You will get a RAG chatbot that answers from your own database
Project details
You will get a high quality project with technical specifications for informed decision-making, like accuracy, latency etc of your LLM, your embedding model, your database etc. This helps you improve and prioritize particular component features as you proceed with both project development and post-production improvement.
AI Algorithms
Autoencoder, Generative Adversarial Network, Large Language ModelAI Applications
AI Chatbot, Automatic Speech Recognition, Conversational AIAI Development Language
PythonAI Tools
Streamlit, TensorFlowAI Models
ChatGPT, GPT-4, LLaMAWhat's included
| Service Tiers |
Starter
$684
|
Standard
$1,174
|
Advanced
$6,225
|
|---|---|---|---|
| Delivery Time | 4 days | 7 days | 21 days |
Number of Revisions | 1 | 4 | 10 |
AI Model Integration | |||
Batch Normalization | - | - | - |
Database Integration | |||
Detailed Code Comments | |||
Image Upscaling | - | ||
MLOps | |||
Model Deployment | |||
Model Documentation | |||
Model Monitoring | - | - | |
Model Testing & Optimization | - | ||
Model Tuning | - | - | |
Natural Language Processing | |||
NLP Tokenization | |||
Pre-Training | |||
Prompt Engineering | |||
Setup File | |||
Source Code |
About Ash
ML Engineer / Data Scientist
Quezon City, Philippines - 2:48 am local time
What I've Built & Deployed:
Machine Learning & Predictive Modeling
Predictive Maintenance (Piranaware): Engineered an autoencoder model to identify irregularities from audio using mel spectrograms. This system is fully deployed on Google Cloud Platform and was shortlisted at the ITEC Regional Competition in Beijing.
Predictive Health Tech (Migraine Gabay): Built and deployed an end-to-end predictive modeling application on Hugging Face that forecasts migraine attacks based on environmental triggers and user logs.
Restaurant Demand Forecasting: Developed a dedicated time-series forecasting application specifically designed to predict operational demand for the restaurant industry.
Computer Vision
Disease Classification (Arisi): Implemented a Convolutional Neural Network (CNN) with advanced data augmentation to accurately detect plant diseases. The model is deployed via GCP with a user-facing frontend hosted on Netlify.
Process Automation & Business Tools
Workflow Automation: Built complex, reliable workflows using Make and n8n to eliminate manual tasks and optimize daily business operations.
Educational Tech (Eskwela): Developed and deployed an automated homework grading application to streamline educator workflows.
I transition complex data concepts into deployed, user-friendly tools. Let's discuss your operational bottlenecks and how we can engineer a deployed solution to fix them.
Steps for completing your project
After purchasing the project, send requirements so Ash can start the project.
Delivery time starts when Ash receives requirements from you.
Ash works on your project following the steps below.
Revisions may occur after the delivery date.
Send me your problem description, preferred solution and timeline