You will get production-ready Machine Learning and Deep Learning AI models
Project details
You will get a production-ready, custom Machine Learning or Deep Learning model tailored to your specific enterprise data needs. My engineering philosophy revolves around a strict "Simulation-First" approach, ensuring that every algorithmic pipeline is rigorously validated in synthetic, software-in-the-loop environments long before production deployment.
I specialize in high-frequency data processing, building modular strategy architectures, and developing advanced recurrent neural networks (such as LSTMs and GRUs) that perform with sub-millisecond latency. Whether you require predictive time-series models for quantitative financial analysis or robust categorization pipelines, the infrastructure delivered will be clean, highly scalable, and built for real-world execution. All deliverables include well-documented Python code, latency benchmarks, and comprehensive backtesting reports.
I specialize in high-frequency data processing, building modular strategy architectures, and developing advanced recurrent neural networks (such as LSTMs and GRUs) that perform with sub-millisecond latency. Whether you require predictive time-series models for quantitative financial analysis or robust categorization pipelines, the infrastructure delivered will be clean, highly scalable, and built for real-world execution. All deliverables include well-documented Python code, latency benchmarks, and comprehensive backtesting reports.
Machine Learning Tools
ChatGPT, Deeplearning4j, GPT-3, Keras, Microsoft Excel, MLflow, NLTK, NumPy, Open Neural Network Exchange, OpenCV, pandas, Python, Python Scikit-Learn, PyTorch, scikit-learn, SciPy, SQL, TensorFlow, Word2vec, XGBoostWhat's included
| Service Tiers |
Starter
$50
|
Standard
$90
|
Advanced
$120
|
|---|---|---|---|
| Delivery Time | 3 days | 5 days | 10 days |
Number of Revisions | 1 | 2 | 3 |
Number of Model Variations | 1 | 2 | 4 |
Number of Scenarios | 1 | 2 | 3 |
Number of Graphs/Charts | 1 | 3 | 5 |
Model Validation/Testing | - | - | |
Model Documentation | |||
Data Source Connectivity | - | ||
Source Code |
Frequently asked questions
About Narendra
Advanced Systems Engineer | Python Automation, Machine Learning & CAD
Bengaluru, India - 9:40 am local time
Steps for completing your project
After purchasing the project, send requirements so Narendra can start the project.
Delivery time starts when Narendra receives requirements from you.
Narendra works on your project following the steps below.
Revisions may occur after the delivery date.
Data Ingestion & Simulation-First Architecture
I will normalize your raw data and build a software-in-the-loop validation environment. We will test baseline models (via Scikit-learn/Pandas) to validate the pipeline infrastructure before scaling
Deep Learning Optimization & Backtesting
The model is upgraded to handle high-frequency data using advanced neural network architectures. I will conduct rigorous backtesting and latency checks to ensure zero-drift performance.