ClickMasters builds deep learning solutions for B2B companies across the USA, Europe, Canada, and Australia. CNN-based image classification and object detection. Transformer-based NLP models for text classification and named entity recognition. LSTM and Transformer models for time series forecasting. Tabular deep learning for high-dimensional structured data. Transfer learning from pre-trained models when labelled data is limited. Deployed with ONNX Runtime or TorchServe for production inference.
Years Experience
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Deep learning is not universally better than classical ML. Choose gradient boosting (XGBoost, LightGBM) over deep learning when: the data is tabular and structured (gradient boosting consistently outperforms neural networks on tabular data with fewer samples); the dataset is small (<10,000 labelled examples deep networks overfit on small datasets where gradient boosting generalises better); interpretability is required (gradient boosting + SHAP is more interpretable than a deep network); or inference latency and computational cost are constraints (gradient boosting inference is orders of magnitude cheaper than a neural network on the same hardware). Choose deep learning when: the input is unstructured (images, text, audio, video); the dataset is large (100,000+ labelled examples); the prediction task requires learned representations that feature engineering cannot capture; or transfer learning from a pre-trained model provides a shortcut that makes the dataset size problem tractable.
Transfer learning uses a model pre-trained on a large general dataset as the starting point for training on a smaller task-specific dataset rather than training from random weights. For computer vision: a ResNet or EfficientNet pre-trained on ImageNet (1.2M labelled images, 1,000 classes) has learned general visual features edges, textures, shapes that transfer usefully to almost any visual recognition task. Fine-tuning this pre-trained model on 1,000-10,000 domain-specific labelled images produces better results than training from scratch on the same data. For NLP: BERT and its variants (RoBERTa, DeBERTa) pre-trained on billions of words have learned language representations that transfer to classification, NER, and QA tasks with 100-10,000 labelled examples. Transfer learning makes deep learning practical for B2B use cases where labelling costs limit dataset size.
PyTorch and TensorFlow are both production-grade deep learning frameworks, but they have evolved differently. PyTorch uses a dynamic computation graph (define-by-run) operations execute immediately when called, making debugging intuitive and code that looks like standard Python. PyTorch is the dominant framework in ML research (85%+ of papers) and increasingly in production. TensorFlow uses a static computation graph that is defined and then executed offering production deployment advantages (TensorFlow Serving, TFLite for mobile, TFX for pipelines) but historically more complex debugging. With the adoption of PyTorch 2.0's torch.compile and TorchServe for production serving, and ONNX for cross-framework deployment, the production deployment gap has largely closed. ClickMasters uses PyTorch as the primary framework for all new deep learning work, with TensorFlow for legacy model maintenance and TFLite targets.
What we deliver
05 capabilities
ClickMasters operates as a full-stack deep learning solutions partner — product strategy, UI/UX, engineering, cloud infrastructure, QA, and ongoing support in one delivery model.
CNN-based vision: image classification (ResNet, EfficientNet, ViT with transfer learning), object detection (YOLO v8 or DETR for real-time, Faster R-CNN for high accuracy), instance segmentation, OCR + layout analysis (Tesseract + LayoutLM). Transfer learning from ImageNet-pretrained weights requires only 1,000-10,000 domain-specific labelled examples.
Transformer-based NLP: text classification (fine-tune BERT, RoBERTa, DeBERTa on labelled examples), named entity recognition (domain-specific entity types), sequence-to-sequence (T5/BART for summarisation, translation), multi-label classification. Hugging Face Transformers with 500,000+ pre-trained checkpoints.
Neural architectures for forecasting when classical models insufficient: LSTM (long-range dependencies), Temporal Fusion Transformer (attention-based, interpretable multi-horizon probabilistic forecasts), N-BEATS (univariate, interpretable trend/seasonality), N-HiTS (hierarchical forecasting).
Neural networks for high-dimensional structured tabular data where gradient boosting hits limits: TabNet (attention-based, interpretable feature selection), FT-Transformer (feature tokenisation + transformer, strong on high-cardinality categoricals), SAINT (self-attention + intersample attention), NODE (neural oblivious decision ensembles).
Maximising performance with limited labelled data: pre-trained model fine-tuning, few-shot learning (prototypical/siamese networks 5-20 examples per class), active learning (reduce labelling cost 50-70%), semi-supervised learning (consistency regularisation, pseudo-labelling).
Why choose us
05 advantages
We combine architecture discipline, transparent delivery, and long-term partnership — so your investment translates into measurable business results, not just shipped code.
"When DL is NOT the right choice" amber callout XGBoost for tabular | Basic: Deep learning for everything (overkill, slower, less interpretable)
Pre-trained models from ImageNet (vision) and Hugging Face (NLP) 1,000-10,000 examples sufficient | Basic: Training from scratch (requires millions of examples)
ONNX Runtime (2-5x faster), TorchServe, quantisation | Basic: Naive PyTorch inference (slow, high latency)
Interpretable multi-horizon probabilistic forecasts with variable importance | Basic: Black-box LSTM (no explanation of drivers)
PyTorch DDP for multi-GPU training | Basic: Single GPU training (slow on large datasets)
500+
Companies served
4.9/5
Client rating
15+
Years in delivery
Scroll to walk through each phase — lines connect as you move down.
Use case analysis, deep learning vs classical decision (tabular DL only for high-dim/high-cardinality data), architecture selection (CNN/Transformer/LSTM/TFT), data requirements assessment (minimum labelled examples for transfer learning), transfer learning strategy. Deliverable: Deep Learning Architecture Design.
Dataset curation, labelling (Label Studio), data augmentation (vision: random crop, flip, rotation, MixUp, CutMix; NLP: back-translation, word dropout), train/validation/test split stratified by key attributes. Deliverable: Prepared Dataset + Augmentation Pipeline.
Load pre-trained model (ImageNet for vision, Hugging Face for NLP), freeze backbone, train new head on domain data, unfreeze backbone for fine-tuning (lower learning rate), early stopping, learning rate scheduling (cosine annealing). GPU training with PyTorch DDP for multi-GPU. Deliverable: Trained Model + Checkpoints.
Test set performance: classification (accuracy, precision/recall, F1, AUC-ROC), object detection (mAP), segmentation (IoU). Calibration check, failure case analysis, model card documentation (intended use, limitations, performance across groups). Deliverable: Model Evaluation Report + Model Card.
ONNX export (2-5x faster inference than PyTorch), TorchServe or Triton Inference Server deployment, batching for throughput, GPU inference (G5 instances) or CPU for batch, monitoring (latency distribution, throughput, prediction drift). Deliverable: Production Inference API.
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Deep expertise across various sectors with tailored solutions
Pricing
Transparent pricing tailored to your business needs
* All prices are estimates and may vary based on requirements.
To build scalable, intelligent custom software development solutions that empower businesses to grow, automate, and transform in a digital-first world.
We are not building software. We are architecting the infrastructure of tomorrow—systems that think, adapt, and grow alongside the businesses they power.
Amjad Khan
Chief Executive Officer
12+
Years Exp
300+
Success
98%
Retention
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