You will get Working AI Prototype to Validate Your Use Case and Business Value
Project details
The Proof of Concept (PoC) service assesses the feasibility of turning an idea into a practical machine learning solution, with a focus on technical viability and business relevance rather than market validation or full-scale production.
The engagement starts with understanding the business domain, identifying key problems, and clarifying business needs. Available and potential data and sources are reviewed, followed by exploratory data analysis to uncover insights, constraints, and risks. Success metrics are defined, relevant models and approaches are evaluated, and models are trained and compared on domain-specific data.
The work then moves toward improving model performance through experimentation and hyperparameter tuning. Outcomes are summarized in an overview of models and improvements, supported by a prototype demo. The solution is delivered as a containerized, API-enabled web service, along with brief documentation and recommendations for next steps and future enhancements.
The engagement starts with understanding the business domain, identifying key problems, and clarifying business needs. Available and potential data and sources are reviewed, followed by exploratory data analysis to uncover insights, constraints, and risks. Success metrics are defined, relevant models and approaches are evaluated, and models are trained and compared on domain-specific data.
The work then moves toward improving model performance through experimentation and hyperparameter tuning. Outcomes are summarized in an overview of models and improvements, supported by a prototype demo. The solution is delivered as a containerized, API-enabled web service, along with brief documentation and recommendations for next steps and future enhancements.
Machine Learning Tools
Amazon SageMaker, Apache Spark, Apache Spark MLlib, Azure Machine Learning, ChatGPT, Databricks MLflow, Google Sheets, GPT-3, MLflow, NLTK, NumPy, OpenCV, pandas, Python, Python Scikit-Learn, PyTorch, scikit-learn, SciPy, Scrapy, SQL, TensorFlow, Vertex AI, Word2vec, XGBoostWhat's included
| Service Tiers |
Starter
$4,000
|
Standard
$6,000
|
Advanced
$8,000
|
|---|---|---|---|
| Delivery Time | 10 days | 30 days | 60 days |
Number of Revisions | 1 | 1 | 1 |
Number of Model Variations | 1 | 1 | 1 |
Number of Scenarios | 1 | 1 | 1 |
Number of Graphs/Charts | 1 | 1 | 1 |
Model Validation/Testing | |||
Model Documentation | |||
Data Source Connectivity | - | - | |
Source Code |
Optional add-ons
You can add these on the next page.
Additional Scenario
(+ 4 Days)
+$500Frequently asked questions
About Oleksandr
Lead Data Scientist | Applied AI - LLMs - RAG - ML Systems
Ternopil, Ukraine - 4:33 pm local time
Data Scientist with 6+ years of experience helping startups turn AI ideas into working, high-impact solutions. I focus on NLP, Large Language Models, Retrieval-Augmented Generation (RAG), and Computer Vision, with a strong emphasis on fast experimentation and practical results.
I enjoy exploring different approaches, testing assumptions, and iterating quickly to find what actually works. I’m proactive, detail-oriented, and comfortable taking ownership in ambiguous situations - helping teams move from prototype to production with confidence.
I work hands-on across the entire ML lifecycle - from shaping the initial approach and running experiments to evaluating models, improving performance, and deploying systems on AWS and Google Cloud. I’ve contributed to projects in industrial analytics, document intelligence (PDFs), voice processing, and recommendation systems, often in early or fast-moving product environments.
All industries I had dealt with:
Enterprise Software, Knowledge Management, Business Intelligence, Technologies, Internal R&D, Product Innovation, Localization & Translation, Advertisement & Marketing, Media & Broadcasting, Healthcare, IoT & Smart Devices, Medical Devices, Sports Analytics, Railroads, Geospatial Analytics, Logistics & Supply Chain, Manufacturing, Warehouse Operations, Retail, Customer Analytics, Physical Stores & Chains, Loss Prevention & Security
Let's discuss your request. Write me in DM!
Steps for completing your project
After purchasing the project, send requirements so Oleksandr can start the project.
Delivery time starts when Oleksandr receives requirements from you.
Oleksandr works on your project following the steps below.
Revisions may occur after the delivery date.
Week 1. Getting familiar with domain
Identify business problems and opportunities, clarify business needs, assess available and potential data and sources, perform exploratory data analysis (EDA), and define expected deliverables.
Week 2. Investigating and adopting solutions
Define evaluation metrics, assess available models and approaches, train and compare models on domain data, and deliver a proof of concept as the foundation for the service stage.