Ludi Rehaks' meetup on 03.17.16
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
H2O Rains with Databricks Cloud - Parisoma SFSri Ambati
Michal Malohlava's meetup on H2O Rains with Databricks Cloud at Parisoma SF, 02.02.16
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
AWS Lambda and Serverless framework: lessons learned while building a serverl...Luciano Mammino
Planet9energy.com is a new electricity company that is building a sophisticated analytics and energy trading platform for the UK market. Since the earliest days of the company we took the unconventional decision to go serverless and finally we are building the product on top of AWS Lambda and the Serverless framework using Node.js. In this talk we will discuss why we took this radical decision, what are the pros and cons of this approach and what are the main issues we faced as a tech team in our design and development experience. We will discuss how normal things like testing and deployment need to be re-thought to work on a serverless fashion but also the benefits of (almost) infinite auto-scalability and the piece of mind of not having to manage hundreds of servers. Finally we will underline how Node.js seems to fit naturally in this scenario and how it makes developing serverless applications extremely convenient.
Thanks to Padraig O'Brien and Luciano Mammino for speaking this month.
Speakers Bio:
Padraig O'Brien
Podge @Podgeypoos79 is a software engineer for over 15 years, most of that was spent developing in .NET and SQL Server, designing and building large scale data intensive applications. Lately he has shifted towards open source technologies and is spending most of his time learning Node.js, Scala and cool data tech like Spark, Cassandra. He is also working on a “super-secret” project called UnicornDB, don’t tell anybody!
In his spare time he helps out with organising some meetups like NodeSchool Dublin, NodeSchool Dun Laoghaire and teaching Kanban via Agile Lean Ireland.
Luciano Mammino
Luciano @loige is a Software Engineer born in 1987, the same year that the Nintendo released “Super Mario Bros” in Europe, which, “by chance” is his favourite game! His primary passion is code and he is extremely fascinated by the web, smart apps and everything that's creative like music, art and design. He started coding at the age of 12 using his father's old i386 provided only with DOS and the qBasic interpreter.He is a senior software developer at Planet9Energy in Dublin and he loves JavaScript (React/Node.js). He is also the co-author of "Node.js design patterns" 2nd edition (Packt, http://amzn.to/1ZF279B).
Hosted by Intercom, sponsored by Nearform and organised by Node.js Dublin (https://www.meetup.com/Dublin-Node-js-Meetup/events/236870576/)
Arno Candel, Chief Architect, H2O.ai talks about what's new in H2O including all the new advancements in the algorithms.
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Michal Malohlava's presentation on Building Your Own Recommendation Engine 03.17.16
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Building a Real Time Dashboard with Amazon Kinesis, Amazon Lambda and Amazon ...Amazon Web Services
Organisations today need a way to manage the ever-increasing volume of data from numerous sources such as log systems, click streams or connected devices and be able to analyse this data in real-time. In this session we will walk through an architecture demonstration of how to leverage AWS services to meet these needs.
Speaker: Ganesh Raja, Solutions Architect, Amazon Web Services
The “Twelve-Factor” application model has come to represent twelve best practices for building modern, cloud-native applications. With guidance on things like configuration, deployment, runtime, and multiple service communication, the Twelve-Factor model prescribes best practices that apply to everything from web applications to APIs to data processing applications. Although serverless computing and AWS Lambda have changed how application development is done, the “Twelve-Factor” best practices remain relevant and applicable in a serverless world. In this talk, we’ll apply the “Twelve-Factor” model to serverless application development with AWS Lambda and Amazon API Gateway and show you how these services enable you to build scalable, low cost, and low administration applications.
A Tale of Three Deep Learning Frameworks: TensorFlow, Keras, & Deep Learning ...Databricks
We all know what they say – the bigger the data, the better. But when the data gets really big, how do you use it? This talk will cover three of the most popular deep learning frameworks: TensorFlow, Keras, and Deep Learning Pipelines, and when, where, and how to use them.
We’ll also discuss their integration with distributed computing engines such as Apache Spark (which can handle massive amounts of data), as well as help you answer questions such as:
– As a developer how do I pick the right deep learning framework for me?
– Do I want to develop my own model or should I employ an existing one
– How do I strike a trade-off between productivity and control through low-level APIs?
In this session, we will show you how easy it is to build an image classifier with Tensorflow, Keras, and Deep Learning Pipelines in under 30 minutes. After this session, you will walk away with the confidence to evaluate which framework is best for you, and perhaps with a better sense for how to fool an image classifier!
