kumarchinnakali Goto Github PK

ai-fundamentals

Study notes for the Azure AI Fundamentals

airflow-site

Apache Airflow Website

apijam

GitHub repo for API Jam Workshop

apollo

Mesos cluster provisioning and orchestration using Packer and Terraform. An open-source platform for apps, microservices and big data.

apple-search-ads

API to fetch Apple Search Ads data

autom8d-basics-to-guru

awesome-adtech

😎An awesome list of awesome adtech things curated by the community.

awesome-apache-airflow

Curated list of resources about Apache Airflow

awesome-gcp-certifications

Google Cloud Platform Certification resources.

awesome-generative-ai-guide

A one stop repository for generative AI research updates, interview resources, notebooks and much more!

aws-sa-associate-saac02

Course Files for AWS Certified Solutions Architect Certification Course (SAAC02) - Adrian Cantrill

aws-saa-certification

AWS Solutions Architect Associate Crash Course

azure-data-guide

Azure Data Architecture Guide

azure-quickstart-templates

Azure Quickstart Templates

bds-files

Supplementary files for my book, "Bioinformatics Data Skills"

big-data-analytics-using-splunk

Source code for 'Big Data Analytics Using Splunk' by Peter Zadrozny and Raghu Kodali

bigdl

BigDL: Distributed Deep Learning Library for Apache Spark

blockchain-for-beginners

Blockchain for beginners [video], published by Packt

cloudco-insurance

A modern insurance company. The application showcases cognitive and cloud computing ideas in the context of insurance.

contoso-chat

data-science-on-gcp

Source code accompanying book: Data Science on the Google Cloud Platform, Valliappa Lakshmanan, O'Reilly 2017

databot

Safecast tool to convert bGeigie logs to static pdf maps

databricks-spark-certification

Guide for databricks spark certification

datasciencecoursera

Data Science Course @ Coursera

datasharing

The Leek group guide to data sharing

digital-foundry-demand-forcasting

In tune with conventional big data and data science practitioners’ line of thought, currently causal analysis was the only approach considered for our demand forecasting effort which was applicable across the product portfolio. Experience dictates that not all data are same. Each group of data has different data patterns based on how they were sold and supported over the product life cycle. One-methodology-fits-all is very pleasing from an implementation of view. On a practical ground, one must consider solutions for varying needs of different product types in our product portfolio like new products both evolutionary and revolutionary, niche products, high growth products and more. With this backdrop, we have evolved a solution which segments the product portfolio into quadrants and then match a series of algorithms for each quadrant instead of one methodology for all. And technology stack would be simulated/mocked data(Hadoop Ecosystem) > AzureML with R/Python > Zeppelin.

eks-training

Materials for an online EKS class

excalidraw

Virtual whiteboard for sketching hand-drawn like diagrams

gcp-cud-faq

Tips on how to use commited use discounts on Google Cloud Plataform

gcpsafari

GCP Class Repository