What we're about

A group for anyone interested in learning and getting hands-on practice on Big Data, Streaming, Spark, Hive, Pig , Kafka, Data Engineering, Data Visualization, Cloud, Amazon, AWS etc..

Upcoming events (3)

Scale Out and Conquer: Distributed In-Memory Systems Architectural Decisions

ATTENTION: The health and well being of our Data Riders community is our highest priority. Due to the existing Coronavirus environment, we are moving all our meet up events online for the foreseeable future, until things get back to normalcy. Participation is completely ONLINE for this event!! WEBINAR LINK: https://global.gotomeeting.com/join/123655917 ABSTRACT: Distributed platforms, like Apache Ignite, rely on horizontal scalability. More machines in the cluster lead to the greater performance of applications. But, do we always get twice the speed after adding the second machine to the cluster? Ten times faster after adding ten machines? Is that [always] true? What is the responsibility of the platform? And where do engineers’ responsibilities begin? In this talk attendees will learn about the compromises and pitfalls architects face when designing distributed systems: Advantages and disadvantages of different data-sharding algorithms. Effective data models for distributed environments. Synchronization and coordination in distributed systems. SPEAKER BIO: Valentin Kulichenko, Solutions Architect at GridGain Systems LinkedIn: https://www.linkedin.com/in/vkulichenko/ Valentin Kulichenko is a software engineer, solutions architect, and a distributed systems enthusiast. He has been working with in-memory distributed systems since 2010. Valentin currently holds the position of Lead Architect at GridGain Systems. He contributes to GridGain’s Enterprise products, works directly with large scale customer deployments, manages proof-of-concept projects and technical support engagements. He is also a passionate open-source Apache Ignite community member. He dedicates his time to public speaking, contributing code and providing technical help through the dev and user mailing lists.

Hands-on workshop: Image Recognition using PyTorch and RedisAI - Part 1

ATTENTION: Due to the prevailing Coronavirus environment, we are moving all our meet-up events online until things get back to normalcy. Participation is completely ONLINE for this event!! WEBINAR LINK: TBD ABSTRACT Join us for a hands-on workshop where you will learn how to get started in Image Recognition with PyTorch Deep Learning framework and RedisAI. AGENDA: 1) Learn fundamentals of Convolutional Neural Networks: convolutions, pooling, fully connected layers, training with stochastic gradient descent. 2) Apply transfer learning to solve real-world Image Recognition problems. 3) Hands-on Image Recognition workshop using PyTorch. 4) Deploy PyTorch model to RedisAI for Production use. SPEAKER: Alex Kalinin currently leads AI/Machine Learning at Facebook Ads team. Previously, he developed smart home software at Home.ai using computer vision and deep learning. At Yahoo he led development of Big Data user acquisitions systems for Yahoo Games business. Alex holds MS in Physics, and published several papers on Image Recognition and Pattern detection. LinkedIn: https://www.linkedin.com/in/alexkalinin/ PRE-REQUISITES: Instructions would be sent out before the event via email.

Hands-on workshop: Image Recognition using PyTorch and RedisAI - Part 2

ATTENTION: Due to the prevailing Coronavirus environment, we are moving all our meet-up events online until things get back to normalcy. Participation is completely ONLINE for this event!! WEBINAR LINK: TBD ABSTRACT Join us for a hands-on workshop where you will learn how to get started in Image Recognition with PyTorch Deep Learning framework and RedisAI. AGENDA: 1) Learn fundamentals of Convolutional Neural Networks: convolutions, pooling, fully connected layers, training with stochastic gradient descent. 2) Apply transfer learning to solve real-world Image Recognition problems. 3) Hands-on Image Recognition workshop using PyTorch. 4) Deploy PyTorch model to RedisAI for Production use. SPEAKER: Alex Kalinin currently leads AI/Machine Learning at Facebook Ads team. Previously, he developed smart home software at Home.ai using computer vision and deep learning. At Yahoo he led development of Big Data user acquisitions systems for Yahoo Games business. Alex holds MS in Physics, and published several papers on Image Recognition and Pattern detection. LinkedIn: https://www.linkedin.com/in/alexkalinin/ PRE-REQUISITES: Instructions would be sent out before the event via email.

Past events (46)

Building a Grown Up Database

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