Machinelearningthatdelivers
Build and deploy production-ready machine learning models. From neural networks to NLP, we engineer AI solutions that deliver measurable business impact.
AI/ML Engineering is included in your allocation
Access this capability with your subscription. No separate pricing.
Production-Ready ML Pipelines
End-to-end machine learning with experiment tracking and deployment
ML Training Pipeline
style ="color: #ff7b72;">import torch
style ="color: #ff7b72;">from transformers style ="color: #ff7b72;">import AutoModelForSequenceClassification
style ="color: #ff7b72;">from sklearn.model_selection style ="color: #ff7b72;">import train_test_split
style ="color: #ff7b72;">from mlflow style ="color: #ff7b72;">import log_metric, log_model
style ="color: #6b7280;"># Load and prepare data
X_train, X_test, y_train, y_test = style ="color: #d2a8ff;">train_test_split(
features, labels, test_size =0.2
)
style ="color: #6b7280;"># Initialize model
model = AutoModelForSequenceClassification.style ="color: #d2a8ff;">from_pretrained(
style ="color: style ="color: #6b7280;">#a5d6ff;">"bert-base-uncased", num_labels =num_classes
)
style ="color: #6b7280;"># Train style ="color: #ff7b72;">with tracking
style ="color: #ff7b72;">for epoch style ="color: #ff7b72;">in style ="color: #d2a8ff;">range(num_epochs):
loss = style ="color: #d2a8ff;">train_epoch(model, X_train, y_train)
accuracy = style ="color: #d2a8ff;">evaluate(model, X_test, y_test)
style ="color: #d2a8ff;">log_metric(style ="color: style ="color: #6b7280;">#a5d6ff;">"accuracy", accuracy, step =epoch)
style ="color: #d2a8ff;">log_model(model, style ="color: style ="color: #6b7280;">#a5d6ff;">"production-classifier")AI/ML capabilities
From data science to deployment, we cover the full ML lifecycle.
Custom ML Models
Purpose-built machine learning models trained on your data. Classification, regression, clustering, and recommendation systems.
Neural Network Architecture
Deep learning solutions with custom architectures. CNNs, RNNs, Transformers, and hybrid models for complex problems.
Computer Vision
Image recognition, object detection, video analysis, and visual inspection systems for automation and insights.
Natural Language Processing
Text analysis, sentiment detection, entity extraction, and language generation with state-of-the-art models.
MLOps & Deployment
End-to-end ML pipelines with automated training, validation, deployment, and monitoring in production.
AI Governance
Responsible AI implementation with bias detection, explainability, and compliance with AI regulations.
Real-World Applications
See how ML transforms business operations.
Predictive Maintenance
ML models that predict equipment failures before they happen. Reduce downtime by 40% and maintenance costs by 25%.
Demand Forecasting
Accurate demand predictions using historical data and market signals. Optimize inventory and reduce stockouts.
Fraud Detection
Real-time anomaly detection systems that identify fraudulent transactions with 99.5% accuracy.
Document Intelligence
Extract structured data from unstructured documents. Automate processing of contracts, invoices, and reports.
Our Tech Stack
PyTorch
Framework
TensorFlow
Framework
Hugging Face
Models
scikit-learn
ML
MLflow
MLOps
Kubeflow
Pipeline
Our ML Development Process
Problem Definition
1-2 weeksDefine the ML problem, success metrics, and data requirements. Assess feasibility and expected ROI.
Data Engineering
2-4 weeksCollect, clean, and prepare training data. Feature engineering and data pipeline development.
Model Development
4-8 weeksTrain, validate, and optimize ML models. Experiment with architectures and hyperparameters.
Production Deployment
2-3 weeksDeploy models with monitoring, A/B testing, and automated retraining pipelines.
Continuous Improvement
OngoingMonitor model performance, retrain on new data, and optimize based on production feedback.
Frequently Asked Questions
We handle classification, regression, clustering, recommendation systems, time series forecasting, NLP, computer vision, and reinforcement learning. If data can inform a decision, ML can likely help.
It depends on the problem complexity. For many tasks, we can use transfer learning with pre-trained models to work with smaller datasets. We'll assess your data situation during discovery.
Yes, we integrate with existing tools and platforms (AWS SageMaker, Azure ML, GCP Vertex AI, etc.) or help you build new infrastructure tailored to your needs.
Rigorous validation with hold-out sets, cross-validation, and A/B testing in production. We monitor for data drift, model decay, and set up automated retraining.
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