Development of a machine learning model for multi-class classification of large data

Budget: 2990 PLN Deadline: 29 days

Good day!
My name is Valentin, and I represent Arctic Web Agency. We are a team of experienced web developers specializing in creating modern and effective web solutions for businesses. I can provide examples of our similar work in personal messages. We are ready to take your project to work!

Sincerely,
Arctic Web Team
Freelancehunt

Budget: 19500 PLN Deadline: 30 days

Hello!

I can create a robust model for your large dataset and ensure it achieves the desired accuracy. I will use Spark for smooth data processing, and then apply transformer models and boosted tree methods such as LightGBM or CatBoost. Each step will be carefully tuned so that we can achieve the best balance between speed and accuracy.

One idea that could make this solution stand out is enabling collaboration between the transformer model and the tree. When both models give the same, confident answer, we trust it. When they disagree, the system can use a fallback rule and log the case for later review. This simple setup often improves the F1 score and maintains result stability as data changes over time.

The code will be fully documented and written to be easily scalable. The model will be deployed in FastAPI, ready to connect as a REST API, with real-time predictions and batch processing. It will also include tests, monitoring notes, and a brief guide to ensure smooth integration.

Thank you!

Budget: 28750 PLN Deadline: 50 days

Hello, Oleksandra
I can create an AI model, that meets your accuracy threshhold, whilst maintaining the mandatory use of distributed computing.
Kindly inbox me so that we can discuss in more detail

Budget: 20000 PLN Deadline: 20 days

I have significant experience in developing machine learning models for large datasets (100GB+). I have worked with multi-class classification, applying modern architectures (Transformer, LightGBM, CatBoost) and hyperparameter optimization methods.

I use distributed computing (Spark, Dask, PyTorch Distributed) to accelerate model training. I guarantee achieving high metrics (F1-score > 0.92) and stability on test samples.

The code will be well-documented and prepared for scaling and integration via REST API. I am ready to complete the project within the specified deadlines, taking your requirements into account.