H2O Rains with Databricks Cloud - Parisoma SFSri Ambati
Michal Malohlava's meetup on H2O Rains with Databricks Cloud at Parisoma SF, 02.02.16
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
AWS Lambda and Serverless framework: lessons learned while building a serverl...Luciano Mammino
Planet9energy.com is a new electricity company that is building a sophisticated analytics and energy trading platform for the UK market. Since the earliest days of the company we took the unconventional decision to go serverless and finally we are building the product on top of AWS Lambda and the Serverless framework using Node.js. In this talk we will discuss why we took this radical decision, what are the pros and cons of this approach and what are the main issues we faced as a tech team in our design and development experience. We will discuss how normal things like testing and deployment need to be re-thought to work on a serverless fashion but also the benefits of (almost) infinite auto-scalability and the piece of mind of not having to manage hundreds of servers. Finally we will underline how Node.js seems to fit naturally in this scenario and how it makes developing serverless applications extremely convenient.
Thanks to Padraig O'Brien and Luciano Mammino for speaking this month.
Speakers Bio:
Padraig O'Brien
Podge @Podgeypoos79 is a software engineer for over 15 years, most of that was spent developing in .NET and SQL Server, designing and building large scale data intensive applications. Lately he has shifted towards open source technologies and is spending most of his time learning Node.js, Scala and cool data tech like Spark, Cassandra. He is also working on a “super-secret” project called UnicornDB, don’t tell anybody!
In his spare time he helps out with organising some meetups like NodeSchool Dublin, NodeSchool Dun Laoghaire and teaching Kanban via Agile Lean Ireland.
Luciano Mammino
Luciano @loige is a Software Engineer born in 1987, the same year that the Nintendo released “Super Mario Bros” in Europe, which, “by chance” is his favourite game! His primary passion is code and he is extremely fascinated by the web, smart apps and everything that's creative like music, art and design. He started coding at the age of 12 using his father's old i386 provided only with DOS and the qBasic interpreter.He is a senior software developer at Planet9Energy in Dublin and he loves JavaScript (React/Node.js). He is also the co-author of "Node.js design patterns" 2nd edition (Packt, http://amzn.to/1ZF279B).
Hosted by Intercom, sponsored by Nearform and organised by Node.js Dublin (https://www.meetup.com/Dublin-Node-js-Meetup/events/236870576/)
Arno Candel, Chief Architect, H2O.ai talks about what's new in H2O including all the new advancements in the algorithms.
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Michal Malohlava's presentation on Building Your Own Recommendation Engine 03.17.16
- Powered by the open source machine learning software H2O.ai. Contributors welcome at: https://github.com/h2oai
- To view videos on H2O open source machine learning software, go to: https://www.youtube.com/user/0xdata
Building a Real Time Dashboard with Amazon Kinesis, Amazon Lambda and Amazon ...Amazon Web Services
Organisations today need a way to manage the ever-increasing volume of data from numerous sources such as log systems, click streams or connected devices and be able to analyse this data in real-time. In this session we will walk through an architecture demonstration of how to leverage AWS services to meet these needs.
Speaker: Ganesh Raja, Solutions Architect, Amazon Web Services
The “Twelve-Factor” application model has come to represent twelve best practices for building modern, cloud-native applications. With guidance on things like configuration, deployment, runtime, and multiple service communication, the Twelve-Factor model prescribes best practices that apply to everything from web applications to APIs to data processing applications. Although serverless computing and AWS Lambda have changed how application development is done, the “Twelve-Factor” best practices remain relevant and applicable in a serverless world. In this talk, we’ll apply the “Twelve-Factor” model to serverless application development with AWS Lambda and Amazon API Gateway and show you how these services enable you to build scalable, low cost, and low administration applications.
A Tale of Three Deep Learning Frameworks: TensorFlow, Keras, & Deep Learning ...Databricks
We all know what they say – the bigger the data, the better. But when the data gets really big, how do you use it? This talk will cover three of the most popular deep learning frameworks: TensorFlow, Keras, and Deep Learning Pipelines, and when, where, and how to use them.
We’ll also discuss their integration with distributed computing engines such as Apache Spark (which can handle massive amounts of data), as well as help you answer questions such as:
– As a developer how do I pick the right deep learning framework for me?
– Do I want to develop my own model or should I employ an existing one
– How do I strike a trade-off between productivity and control through low-level APIs?
In this session, we will show you how easy it is to build an image classifier with Tensorflow, Keras, and Deep Learning Pipelines in under 30 minutes. After this session, you will walk away with the confidence to evaluate which framework is best for you, and perhaps with a better sense for how to fool an image classifier!
High Throughput Genomics on AWS - using containers and serverless technology for science. Talk delivered by AWS's lead genomics and life sciences expert from the Research and Technical Computing team.
Scala eXchange: Building robust data pipelines in ScalaAlexander Dean
Over the past couple of years, Scala has become a go-to language for building data processing applications, as evidenced by the emerging ecosystem of frameworks and tools including LinkedIn's Kafka, Twitter's Scalding and our own Snowplow project (https://github.com/snowplow/snowplow).
