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At a Glance

Key details about this service to help you decide. Generated by Zinn Hub, not the seller.

ML Stack Coverage

15+ Frameworks & Tools
Covers PyTorch, TensorFlow, Keras, scikit-learn, OpenCV, Flask, Docker, and cloud platforms (AWS, GCP, Azure) — suitable for end-to-end ML pipelines.

Vision & AI Specialisms

Detection, Segmentation, OCR, NLP
Supports YOLO, Mask-RCNN, SAM2, PaddleOCR, LLMs (LLAMA/GPT), RAG, GANs, and pose estimation — broad coverage across modern AI disciplines.

Edge & Cloud Deployment

Jetson, Raspberry-Pi, AWS/GCP/Azure
Models can be deployed on edge hardware or cloud infrastructure with REST API support via Flask, making this suitable for both embedded and scalable web-based applications.

Tier Differentiation

Fine-Tuning & Monitoring from Boost+
Basic tier covers model creation and source code at 14 days. Fine-tuning unlocks at Boost (21 days); cloud deployment, performance monitoring, and documentation only available at Premium (30 days).

What You'll Receive

Formats:
Source FilesWritten ReportCloud LinkDigital Files
Delivery Method: Order Manager
Notes: Deliverables are shared via the order manager. You will receive source code files, trained model weights, and a Jupyter Notebook or equivalent. The Full Delivery tier additionally includes written model documentation and cloud deployment details. All files are clearly organised and commented for ease of handover.

Full Description

You need a working machine learning or computer vision model, not a half-finished prototype or a stack of theoretical notebooks. Whether you are tackling object detection, image segmentation, facial recognition, NLP, time series forecasting or something more specialised, this service delivers production-ready models built to your requirements — with clean source code, thorough validation, and the rigour your project deserves.

Every engagement begins with a research phase to understand the problem, the data and the most appropriate architecture. From there, data preprocessing is handled end-to-end — cleaning, augmentation, feature engineering and pipeline construction — before the model is built, trained and validated against your performance targets. You receive fully commented source code as standard on every tier.

The breadth of capability on offer is extensive. Object detection with YOLO-v8, YOLO-v11 and Faster-RCNN. Object tracking via OC-SORT, ByteTrack, BOT-SORT and Strong-SORT. Image segmentation using Mask-RCNN, U-NET, SAM2 and Sapiens. Image and audio classification with InceptionResnetV2, VGG, ResNet and ViT. Pose estimation, facial recognition with Facenet, Dlib and DeepFace, OCR with TesseractOCR and PaddleOCR, GAN workflows, image captioning with Florence2 and LSTM, depth estimation, inpainting with LaMa and MIGAN, embedding analysis, and time series forecasting with Transformers, RNN and LSTM. For language tasks, LLMs (LLAMA, GPT) and RAG pipelines via LangChain are also in scope.

All work is built on industry-standard frameworks: PyTorch, TensorFlow, Keras and scikit-learn. Cloud infrastructure spans AWS, GCP and Azure. Containerisation via Docker, vector databases including Chroma and Pinecone, and edge deployment on Jetson, Raspberry Pi and NCS devices are all supported. Databases — MySQL, MongoDB and PostgreSQL — can be integrated as required.

The Standard tier adds fine-tuning on top of the core build, improving model accuracy for your specific dataset with additional revision rounds. The Full Delivery tier brings everything together: fine-tuning, cloud deployment, performance monitoring, and thorough model documentation — making it the right choice for teams who need a model that is ready to operate in a live environment.

This service is right for data scientists who need specialist implementation support, product teams building AI-powered features, researchers requiring a robust baseline model, and businesses looking to automate visual or analytical workflows. Please get in touch via the order chat before placing your order if you have questions about scope — the breadth of the technology stack means almost any ML or CV challenge can be accommodated.

