AI / ML Services | ATRI Systems

AI / ML Development and Services

Data Cleaning: Handling missing values, outliers, duplicate records, and inconsistencies using Pandas and PySpark
Data Augmentation: Techniques such as SMOTE for imbalanced datasets, synthetic data gen-eration, and data transformation
Feature Extraction: Using TF-IDF, Word2Vec, FastText, and BERT embeddings for NLP applications
Automated Data Labeling: Active learning, weak supervision, and human-in-the-loop labeling for scalable dataset annotation
Big Data Processing: Using Apache Spark, Hadoop, and distributed data processing frameworks

Supervised Learning: Regression, classification, time-series casting
Unsupervised: Clustering, dimensionality reduction, anomaly detection
Ensemble Learning: Random Forest, Gradient Boosting, XGBoost
Neural Networks & Deep Learning: CNNs, RNNs, Transformers
Feature selection and engineering using PCA, t-SNE, LDA
Hyperparameter tuning with grid search, random search, and Bayesian optimization
Cross-validation techniques (K-Fold, Leave-One-Out, Stratified) for robust model evaluation
Model interpretability using SHAP, LIME, and Integrated Gradients

Image recognition using CNN architectures like ResNet, VGG, EfficientNet, and Vision Transformers (ViTs)
Sequence modeling with LSTMs, GRUs, Bi-directional Transformers, and attention-based architectures
Generative AI models such as GANs, VAEs, and diffusion models for image and text synthesis
Speech-to-text and text-to-speech processing using deep learning techniques like Wav2Vec and Tacotron

AI Model Training and RAG

Transfer Learning: Utilizing pre-trained models such as BERT, GPT, YOLO, and DALL-E for domain-specific tasks. This significantly reduces training time and improves performance on specialized datasets.
Federated Learning: Secure model training across decentralized data sources to maintain data privacy. We implement frameworks like TensorFlow Federated (TFF) and PySyft to enable training across distributed edge devices.
Neural Architecture Search (NAS): Automating deep learning model design for optimized performance. We utilize AutoML techniques like Google’s AutoML, reinforcement learning-based NAS, and evolutionary algorithms to design optimal neural network architectures.

Data Parallelism: Splitting data batches across multiple GPUs or TPUs, computing gradients independently
Model Parallelism: Distributing different parts of a deep learning model across multiple GPUs,
Pipeline Parallelism: Breaking a deep learning model into sequential stages and processing multiple mini-batches simultaneously
Federated Learning: Training models across decentralized devices without sharing raw data, preserving privacy.
Horovod Integration: Optimized distributed training framework for scaling across multiple nodes, reducing synchronization overhead.

Integrating retrieval mechanisms into transformer models to dynamically fetch relevant data from external sources.
Implementing FAISS, Pinecone, and Weaviate for scalable similarity search and document retrieval.
Hybrid RAG: Combining traditional search techniques (BM25, TF-IDF) with neural search for improved query.
Fine-Tuning RAG: Customizing retrieval-augmented models for domain-specific applications in healthcare, finance, etc
Optimizing prompts and context retrieval using embedding-based approaches like DPR (Dense Passage Retrieval).

Deployment and Maintenance strategies

Privacy-Preserving AI: Implementing differential privacy, homomorphic encryption, federated learning, and secure multi-party computation (SMPC) to ensure user data protection.
AI Robustness & Adversarial Defense: Securing AI models against adversarial attacks using adversarial training, gradient masking, and anomaly detection.
Compliance: GDPR, HIPAA, ISO/IEC 27001, and AI governance frameworks
Model Accountability & Logging: Implementing AI monitoring tools to track decisions, flag biases, and log interactions for auditability.
Secure AI Infrastructure: Using encryption, access controls, and cloud security best practices to protect AI models from cyber threats.

Containerization & Orchestration: Docker, Kubernetes, Kubeflow for scalable AI solutions
Model Serving Frameworks: TensorFlow Serving, TorchServe, NVIDIA Triton for high-performance inference
CI/CD Pipelines for ML: Automating model updates and deployments with Jenkins, GitHub Actions, and ArgoCD
Cloud & Edge AI Deployment: AWS SageMaker, GCP Vertex AI, Azure ML, TensorFlow Lite, ONNX for mobile and IoT devices
Model Monitoring & Drift Detection: Using MLflow, Prometheus, Grafana, and automated retraining strategies

Automated Model Monitoring: Implementing MLOps frameworks for real-time performance tracking and drift detection.
Continuous Learning: Using active learning and reinforcement learning to update models dynamically.
Error Analysis & Debugging: Root cause analysis, counterfactual testing, and adversarial robustness testing.
Version Control & Rollbacks: Managing model versions and deploying fallback mechanisms in production.
Anomaly & Outlier Detection: Using statistical methods and unsupervised learning techniques to detect data distribution shifts.

Expert Team: Deep domain expertise in AI and ML, ensuring high-quality, cutting-edge solutions.
Comprehensive Solutions: End-to-end from data preparation and model training to deployment and maintenance.
Scalability & Performance: By leveraging advanced distributed training and optimization techniques.
Security & Compliance: Ethical AI practices, robust security measures, and compliance with industry standards.
Innovation & Reliability: Continuously improve our models and technologies, ensuring you stay ahead