In this talk, Alex will draw on his experiences at Snowplow to explore how to build rock-sold data pipelines in Scala, highlighting a range of techniques including:
* Translating the Unix stdin/out/err pattern to stream processing
* "Railway oriented" programming using the Scalaz Validation
* Validating data structures with JSON Schema
* Visualizing event stream processing errors in ElasticSearch
Alex's talk draws on his experiences working with event streams in Scala over the last two and a half years at Snowplow, and by Alex's recent work penning Unified Log Processing, a Manning book.
With AWS you can choose the right database technology and software for the job. Given the myriad of choices, from relational databases to non-relational stores, this session provides details and examples of some of the choices available to you. This session also provides details about real-world deployments from customers using Amazon RDS, Amazon ElastiCache, Amazon DynamoDB, and Amazon Redshift.
AWS re:Invent 2016: Tableau Rules of Engagement in the Cloud (STG306)Amazon Web Services
You have billions of events in your fact table, all of it waiting to be visualized. Enter Tableau… but wait: how can you ensure scalability and speed with your data in Amazon S3, Spark, Amazon Redshift, or Presto? In this talk, you’ll hear how Albert Wong and Srikanth Devidi at Netflix use Tableau on top of their big data stack. Albert and Srikanth also show how you can get the most out of a massive dataset using Tableau, and help guide you through the problems you may encounter along the way. Session sponsored by Tableau.
AWS Competency Partner
How Disney+ uses fast data ubiquity to improve the customer experience Martin Zapletal
Disney+ uses Amazon Kinesis to drive real-time actions like providing title recommendations for customers, sending events across microservices, and delivering logs for operational analytics to improve the customer experience. In this session, you learn how Disney+ built real-time data-driven capabilities on a unified streaming platform. This platform ingests billions of events per hour in Amazon Kinesis Data Streams, processes and analyzes that data in Amazon Kinesis Data Analytics for Apache Flink, and uses Amazon Kinesis Data Firehose to deliver data to destinations without servers or code. Hear how these services helped Disney+ scale its viewing experience to tens of millions of customers with the required quality and reliability.
Learn more about re:Invent 2020 at http://bit.ly/3c4NSdY
Real-time Data Processing with Amazon DynamoDB Streams and AWS LambdaAmazon Web Services
DynamoDB Streams is a feature of DynamoDB that allows you to access a stream of all changes made to your DynamoDB tables in the last rolling 24 hours. You can use AWS Lambda to process event data generated from a DynamoDB Stream.
In this webinar, we will cover key Amazon DynamoDB Streams and AWS Lambda features, walk through sample use cases for real-time data processing, and discuss best practices on using the services together. We'll then demonstrate setting up Amazon DynamoDB Streams and an associated Lambda function to capture and perform custom computations on database table updates, all without setting up any infrastructure
Learning Objectives:
· Understand key Amazon DynamoDB Streams and AWS Lambda features
· Learn how to set up a real-time data modification framework using Amazon DynamoDB Streams AWS Lambda
· Learn sample use cases, best practices and tips on using AWS Lambda with Amazon DynamoDB Streams
Streaming analytics on Google Cloud Platform, by Javier Ramirez, teowakijavier ramirez
Do you think you can write a system to get data from sensors across the world, do real time analytics, and display the data on a dashboard in under 100 lines of code? Would you like to add some monitoring and autoscaling too? And what about serverless? In this talk I'll show you all the technologies GCP offers to build such a system reliably and at scale.
AWS re:Invent 2016: Streaming ETL for RDS and DynamoDB (DAT315)Amazon Web Services
During this session Greg Brandt and Liyin Tang, Data Infrastructure engineers from Airbnb, will discuss the design and architecture of Airbnb's streaming ETL infrastructure, which exports data from RDS for MySQL and DynamoDB into Airbnb's data warehouse, using a system called SpinalTap. We will also discuss how we leverage Spark Streaming to compute derived data from tracking topics and/or database tables, and HBase to provide immediate data access and generate cleanly time-partitioned Hive tables.
In this session, storage experts will walk you through the object storage offering, Amazon S3, a bulk data repository that can deliver 99.999999999% durability and scale past trillions of objects worldwide. Learn about the different ways you can accelerate data transfer to S3 and get a close look at some of the new tools available for you to secure and manage your data more efficiently. Announced at re:Invent 2016, see how you can use Amazon Athena with S3 to run serverless analytics on your data and as a bonus, walk away with some code snippets to use with S3. Hear AWS customers talk about the solutions they have built with S3 to turn their data into a strategic asset, instead of just a cost center. And bring your toughest questions to our experts on hand and walk away that much smarter on how to use object storage from AWS.
