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Machine Learning & Computer Vision

Custom Machine Learning & Computer Vision

For many problems, classical machine learning is still the right tool: cheaper, faster and more accurate on structured data and images. I build and deploy models with PyTorch, TensorFlow and scikit-learn, from medical-imaging classifiers to demand-forecasting systems.

At a glance

  • Decisions from data
  • Vision that scales
  • Honest evaluation
  • Production-ready
Book a free discovery call See related case studies
Outcomes

What you get from this engagement.

  • 01

    Decisions from data

    Forecasts and predictions your team can act on.

  • 02

    Vision that scales

    Image classification and screening at volume.

  • 03

    Honest evaluation

    Clear metrics on held-out data, not cherry-picked demos.

  • 04

    Production-ready

    Models served as APIs with monitoring.

Deliverables

Scope, made concrete.

  • Data audit and feasibility assessment
  • Feature engineering and model development
  • Computer vision with transfer learning
  • Forecasting and statistical modeling
  • Evaluation report
  • Deployment as an API with monitoring
Use cases

Where it delivers value.

  • Medical image screening
  • Demand forecasting
  • Classification and scoring
  • NLP and text analytics
  • Anomaly detection

Technology

  • PyTorch
  • TensorFlow
  • Keras
  • scikit-learn
  • Pandas
  • NumPy
  • Hugging Face
  • FastAPI
  • Docker
  • AWS
Proof

Related case studies.

Real systems delivered with this approach, with the constraints and results that came with them.

Healthcare5 stages
  1. 01Scan
  2. 02Prepare
  3. 03Backbone
  4. 04Classify
  5. 05Evaluate

Stage 1, Scan: Medical image intake. Stage 2, Prepare: Image preprocessing. Stage 3, Backbone: Pretrained CNN (transfer learning). Stage 4, Classify: Tumor and retinopathy heads. Stage 5, Evaluate: Held-out validation.

01Medical Imaging AI

Medical Imaging Diagnostics with Computer Vision

CNN classifiers for brain-tumor detection and diabetic-retinopathy screening, built with transfer learning in TensorFlow as decision support for faster triage of medical scans.

Diagnostic screening models
2
Transfer-learning architecture
CNN
Training framework
TensorFlow
Read case study
How we work

A delivery process built for zero surprises.

Clear milestones, working demos every week and measurable quality gates, so you always know what's done, what's next and what it costs.

  1. 01

    Discover

    A free 30-minute call to understand the problem, the data and what success looks like.

    You getClear problem statement and success metrics

  2. 02

    Architect

    A written technical plan: architecture, model and vector-store choices, risks, milestones and a fixed quote.

    You getArchitecture document and milestone plan

  3. 03

    Build

    Iterative delivery in short milestones with working demos, so you see progress every week, not at the end.

    You getWorking increments you can test

  4. 04

    Evaluate & harden

    Measured against an evaluation set, then hardened with guardrails, access control, error handling and load testing.

    You getEvaluation report and test suite

  5. 05

    Deploy & support

    Dockerized deployment to your cloud, documentation and handover, with optional ongoing support and improvement.

    You getProduction system plus documentation

AI project estimator

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FAQ

Machine Learning & Computer Vision: common questions.

How much data do we need?

Less than you might think when transfer learning applies. A feasibility assessment on a sample of your data tells us early whether the target accuracy is realistic.

Can you deploy the model for us?

Yes. Models ship as containerized APIs with documentation, and with monitoring when needed.

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Available for new engagements

Let’s scope your Machine Learning & Computer Vision project in 30 minutes.

Free discovery call. You'll leave with a clear view of the architecture, risks and next steps, whether or not we work together. I reply within 24 hours.

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