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Upcoming events (3)
** Register and attend here:
(Free online AI tech talk event, you can join from anywhere with zoom)
* pre-event networking (20mins)
* community updates, jobs/interns/talents announcements. (5mins)
* tech talk (40mins)
* Q&A and open discussion (10mins)
With the computational advances over the past few decades, Bayesian analysis approaches are starting to be fully appreciated. Forecasting and time series also have Bayesian approaches and techniques, but most people are unfamiliar with them due to the immense popularity of Exponential Smoothing and autoregressive integrated moving average (ARIMA) classes of models.
However, Bayesian modeling and time series analysis have a lot in common! Both are based on using historical information to help inform future modeling and decisions. Using past information is key to any time series analysis because the data typically evolves over time in a correlated way. Bayesian techniques rely on new data updating their models from previous instances for better estimates of posterior distributions.
This talk will briefly introduce the differences between classical frequentist approaches of statistics to their Bayesian counterparts as well as the difference between time series data made for forecasting compared to traditional cross-sectional data. From there, it will compare the classical Exponential Smoothing and ARIMA class models of time series to Bayesian models with autoregressive components. Comparing the results of these models across the same data set allows the audience to see the potential benefits and disadvantages of using each of the techniques.
This talk aims to allow people to update their own skill set in forecasting with these potentially Bayesian techniques.
At the end, the talk explores the technique of model ensembling in a time series context. From these ensembles, the benefits of all types of models are potentially blended together. These models and their respective outputs will be displayed in R.
** Register and attend here:
(Free online tech event, you can join from anywhere. after register at this link, you will receive the join link. also receive recordings if you miss the live sessions. thanks)
This three hour Apache Kafka workshop will explore three advanced topics relating to the distributed event streaming platform. The breakdown for the workshop will be as follows:
For the first hour, the workshop will begin with comparison between Legacy Monolithic Architecture with Microservice Architecture. For Kafka Solutions, it will cover Event Driven Architecture and Kafka fundamental concepts including Partitions and Streams.
The second hour of the workshop will cover Kafka Producers, Consumers, Connectors, and Schema Registry. We will provide example code for a Producer, a sample Consumer, 3rd party connector, and examples of schema type Avro/Json/other formats.
For the last hour, we will discuss Kafka Cluster Deployment. This discussion will include deploying a broker, Zookeeper, Schema Registry, Producer and Consumer Config settings, enabling SSL, setting ACL for each topic, enabling firewall, and providing examples for each. We will also cover Kafka monitoring including Metricbeat or Prometheus with Alertmanager and JMX_Exporter.
More AI/ML/Data Tech events up coming (join from anywhere with zoom):
* May 17th, The Bayesians are Coming to Time Series
* May 19th, Interactive SQL on the Lakehouse: Making BI work without Data Warehouses and Extracts
* May 20, NLP Advances for African Languages
*May 28th, Graph Analytics and Graph-based Machine Learning
*Jun 1st, Full Page Handwriting Recognition via Image to Sequence Extraction
* Jun 22~24, Ray Summit - Scalable ML and Python (free, virtual conference)
Details and registration: https://www.aicamp.ai/event/events
** Register and attend: https://www.anyscale.com/ray-summit-2021?utm_source=aicamp&utm_medium=email&utm_campaign=raysummit
(rsvp on meetup was turned off)
This is a three-days, free virtual interactive event. Ray Summit brings together developers, AI&ML engineers, data scientists & architects to build scalable AI and machine learning systems. Topics include:
* Top AI and Machine Learning trends.
* ML in production & MLOps.
* Deep Learning & Reinforcement learning.
* End-to-End AutoML, Distributed XGBoost and Massive-scale ML.
* Building highly available & scalable application in the cloud
* Petabyte-Scale Data Lake
* Cloud computing, serverless & more.
* Eric Brewer, Google Fellow and Professor Emeritus, UC Berkeley.
* Marvin Theimer, Distinguished Engineer, Amazon Web Services.
* Matt Johnson, Research Scientist, Google Brain.
* Dawn Song, Professor EECS, UC Berkeley.
* Sarah Bird, Principal Program Manager, Azure AI Microsoft.
* Sahika Genc, Principal Applied Scientist, Amazon AI.
* Michael Mui, Senior Software Engineer, Uber ML.
* Raghu Ganti, Principal Research Staff Member, IBM.
* Wei Chen, Deep Learning Software Engineer, NVIDIA.
* Edi Palencia, Principal AI Engineer, Microsoft.
* and 50 more
*** Details and RSVP: https://www.anyscale.com/ray-summit-2021?utm_source=aicamp&utm_medium=email&utm_campaign=raysummit