Machine Learning Engineering
Custom ML models trained on your data — vision, time-series, and tabular — packaged as production APIs with monitoring and re-training pipelines.
Who this is for
Teams who need a custom model, not an LLM wrapper, and want it to keep working in 6 months.
What problem this solves
An LLM API call costs $0.03 forever. A custom model costs $0.0001 once it's running, but requires real engineering to train, serve, monitor, and re-train. Most consultancies stop at the Jupyter notebook.
What you get
- Trained model checked into MLflow with metrics, lineage, and a documented baseline
- FastAPI inference service in a Docker container, deployed to your infra
- Prediction monitoring + drift detection
- Re-training pipeline triggered on data drift or schedule
How the engagement runs
- Problem framing. Classification vs. regression vs. recommendation, success metric, baseline accuracy required.
- Data audit + labelling plan. Tell you what data you need, what's missing, and how to get there.
- Training. Baseline → tuned → ensembled. Each step logged in MLflow with experiment-level diffs.
- Production. FastAPI + Docker, A/B framework for shadow deployment, monitoring + alerts.
Deliverables
- Training pipeline (Python) checked into your repo
- Production FastAPI inference service
- MLflow experiment registry
- Monitoring dashboard (Grafana / Sentry / custom)
- Re-training pipeline (Airflow or simple cron)
Outcomes you can expect
- Production accuracy at or above the baseline you set
- Inference cost an order of magnitude below an LLM API call
- Model card documenting limits, biases, and known failure modes
Pricing & timeline
Model training + production deploy: $12K–$45K USD. Vision-API engagements $15K–$60K USD.
First model in production in 4–8 weeks; ongoing re-training is a separate retainer.
Tech stack
- PyTorch, Keras, TensorFlow, scikit-learn, XGBoost, LightGBM
- FastAPI, Docker, Kubernetes (when needed)
- MLflow, DVC for experiment + data versioning
- Apache Airflow for re-training
- Computer vision: YOLO, ResNet, custom CNNs
- Time-series: ARIMA, Prophet, gradient-boosted trees, neural nets
Relevant case studies
- AgenticAI - AI-Powered CV Screening Platform — An intelligent recruitment platform that uses AI to analyze and rank CVs against job requirements, helping companies find perfect candidates in minutes instead of weeks.
- Enterprise Data Pipeline & Analytics Engine — A production-grade data engineering pipeline processing 10M+ records daily with automated ETL workflows, real-time analytics, and comprehensive business intelligence reporting.
- Deep Learning Image Classification & Object Detection API — A production ML API for image classification and object detection using PyTorch and Keras, deployed with FastAPI and Docker for scalable inference.
- Statistical Analysis & Predictive Modeling Suite — A comprehensive statistical analysis platform combining Python and R for advanced analytics, predictive modeling, and automated report generation.
Frequently asked questions about ML engineering
- When should I use a custom ML model vs. an LLM API call?
- Use an LLM when the task is open-ended language and your volume is low (under ~100k requests/month). Train a custom model when the task is narrow (classification, detection, ranking) and your volume justifies the upfront cost — typically beyond ~500k inferences/month, or when latency below 100ms matters.
- Do you fine-tune LLMs?
- Yes — LoRA / QLoRA fine-tuning on open-source LLMs (Llama, Mistral, Qwen) when you need a smaller, cheaper, on-prem model that knows your domain. We tell you when fine-tuning is the right call vs. RAG vs. prompting.
- How do you handle data drift?
- Two layers: (1) feature-distribution monitoring at inference time, (2) prediction-quality monitoring against a delayed-label backfill. When either crosses threshold, the re-training pipeline kicks off automatically.
- What about explainability?
- SHAP for tabular and tree-based models. Grad-CAM for vision. Model cards for everything. If the model will inform a regulated decision (medical, financial, hiring), explainability is part of the spec from day one.
- Can you work on GPU-heavy training?
- Yes. We've trained on Lambda Labs, RunPod, AWS p3/p4 instances, and on-prem GPUs. We tell you the cost up front and stop at the budget.
- What if the model doesn't hit the accuracy target?
- We agree on a stop-loss in the SOW. If after the first training round the baseline is unreachable, we pause, do a data audit, and tell you what would unblock it (more data, better labels, different architecture). You don't pay for the second round of training without your approval.