Machine learning development services from senior ML pod teams
Senior ML engineers and data engineers build predictive models on your data, from feature pipelines to deployment and drift monitoring, in your cloud.
By the Ryz Labs team · Updated October 2026
Ryz Labs provides machine learning development through dedicated pods of senior ML engineers, data engineers and backend engineers who build predictive models on your data (fraud and risk scoring, forecasting, ranking, anomaly detection) and run them in production with feature pipelines, deployment and monitoring. Teams work in your cloud on US business hours, and our engineers are the top 1% of the tens of thousands we have interviewed.
What we build
Machine learning here means models trained on your historical data to predict a number, a class or a ranking. For tabular business data, that is still usually the right tool, and cheaper and more predictable than an LLM. Typical builds:
- Fraud and anomaly detection. Supervised models on labeled cases, combined with unsupervised anomaly scores for patterns nobody has labeled yet, feeding an investigator queue.
- Risk and propensity scoring. Default, churn, lapse or conversion probabilities with calibrated outputs a business rule can threshold.
- Demand and volume forecasting. Hierarchical forecasts by product, region or channel, with backtests and prediction intervals.
- Ranking and recommendations. Learning-to-rank and candidate-generation models for search results, next-best-action and content feeds.
- Feature pipelines. Point-in-time-correct features computed the same way for training and serving, in batch and streaming.
- Model serving. Batch scoring jobs or low-latency APIs, with shadow deployments and A/B tests before full rollout.
- Monitoring and retraining. Data drift, prediction drift and outcome tracking, with retraining pipelines that need human sign-off to promote a model.
How an engagement works
- Talk. We define the decision the model supports, the label, how quickly outcomes become known and what an error costs in each direction.
- Match. We propose a pod for your data platform, typically an ML engineer, a data engineer, a backend engineer and a tech lead, with names and a price.
- Join. The pod works in your repos, warehouse and cloud, attends your standups and reviews results weekly with the people who own the decision.
- Grow. Add models that reuse the same features, or hand over training code, pipelines and dashboards to your data team.
In week 1, the pod audits the data: label quality, coverage, leakage risks and how features would be available at prediction time. By month 1, there is usually a baseline model with an honest backtest on a time-based holdout, compared against the current rules or process. By month 3, typical work is serving, monitoring and a shadow or limited production run. Scope and data access set the real pace.
The stack our teams work in
| Layer | Tools we use | Notes |
|---|
| Modeling | scikit-learn, XGBoost, LightGBM, CatBoost, PyTorch | Gradient boosting first for tabular data; deep learning when the data calls for it. |
| Data and features | SQL, Spark, dbt, Snowflake, Databricks, Postgres | Point-in-time joins to avoid leaking future data. |
| Feature stores | Feast, SageMaker Feature Store, Databricks Feature Store | Only when several models share features online. |
| Experiment tracking and registry | MLflow, SageMaker, Azure Machine Learning | Every model traceable to its data, code and parameters. |
| Serving | SageMaker endpoints, Azure ML endpoints, FastAPI on Kubernetes, batch jobs | Batch where latency allows; it is simpler to run. |
| Monitoring | Evidently, CloudWatch, Azure Monitor, Grafana | Drift, latency and business outcome dashboards. |
How we keep models honest in production
Many ML models look excellent in a notebook and disappoint in production. The causes are well known, and a senior team checks for each:
- Target leakage. A feature that is only known after the outcome makes offline scores look great. We review every feature for when it becomes available and use point-in-time joins.
- Random splits on time-ordered data. Shuffled splits overstate accuracy. We validate on later periods than we train on, the way the model will actually be used.
- Training-serving skew. Features computed differently in the pipeline and the API produce different predictions. One feature definition serves both.
- Misleading metrics on rare events. With 0.5% fraud, accuracy is meaningless. We use precision-recall curves, cost-weighted thresholds and the capacity of the review team.
- Uncalibrated scores. If a 0.8 score does not mean roughly 80%, downstream rules break. We check calibration and correct it.
- Feedback loops. Models that decide which cases get reviewed also shape their future labels. We keep a random holdout sample to measure true performance.
- Drift. Customer behavior and fraud patterns change. Monitoring compares live inputs and outcomes against training data and triggers review.
- Explanations for reviewers. SHAP-style feature attributions give investigators and model risk reviewers a reason for each score.
One of our pods built AI fraud detection for a global fleet company: 244K+ records scored at under 30 seconds each and $5.94M in fraud confirmed by the client's own fraud team. See the case studies.
Team shapes and cost
Ryz engineers typically cost $7,000 to $15,000 per engineer per month: mid-level (comparable to Amazon L5) $7,000 to $10,000, senior (comparable to Amazon L6) $10,000 to $15,000, and leads $15,000+.
- Model pod: a tech lead, a senior ML engineer and a senior data engineer. 1 × $15,000+ plus 2 × $10,000 to $15,000 = about $35,000 to $45,000+ per month.
- Production ML pod: about 7 senior engineers, including a tech lead, ML engineers, data engineers and backend engineers. 1 × $15,000+ plus 6 × $10,000 to $15,000 = about $75,000 to $105,000+ per month.
- Added ML capacity: 1 senior ML engineer or data scientist on your team at $10,000 to $15,000 per month.
Project cost is team size × duration × monthly rate. You get a scoped plan, a price and the names of the people before you start.
Dedicated team or staff augmentation?
A dedicated AI pod team fits when you need a model built and operated end to end, especially if you lack data engineering capacity. If you already have a data science team and need more people, staff augmentation is the faster route: hire machine learning engineers, data scientists or MLOps engineers who work on your team.
When Ryz isn't the right fit
If a vendor's packaged scoring model already works on your data and you are allowed to use it, buy it. If you want a proprietary ML platform to license, or need data scientists in European or Asian time zones, other providers fit better. Academic research with no production target is also outside our work.
Related
FAQ
Do we need machine learning or an LLM?
If you are predicting an outcome from structured historical data, such as fraud, churn or demand, classical ML is usually more accurate, cheaper and easier to explain. LLMs fit unstructured text and language tasks. Many systems use both, for example an LLM extracting fields that feed an ML model.
How much data do we need?
It depends on how rare the outcome is and how noisy the labels are. The first weeks of an engagement answer this on your data, with a baseline model and learning curves, before you commit to a full build.
What does machine learning development cost?
Ryz engineers typically cost $7,000 to $15,000 per engineer per month, leads from $15,000. A three-person model pod is about $35,000 to $45,000+ per month, and total cost is team size × duration × monthly rate.
How fast can an ML team start?
After the scoping call we propose a team with names. Most of the timeline depends on scope and on how quickly your team can grant access to the data and the cloud environment.
Who owns the model?
You do. Code, training pipelines, model artifacts and data stay in your repos and accounts.
Questions we didn't answer? Email info@ryzlabs.com.