NLP, Neural Networks & Time Series
Deep Learning
When classical ML hits its ceiling, deep learning breaks through. We architect and train neural networks for NLP, sequence modeling, and time-series problems where traditional approaches fall short.
Challenges We Solve
Sound Familiar?
- Unstructured text data sitting unused because classical NLP can't process it reliably
- Time-series patterns too complex for statistical models
- Sentiment and intent analysis that misses nuance in industry-specific language
- High-dimensional data where feature engineering is impractical
- Need for domain-specific models that out-perform generic pre-trained APIs
Our Approach
How We Help
NLP & Text Intelligence
Named entity recognition, text classification, sentiment analysis, and intent detection using fine-tuned transformer models for domain-specific language.
Time-Series Deep Learning
LSTM, Temporal Convolutional Networks, and Transformer-based models for complex sequence prediction and anomaly detection.
Model Fine-Tuning
Fine-tune HuggingFace transformer models on your domain data for text classification, span extraction, and generation tasks.
Embedding & Semantic Similarity
Custom embedding models for semantic search, document similarity, and representation learning over proprietary data.
Tech Stack
Technologies We Use
How We Work
Delivery Process
Capability Assessment
Determine whether deep learning is the right tool — we only recommend it when classical approaches genuinely can't solve the problem.
Data Preparation & Labeling
Tokenization, augmentation, and labeling pipeline design. We help structure annotation workflows for custom NLP tasks.
Baseline & Architecture Selection
Benchmark pre-trained models before training from scratch. Select architecture based on task, latency, and compute budget.
Training & Fine-Tuning
Distributed training on Azure ML compute clusters with mixed-precision training, learning rate scheduling, and early stopping.
Quantization & Optimization
Post-training quantization, ONNX export, and latency profiling to meet production inference requirements.
Deployment & Monitoring
Serve via TorchServe or Azure ML managed endpoints with latency monitoring and model drift tracking.
What You Get
Deliverables
Every engagement has a defined scope and concrete outputs. No vague “consulting reports” — you get production-ready artifacts.
- Fine-tuned or custom-trained deep learning model
- ONNX-optimized model for production serving
- Model card with performance benchmarks and known limitations
- Training pipeline (reproducible, version-controlled)
- Inference API with batching and async support
- Evaluation report with precision, recall, and latency benchmarks
Why StarkLogik
What Makes Us Different
Pragmatic Architecture Choices
We start with the simplest model that solves the problem. Deep learning has real compute costs — we only go there when the business case justifies it.
Domain-Specific Fine-Tuning
Generic pre-trained models underperform on specialized vocabulary. We fine-tune on your domain data to get the accuracy that matters for your use case.
Inference-Ready Delivery
Every model we deliver is optimized for inference — quantized, profiled, and benchmarked at the target latency and throughput before handoff.
FAQs
Common Questions
Get Started
Ready to Get Started with Deep Learning?
Book a free 30-minute call with our engineering team to discuss your use case.