Machine Learning · Business Strategy
MLdeepBS is a new ML consultancy built for businesses ready to move beyond dashboards — delivering end-to-end machine learning systems that create measurable, strategic impact.
ML Consulting · Est. 2025
Who we are
MLdeepBS is a brand new machine learning consultancy focused on one thing: making AI work for business — not just in research papers or demos, but in production systems that drive real revenue, reduce costs, and create competitive advantage.
The company was founded on the belief that the hardest part of ML isn't the model — it's the strategic alignment, data readiness, and organizational trust that turn a good algorithm into a good decision. MLdeepBS bridges that gap.
Every engagement is built on transparency: clients have full visibility into processes, code, and infrastructure from day one. No black boxes — in the models or the working relationship.
Capabilities
Time-series models (Prophet, LSTM, XGBoost) applied to supply chain, pricing, and revenue prediction — with confidence intervals that executives can act on.
Fine-tuning and RAG pipelines for customer support automation, document intelligence, sentiment analysis, and internal knowledge bases.
Churn prediction, CLV modeling, segmentation, and recommendation systems that translate directly into retention and revenue outcomes.
Reliable ML pipelines with Airflow, Docker, and cloud platforms. Model monitoring, A/B testing infrastructure, and CI/CD for data science teams.
Experimental design, difference-in-differences, and propensity score matching to answer the questions correlation can't — "What actually caused this?"
Working with leadership to identify high-ROI AI opportunities, assess build-vs-buy decisions, and build data-ready organizations.
Selected Work
Built a full regression pipeline to predict residential sale prices from 220+ engineered features. Applied LASSO regression with 7-fold cross-validation and LassoCV hyperparameter tuning, achieving ~90% variance explained and a mean prediction error of ~$20–23k on a median home price of $163k.
Developed a clustering model to identify which NBA teams most closely match the statistical profile of historical champions. Using advanced regular-season rankings (offensive/defensive rating, net rating, true shooting %, PIE), the model clusters teams by proximity to the championship centroid — correctly placing the 2023 Denver Nuggets in the top 3 most championship-like teams.
Track Record
End-to-end pipeline on 1,460 observations: two-stage missing value imputation, five engineered features (TotalSF, TotalBath, HouseAge, RemodAge, IsRemodeled), one-hot encoding of 220+ features, and StandardScaler normalization. LassoCV with 200 α candidates selected optimal regularization via 7-fold CV. Final model retained ~80–90 non-zero coefficients; top predictors were OverallQual, TotalSF, and neighborhood dummies. CV R² ≈ 0.90, RMSE ≈ $21k.
Clustered 30 NBA teams against a championship centroid derived from historical champions' advanced regular-season rankings. Eight statistical dimensions — offensive/defensive/net rating, true shooting %, effective field goal %, rebound %, assist ratio, and PIE — were selected for low standard deviation among champions. Euclidean distance to the centroid ranked teams by championship likelihood; validated against the 2023 season, with Denver Nuggets finishing 3rd most similar.
Data Governance
No client data is ever removed from the client's environment. All processing, training, and analysis occurs within infrastructure the client controls. MLdeepBS operates on the data — not around it.
The client has complete access to every stage of every engagement — code, pipelines, model artifacts, and documentation. All processes are auditable, versionable, and fully transferable at project close.
Any use of third-party cloud services (AWS, GCP, Azure, or otherwise) requires explicit prior authorization from the client. MLdeepBS does not provision external resources independently. The client owns all accounts and credentials involved.
Any device used to run client processes becomes part of the client's infrastructure from day one. Access credentials, environments, and machine configurations are handed over to the client and remain under their governance throughout and after the engagement.
How we work
End-to-end ML projects scoped by deliverable. Timelines, milestones, and ownership are agreed upon before work begins. Clients receive full handoff documentation and model ownership at completion.
Structured ML training for client teams — curriculum designed around the same hands-on learning methods behind MLdeepBS's own expertise. Sessions are remote, held on a fixed weekly schedule, and run 2 to 4 hours per week. Content is adapted to each team's starting point and business context.
Post-deployment monitoring, retraining pipelines, and performance audits to ensure models remain accurate as data distributions evolve. Clients are never left alone after go-live.
Get in touch
Open to consulting engagements, research collaborations, and speaking. Reach out to start a conversation.