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Compare Packages

FeatureCore BuildStandard BuildFull Delivery
Delivery Time14 days21 days30 days
Revisions24unlimited
Research and architecture selection for your use case
Full data preprocessing and pipeline construction
Model creation and training
Model validation and testing with performance metrics
Complete, commented source code delivered
Covers ML, deep learning, CV, NLP and time series tasks
All Core Build deliverables included
Fine-tuning on your dataset for improved model performance
Extended revision allowance (4 rounds)
Supports advanced architectures: YOLO, SAM2, Transformers, LLMs, RAG
Edge-device deployment guidance (Jetson, Raspberry Pi, NCS)
All Standard Build deliverables included
Cloud deployment on AWS, GCP or Azure via Docker
Performance monitoring setup for ongoing model health
Full model documentation covering architecture, training and usage
Unlimited revisions throughout the engagement
REST API integration via Flask for live inference endpoints

Portfolio

Examples of the seller's work related to this Zinn.

Build Your Machine Learning or Computer Vision Model

Build Your Machine Learning or Computer Vision Model

Build Your Machine Learning or Computer Vision Model
Build Your Machine Learning or Computer Vision Model

Build Your Machine Learning or Computer Vision Model

Build Your Machine Learning or Computer Vision Model

Extra Information

Tools I Use

Frameworks:PyTorch, TensorFlow, Keras, scikit-learn
Cloud Platforms:AWS, GCP, Azure, Docker
CV & Detection Libraries:YOLO-v8/v11, Faster-RCNN, Mask-RCNN, U-NET, SAM2, Sapiens, OpenCV
NLP & Generative:LLAMA, GPT, LangChain (RAG), Florence2, LSTM
Databases & Vector Stores:MySQL, MongoDB, PostgreSQL, Chroma, Pinecone

Perfect For

Who This Service Suits:Product teams building AI-powered features who need reliable model implementation. Researchers requiring a robust, reproducible baseline. Data science teams seeking specialist support with complex architectures. Businesses looking to automate visual inspection, document processing, forecasting or other ML-driven workflows. Startups validating an AI concept with a working prototype.

My Process

Step 1 — Discovery:Review your project brief, dataset and performance targets; clarify scope via order chat.
Step 2 — Research & Planning:Select the most appropriate architecture and toolchain for the task.
Step 3 — Data Preprocessing:Clean, augment and prepare your dataset; build the training pipeline.
Step 4 — Model Build & Training:Implement, train and iterate on the model until performance targets are met.
Step 5 — Validation & Delivery:Validate results, prepare source code and documentation, and deliver via the order manager.

Frequently Asked Questions

Yes — please reach out via the order chat before placing your order. The scope of ML and computer vision projects varies considerably, and a brief conversation ensures the right tier and timeline are selected for your specific requirements.

At minimum, a clear description of your project goal and your dataset (or details about what data you have available). The more context you can share — target performance, deployment environment, existing code or prior experiments — the faster work can begin.

Data preprocessing is included on every tier, which covers cleaning and pipeline construction. If your data requires labelling from scratch, please mention this before ordering so the scope and timeline can be assessed accordingly.

Work is carried out in PyTorch, TensorFlow, Keras and scikit-learn depending on the task. Cloud infrastructure spans AWS, GCP and Azure. Docker is used for containerisation, and vector databases (Chroma, Pinecone) are available where relevant.

Yes. Edge deployment on Jetson, Raspberry Pi and NCS devices is supported. Mention your target hardware when you get in touch so the model architecture can be optimised accordingly.

The Full Delivery tier covers deploying your trained model to a cloud provider of your choice (AWS, GCP or Azure), containerised with Docker, with a Flask-based REST API for inference. Performance monitoring is also configured as part of this tier.

Revisions address changes within the agreed project scope — adjustments to model behaviour, preprocessing logic or output format. Requests that significantly expand the scope may require a separate arrangement, which will be discussed transparently via the order chat.

You will receive source code files, trained model weights, a Jupyter Notebook or equivalent, and — on the Full Delivery tier — written documentation. Everything is delivered via the order manager. Cloud deployment links or API details are shared through the order chat.

Customer Reviews

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I really enjoy working with him and his help is always welcomed. He gets a firm grasp on the concept and then uses his expertise to get the outcome we are desiring.

Zeynoc is the go-to person for advanced ML-work involving Deep Learning, Transfer Learning, and Model Optimisation. Zeynoc has deep subject matter expertise- what this means is less time taken to explain the scenario/ requirements, and more focus on building required outcomes and optimising results. I could not be more pleased for having chosen Zeynoc and will continue to use them for upcoming projects.

Great work, very supportive and reliable.

This was exactly what I needed for our project. Thanks again!

Very pleased once again!

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