 |
Model Validation/Testing | |||
Model Documentation | |||
Data Source Connectivity | - | - | - |
Source Code | - | - | - |
Optional add-ons
You can add these on the next page.
Additional Revision
+$2,000
Additional Model Variation
(+ 5 Days)
+$2,000
Source Code
(+ 1 Day)
+$2,000
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DA
Daniyal A.
Apr 25, 2024
Quick ML Consulting
DA
Daniyal A.
Feb 19, 2024
Quick ML Consulting
Great to work with, really knowledgeable.
About Randall
Software Engineers | Full Stack | Machine Vision | Machine Learning
100%
Job Success
Warsaw, United States - 10:24 am local time
Our core competencies include:
• Machine Vision, Machine Learning, AI
• .NET (C#, VB.NET, ASP.NET)
• Java
• Android / iOS / React Native / Kotlin
• Front and back end web development (many design and coding frameworks)
o HTML / CSS / JavaScript / TypeScript / Ajax / jQuery / React.js / Angular / ColdFusion
• Database design and implementation (many flavors)
• Cloud development (AWS, Azure, Google Compute, Rackspace)
Steps for completing your project
After purchasing the project, send requirements so Randall can start the project.
Delivery time starts when Randall receives requirements from you.
Randall works on your project following the steps below.
Revisions may occur after the delivery date.
Data Collection, Organization, and Evaluation
We will collect the images, organize the files, and evaluate the quality of the images provided. At this step we will provide feedback regarding the images and suggest moving forward or acquiring new images.
Create a training plan
Determine how we will balance, transform, split, and otherwise prepare datasets. Choose which model or models will yield the best results and work on the desired hardware in production.