You will get a custom machine learning model for prediction and forecasting
Project details
I will build a custom machine learning model that turns your data into predictions you can act on, such as forecasting demand, predicting churn, scoring leads, or classifying records.
What you get: a trained and evaluated model, honest performance metrics on held-out data, feature engineering suited to your problem, and the model served as a clean API your product or team can call. I compare a few approaches (such as XGBoost, ensembles, or a neural model) and pick what performs best for your case.
Technologies: Python, Pandas, NumPy, Scikit-learn, XGBoost, LightGBM, CatBoost, TensorFlow, PyTorch, MLflow, DVC, FastAPI, Docker, Git, Jupyter Notebook, and SQL.
Testing and quality: I validate against held-out data, check for overfitting, and report real metrics, not inflated numbers. I add tests around the pipeline.
Documentation: you get clear docs on how to retrain and run the model.
Communication: I explain in plain language what the model can and cannot do, and share results honestly.
Share a sample of your data and the outcome you want to predict, and I will tell you what is realistic and how I would approach it.
What you get: a trained and evaluated model, honest performance metrics on held-out data, feature engineering suited to your problem, and the model served as a clean API your product or team can call. I compare a few approaches (such as XGBoost, ensembles, or a neural model) and pick what performs best for your case.
Technologies: Python, Pandas, NumPy, Scikit-learn, XGBoost, LightGBM, CatBoost, TensorFlow, PyTorch, MLflow, DVC, FastAPI, Docker, Git, Jupyter Notebook, and SQL.
Testing and quality: I validate against held-out data, check for overfitting, and report real metrics, not inflated numbers. I add tests around the pipeline.
Documentation: you get clear docs on how to retrain and run the model.
Communication: I explain in plain language what the model can and cannot do, and share results honestly.
Share a sample of your data and the outcome you want to predict, and I will tell you what is realistic and how I would approach it.
Machine Learning Tools
Amazon SageMaker, ChatGPT, GitHub Copilot, Keras, MLflow, NumPy, pandas, Python, Python Scikit-Learn, scikit-learn, SciPy, TensorFlow, XGBoostWhat's included
| Service Tiers |
Starter
$400
|
Standard
$1,200
|
Advanced
$3,000
|
|---|---|---|---|
| Delivery Time | 6 days | 10 days | 36 days |
Number of Revisions | 2 | 3 | 3 |
Model Validation/Testing | - | ||
Model Documentation | - | ||
Data Source Connectivity | - | - | |
Source Code | - | - | - |
Optional add-ons
You can add these on the next page.
Additional Model Variation
(+ 5 Days)
+$200
Cloud deployment
(+ 10 Days)
+$300
Monitoring and drift checks
(+ 10 Days)
+$300
Dashboard for predictions
(+ 10 Days)
+$300About Jenil
ML Engineer | LLM Fine-Tuning | MLOps | SaaS AI Integration
Ahmedabad, India - 2:23 am local time
I am an AI/ML engineer with hands-on project experience across the full modeling lifecycle: data preparation, feature engineering, model training, evaluation, and experiment tracking. My work spans classical machine learning, deep learning, modern LLM fine-tuning, and integrating AI into live SaaS products.
𝗪𝗵𝗮𝘁 𝗜 𝗰𝗮𝗻 𝗵𝗲𝗹𝗽 𝘆𝗼𝘂 𝘄𝗶𝘁𝗵:
• Prediction Model : Predictive analytics and forecasting, using models such as XGBoost, ensembles, and LSTM for tasks like demand, churn, price, or trend prediction.
• FineTuning : Model fine-tuning and domain adaptation for language models using LoRA and QLoRA, so a model speaks your domain without huge compute.
• MLOps: reproducible pipelines with MLflow and DVC, containerized training, and model serving so your model does not stay stuck in a notebook.
• AI integration into your existing SaaS or app: adding an AI assistant, smart search, or automation feature directly into your current product, wired into your existing auth and data.
𝗧𝗼𝗼𝗹𝘀 𝗮𝗻𝗱 𝘀𝘁𝗮𝗰𝗸 𝗜 𝘂𝘀𝗲:
Python, Scikit-learn, XGBoost, TensorFlow, HuggingFace and PEFT for fine-tuning, MLflow and DVC for tracking and versioning, Docker, FastAPI for serving models as APIs, and Reactjs ,,OpenAI/Claude/LangChain/LangGraph for AI feature integration.
𝗛𝗼𝘄 𝗜 𝘄𝗼𝗿𝗸:
I start from your data (or your product, for integration work) and the decision or feature you want to improve, then choose the right approach rather than the most complex one. I engineer features, train and compare models, and evaluate them honestly with the right metrics. I track every experiment for reproducibility, then package the model—or feature—so it can be served, integrated, and monitored.
𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗮𝗻𝗱 𝗱𝗲𝗹𝗶𝘃𝗲𝗿𝘆:
I validate models against held-out data, watch for overfitting, and report real metrics, not inflated claims. I add tests around the pipeline, handle errors, and document how to retrain, run, or extend the system.
𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁:
I can serve models as clean APIs, containerize them, set up basic monitoring, and integrate AI features directly into your existing product without disrupting what's already live. For production scaling I collaborate with the wider Amilek team.
I communicate in plain language, explain what a model can and cannot do, and share progress and results clearly so you can make decisions with confidence.
If you want a custom model, a forecast, a fine-tuned LLM, or AI features added to your product, tell me about your data or your app and the outcome you are after, and I will suggest a realistic approach and what results to expect.
Steps for completing your project
After purchasing the project, send requirements so Jenil can start the project.
Delivery time starts when Jenil receives requirements from you.
Jenil works on your project following the steps below.
Revisions may occur after the delivery date.
Delivery steps
1. Review your data and prediction goal 2. Clean data and engineer features 3. Train and compare candidate models 4. Evaluate and select the best model 5. Deploy the model as an API and hand over