gitsathish's repositories
interviews.ai
This book was written for you: an aspiring data scientist with a quantitative background, facing down the gauntlet of the interview process in an increasingly competitive field. For most of you, the interview process is the most significant hurdle between you and a dream job.
ai-platform-samples
Official Repo for Google Cloud AI Platform
auto-data-tokenize
Identify and tokenize sensitive data automatically using Cloud DLP and Dataflow
beam-college
Repository for Beam College sessions
bigquery-schema-generator
Generates the BigQuery schema from newline-delimited JSON or CSV data records.
bigquery-utils
Useful scripts, udfs, views, and other utilities for migration and data warehouse operations in BigQuery.
data-engineering-on-gcp
Data Engineering on GCP
data-science-from-scratch
code for Data Science From Scratch book
DataflowTemplates
Google-provided Cloud Dataflow template pipelines for solving simple in-Cloud data tasks
documentai-bounding-boxes
This is tutorial for using DocumentAI
ignite-learning-paths-training-aiml
Microsoft Ignite Learning Path, Train the Trainer materials: Developers Guide to AI
kafka-connect-bigquery
A Kafka Connect BigQuery sink connector
MachineLearningNotebooks
Python notebooks with ML and deep learning examples with Azure Machine Learning | Microsoft
PolicyCenter-8
This repository has been used for two guidewire projects
pratt-savi-810-2020-03
Class for Pratt SAVI 810 2020-03: Intro to Python Scripting for Geospatial
Principles-of-Machine-Learning-Python
Principles of Machine Learning Python
professional-services
Common solutions and tools developed by Google Cloud's Professional Services team
pycon2020-azure-functions
⚡️🙇🏻♀️ Sponsored tutorial content for PyCon 2020
SQL-Managed-Instance-Disaster-Recovery-Architecture-Design
SQL Managed Instance (Disaster Recovery Architecture Design)
streaming-at-scale
How to implement a streaming at scale solution in Azure
support-tickets-classification
This case study shows how to create a model for text analysis and classification and deploy it as a web service in Azure cloud in order to automatically classify support tickets. This project is a proof of concept made by Microsoft (Commercial Software Engineering team) in collaboration with Endava http://endava.com/en
training-data-analyst
Labs and demos for courses for GCP Training (http://cloud.google.com/training).
Utilities
Miscellaneous Utility Development
YCSB
Yahoo! Cloud Serving Benchmark