Introducción a Machine Learning con Databricks. Introducción a Databricks ML Describir el entorno de Databricks ML Preparar Datos para entrenamiento Entrenar modelos de forma manual o con Auto ML Seguimiento de Modelos Compartir, administrar y servir modelo con el Registro de Modelos.
EducationBachelors’degreeinSystemsEngineeringEscuelaPolitécnicaNacional(Quito)Masters’degreeinBigDataandBusinessAnalyticsIMFBusinessSchoolWorkExperience04/2020–presentmyCloudDoorCloudDataEngineer-Consulting and improvement of advanced analytics and Machine Learning processes with Tensor Flow. Code developlemtfor the optimization of the execution of parallel processes. Continuous improvement of Azure DataBricks jobs and processes together with the DataBricks Europe engineering team.-Technology: Azure, Azure Data Lake, DataBricks, PySpark.-Data Pipeline based on AWS technologies. Data Lake storage with S3 from multiple sources, data orchestration with AWS Glue and AWS Lambda data model creation with Redshift.-CDC (Change Data Capture) development tool. Implementation an analytics tool for Real Time Analytics with Azure.-Technology: Azure, Azure Data Lake, Debezium, Apache Kafka, Dockers, Oracle, Azure PostgreSQL. 03/2020–04/2020SoftConsulting SATechnicalAnalyst-Data Lake creation by extracting business ́s data from different data sources. Lead in design and development of client data strategy for financial department.-Technology: Hadoop, apache spark, shell Linux.01/2015–02/2020ModinterTechnicalAnalyst-CORE banking product development and support. DWH implementations for Data&Analytics banking projects. ETL (Microsoft SSIS) Data integration processes, development, and management.-Technology: SQL Server, Oracle, SSIS, SSAS, SQL, PL/SQL and PowerBI.
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