Standard solutions using statistics and probability to design, train, execute, and evaluate predictive machine learning models. Use-case-based feature engineering, design, implementation, and evaluation building blocks for customized solutions. Big data ML pipelines on cloud and Hadoop using Spark.
Data Analytics Services
Services
Data Analytics Components
Data warehouse models, Spark, Cloud, Hadoop, and Kubernetes deployments.
Data Visualization
Tableau, PowerBI, Qlik, and Superset.
Standard Model Implementation
Spark generics classification implementation and Spark generics recommendation implementation. Spark generics time-series implementation, standard feature engineering, ETL building blocks, standard hyperparameter tuning, cross-validation, and grid search in Spark ML.
Secure Notebook Deployment
Dynamic privileges and secure containerization allows for robust deployment of containers in Zeppelin and Jupyter.
