You will get Adaptive Named Entity Recognition with MLOps and Python
Project details
This demonstrates a multi-layered classification strategy that begins with rule-based keyword matching before utilizing Machine Learning models like Random Forest for low-confidence results. Unidentified data is further analyzed using DBSCAN clustering to discover new potential categories for future rule updates. The workflow is professionalized through MLOps tools, employing MLflow for experiment tracking and ZenML for pipeline orchestration. Finally, the source details how to generate automated analytical reports in R to provide stakeholders with clear, visual insights into model performance and data distributions.
Machine Learning Tools
MLflow, Python, Python Scikit-Learn, R, Word2vecWhat's included
| Service Tiers |
Starter
$1,000
|
Standard
$1,200
|
Advanced
$1,500
|
|---|---|---|---|
| Delivery Time | 3 days | 5 days | 7 days |
Number of Revisions | 5 | 5 | 5 |
Model Validation/Testing | |||
Model Documentation | |||
Data Source Connectivity | - | ||
Source Code | - | - |
Optional add-ons
You can add these on the next page.
Additional Revision
+$300
Additional Model Variation
(+ 3 Days)
+$600
Data Source Connectivity
(+ 6 Days)
+$600
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SM
Sayo M.
Jan 18, 2025
Dataquake AI POC
It was wonderful working with Daniel. He is a top notch problem solver and very communicative. We pivoted our project so we weren't able to continue further, but my hope is that we can work with him again.
About Daniel
ML Engineer | Predictive Models, Forecasting & Production ML Pipeline
Surulere, Nigeria - 10:05 am local time
Over the past 5+ years I’ve built machine learning solutions that improve decision-making - from forecasting and customer analytics to automated data pipelines.
If you need help with:
• Predictive modeling (Python, scikit-learn, XGBoost)
• Data cleaning & feature engineering
• ML pipelines and automation
• Forecasting and analytics
I can help you design and implement a solution quickly; my focus is practical machine learning solutions that businesses can actually deploy and use.
Steps for completing your project
After purchasing the project, send requirements so Daniel can start the project.
Delivery time starts when Daniel receives requirements from you.
Daniel works on your project following the steps below.
Revisions may occur after the delivery date.
Data Generation/Retrieval
In Production this step is automated for every pipeline run and traces to catch edge cases.
Implementing the Rule-Based Classifier
Create a YAML-based dictionary of keywords and weighted categories (e.g., groceries, healthcare, transport) . This provides a non-technical way to classify the bulk of transactions instantly based on simple matching