Machine Learning, TimeSeries Forecasting, Predictive Analytics on AWS

Many organizations face challenges in scaling machine learning, including fragmented datasets and high inference costs. Goaltech addresses these issues by creating unified AWS native ML architectures that automate feature engineering, training, deployment, and monitoring, integrating classical ML, deep learning, time series forecasting, and GenAI for optimal performance and governance.

WHAT WE DELIVER

ML and forecasting strategy • Use case selection aligned with financial impact, risk reduction or operational KPIs • Data quality analysis, feature gap assessment and ML readiness scoring • Forecasting suitability analysis for Amazon Forecast versus custom deep learning models • Architecture blueprint for training, inference, feature engineering and monitoring • Compliance checks for responsible AI, governance and auditability

Advanced feature engineering and data foundation • Feature pipelines using SageMaker Processing, Glue and Lambda • SageMaker Feature Store for consistent online and offline feature access • Dataset optimization including sampling, balancing, normalization and embedding creation • Data quality validation with drift detection and monitoring dashboards

Predictive machine learning models • Advanced tabular ML using XGBoost, CatBoost and ensemble models • Deep learning models for structured, sequential and unstructured data • Explainability using SHAP, LIME and model interpretability reports • Bias detection and governance using SageMaker Clarify

Time series forecasting and demand prediction • Amazon Forecast for scalable multi item forecasting workloads • Deep learning forecasting with DeepAR, DeepState, LSTM and N Beats models • Hierarchical forecasting with reconciliation for retail or telecom environments • Intermittent demand modeling for slow moving or sparse datasets • Promotion uplift modeling and event based forecasting • Evaluation using RMSE, MAPE, WAPE, P50 or P90 intervals and probabilistic accuracy

Natural language processing and text intelligence • Text classification, intent detection and sentiment analysis • Document extraction using Textract, Comprehend and Bedrock enhanced workflows • Sequence labeling, entity extraction and topic modeling • Hybrid ML and RAG pipelines for enhanced text reasoning

Computer vision and image intelligence • Object detection, image classification and anomaly detection • Video analytics using Rekognition for safety and compliance automation • Quality inspection and defect detection for manufacturing workloads • Image based attribute extraction for retail and product catalogs

GenAI and Bedrock assisted ML workflows • Hybrid ML and GenAI workflows that combine ML predictions with GenAI reasoning • Agent driven forecasting explanation, anomaly investigation and automated reporting • Document, image or transcript enhanced predictive features using Textract, Rekognition and Transcribe • Bedrock guardrails for safe responses and compliance alignment

Training optimization and advanced modeling • Distributed training using SageMaker model and data parallelism • Spot training for cost optimized deep learning workloads • Hyperparameter search using SageMaker HPO and Bayesian optimization • Experiment tracking using SageMaker Experiments or MLflow • Automatic checkpointing and resume safe training

MLOps automation and model lifecycle management • CI/CD pipelines for ML using SageMaker Pipelines or Step Functions • Model Registry with approval workflows, versioning and lineage • Multi environment deployment, staging and controlled rollout • Automated retraining based on drift or seasonal changes • Full auditability, metadata capture and reproducibility

Real time, batch and edge inference • Low latency ML inference using optimized SageMaker endpoints • SageMaker Serverless Inference and Async Inference for cost reduction • Multi model endpoints for consolidated inference clusters • Edge deployment for offline or factory environments using SageMaker Edge Manager

Model monitoring, drift detection and governance • Model Monitor dashboards for latency, accuracy and feature stability • Bias and fairness evaluation with alerts on deviation • Logging, tracing and compliance ready audit trails • Secure, permission aware access to predictions and models

ENGAGEMENT MODEL

1. Discovery and assessment We evaluate data, ML maturity, governance needs and business outcomes to define the roadmap.
2. Proof of value We build a functional ML or forecasting pilot including data preparation, model training, evaluation and inference.
3. Production architecture and pipelines We design feature store, training, CI/CD, monitoring and inference pipelines with security and compliance built in.
4. ML deployment and governance rollout We deploy models with controlled rollout, approval workflows, drift detection and reliability testing.
5. Optimization and expansion We tune performance, reduce cost, expand to new use cases and improve forecasting accuracy over time.