You will get AI Consulting: Optimized Solutions for Business Growth
Project details
### Summary
With extensive experience in AI and data science, I offer tailored solutions that address specific business challenges. My AI portfolio showcases successful projects, such as improving OCR accuracy for public organizations, enhancing CNN models for national banks, and automating processes for judicial and healthcare systems. By leveraging open-source tools and advanced techniques, I've achieved significant results, such as reducing processing times and increasing accuracy. My approach combines deep technical expertise with a thorough understanding of business needs, ensuring impactful results that drive growth. My projects are driven by the CRISP-DM methodology, providing structured and effective problem-solving from POC to MVP. Whether optimizing models or mentoring teams, my focus on performance and continuous improvement sets me apart, guaranteeing measurable outcomes and business growth.
With extensive experience in AI and data science, I offer tailored solutions that address specific business challenges. My AI portfolio showcases successful projects, such as improving OCR accuracy for public organizations, enhancing CNN models for national banks, and automating processes for judicial and healthcare systems. By leveraging open-source tools and advanced techniques, I've achieved significant results, such as reducing processing times and increasing accuracy. My approach combines deep technical expertise with a thorough understanding of business needs, ensuring impactful results that drive growth. My projects are driven by the CRISP-DM methodology, providing structured and effective problem-solving from POC to MVP. Whether optimizing models or mentoring teams, my focus on performance and continuous improvement sets me apart, guaranteeing measurable outcomes and business growth.
Machine Learning Tools
Apache Spark, Apache Spark MLlib, Azure Machine Learning, Databricks Platform, Databricks MLflow, MLflow, NumPy, OpenCV, pandas, Python, Python Scikit-Learn, PyTorch, scikit-learn, SciPy, TensorFlow, Tesseract OCRWhat's included
| Service Tiers |
Starter
$1,000
|
Standard
$2,000
|
Advanced
$5,000
|
|---|---|---|---|
| Delivery Time | 30 days | 50 days | 70 days |
Number of Revisions | 2 | 4 | 6 |
Number of Model Variations | 1 | 2 | 2 |
Number of Scenarios | 1 | 1 | 2 |
Model Validation/Testing | |||
Model Documentation | |||
Data Source Connectivity | - | - | |
Source Code |
Frequently asked questions
About Diego
Artificial Intelligence Solutions Specialist
Sao Paulo, Brazil - 8:14 am local time
Proficient in advanced analytics tools and platforms, including Jira for project management and Scrum for agile development.
Proven track record in leading projects from conception through to publication, ensuring high impact and relevance.
Dedicated to lifelong learning, I apply cutting-edge technology to solve real-world challenges.
Emphasizes strong communication and effective management to deliver projects that meet and exceed objectives.
RECENT PROJECTS DESCRIPTION
1) Government Law Automation Project
Problem: Reduce the waiting time for the application of jurisprudence in cases received by TRF4.
Solution: Developed a dashboard indicating the relevant case law for due process.
Result: Achieved a 55% reduction in waiting time.
End User: Judges
Project Description:
Led the OCR group, focusing on text recognition using Tesseract and OpenCV in Python.
Initial task involved segregating images with minimal scanner noise.
Implemented various image filtering techniques (grayscale, threshold, binarization, histograms, Otsu) to optimize text extraction.
Successfully extracted text with 90% accuracy using the OCR tool Tesseract.
2) National Bank Project
Problem: Improve the accuracy of the image-based account opening application.
Solution: Enhanced data preprocessing, optimized backpropagation algorithm, and improved computational performance using NUMA-CTL and SPARK environment variables.
Result: Increased the accuracy of the image classifier from 50% to 95%.
Project Description:
Collaborated closely with developers from the installation of SPARK + BigDL compiled for Intel architecture to redefining the neural network model in Scala.
3) Hospital - Genetics Project
Problem: Reduce the time required for genetic analysis (GWAS) per person.
Solution: Applied advanced computational techniques including mathematical modeling and multithreading.
Result: Reduced analysis time from 3 weeks to 8 hours, achieving a 63x speedup.
End User: Medical geneticists, Researchers
Project Description:
Collaborated with Incor statisticians to remodel the nonlinear equation system.
Optimized the R environment for Intel architecture.
Applied shellscript routines for improved multithreading and memory targeting using NUMA-CTL.
4) Hospital – Exam Automation Project
Problem: Automate the anonymization of personal data in image exams.
Solution and Result: Developed a Python back-end system with 90% accuracy in detecting and anonymizing the correct positions.
End User: Internal Doctors and Researchers
Project Description:
Project segmented into 8 stages focusing on image processing and data anonymization:
Tagging images for anonymization.
Text segmentation and localization using Python EAST.
Image pre-processing for noise removal with Python OpenCV.
Text extraction using PyTesseract.
Entity recognition with Python NLTK.
Anonymization via black rectangle overlay with Python OpenCV.
Accuracy assessed using Intersect-over-Union in Python.
Performance analysis using NUMA-CTL for optimization.
Acted as technical leader, managing project stages and utilizing Scrum methodology.
5) National Data Processing Company Project
Problem: Reduce labor costs in analyzing car images for traffic violation applications.
Solution: Demonstrated the feasibility of using LSTM networks combined with Tesseract for license plate recognition.
Project Description:
Worked directly with developers to enhance the application of the free Tesseract tool for effective license plate recognition, developed in Python.
Steps for completing your project
After purchasing the project, send requirements so Diego can start the project.
Delivery time starts when Diego receives requirements from you.
Diego works on your project following the steps below.
Revisions may occur after the delivery date.
Initial Consultation Completed
Confirm the initial consultation has been conducted, and all requirements have been discussed and documented.
AI Strategy and Project Plan Developed
Share the customized AI strategy and detailed project plan, outlining milestones, timelines, and deliverables.