Presented at ServerlessConf NYC 2016.
What happens when you give 6k developers access to the cloud? Introducing Cloud Custodian, an opensource project from Capital One, which provides a DSL for AWS management that operates in real-time using cloud watch events and lambda. We use it for the gamut of compliance/encryption/cost controls. What can it do for you?
Taking the Performance of your Data Warehouse to the Next Level with Amazon R...Amazon Web Services
Amazon Redshift gives you fast SQL query performance on large data sets. We will discuss optimisation from end to end, all the way from loading through to querying to ensure your end users get the data they need, when they need it.
Speaker: Russell Nash, Solutions Architect, Amazon Web Services
Featured Customer - Domain
Deep Learning Streaming Platform with Kafka Streams, TensorFlow, DeepLearning...Kai Wähner
Talk from JavaOne 2017: Apache Kafka + Kafka Streams for Scalable, Mission Critical Deep Learning.
Intelligent real time applications are a game changer in any industry. Deep Learning is one of the hottest buzzwords in this area. New technologies like GPUs combined with elastic cloud infrastructure enable the sophisticated usage of artificial neural networks to add business value in real world scenarios. Tech giants use it e.g. for image recognition and speech translation. This session discusses some real-world scenarios from different industries to explain when and how traditional companies can leverage deep learning in real time applications.
This session shows how to deploy Deep Learning models into real time applications to do predictions on new events. Apache Kafka will be used to execute analytic models in a highly scalable and performant way.
The first part introduces the use cases and concepts behind Deep Learning. It discusses how to build Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN) and Autoencoders leveraging open source frameworks like TensorFlow, DeepLearning4J or H2O.
The second part shows how to deploy the built analytic models to real time applications leveraging Apache Kafka as streaming platform and Apache Kafka’s Streams API to embed the intelligent business logic into any external application or microservice.
Some further material around Apache Kafka and Machine Learning:
- Blog Post: How to Build and Deploy Scalable Machine Learning in Production with Apache Kafka: https://www.confluent.io/blog/build-deploy-scalable-machine-learning-production-apache-kafka/
- Video: Build and Deploy Analytic Models with H2O.ai and Apache Kafka: https://www.youtube.com/watch?v=-q7CyIExBKM&feature=youtu.be
- Code: Github Examples using Apache Kafka, TensorFlow, H2O, DeepLearning4J: https://github.com/kaiwaehner/kafka-streams-machine-learning-examples
"The only real mistake is the one from which we learn nothing.” So how do we learn from system failures? This session will move beyond “blameless” postmortems and show how to use data to avoid and mitigate future failures. We will share the best practices for gathering systems-related data and people-related data. You will then learn how to apply the data to formulate actionable response plans and avoid repeating failures.
This session is brought to you by AWS Summit New York City sponsor, Datadog."
Apache Kafka Streams + Machine Learning / Deep LearningKai Wähner
Machine Learning and Deep Learning Applied to Real Time with Apache Kafka Streams...
Big Data and Machine Learning are key for innovation in many industries today. Large amounts of historical data are stored and analyzed in Hadoop, Spark or other clusters to find patterns and insights, e.g. for predictive maintenance, fraud detection or cross-selling.
This first part of the session explains how to build analytic models with R, Python and Scala leveraging open source machine learning / deep learning frameworks like Apache Spark, TensorFlow or H2O.ai. The second part discusses how to leverage these built analytic models in your own streaming applications or microservices; leveraging the Apache Kafka cluster and Kafka Streams instead of building an own stream processing cluster. The session focuses on live demos and teaches lessons learned for executing analytic models in a highly scalable and performant way.
The last part explains how Apache Kafka can help to move from a manual build and deployment of analytic models to continuous online model improvement in real time.
Join us for a for a Amazon Kinesis tutorial webinar. In this session we will provide a reference architecture and instructions for building a system that performs real-time sliding-windows analysis over streaming clickstream data. We will use Amazon Kinesis for managed ingestion of streaming data at scale with the ability to replay past data, and run sliding-window computation using Apache Storm. We’ll demonstrate in the webinar on how to build the system and deploy on AWS and walkthrough all the steps from ingestion, processing, and storing to visualizing of the data in real-time.
AWS October Webinar Series - AWS Lambda Best Practices: Python, Scheduled Job...Amazon Web Services
AWS Lambda lets you run code without provisioning or managing servers. We have introduced a few new features this year at re:Invent and would like to share with you some of the best practices.
This webinar will introduce you to scheduled AWS Lambda functions and how to use long running functions to handle large volume data ingestion and processing jobs. We will demonstrate how to use versioning to control which Lambda function version is being executed in your development, testing, and production environments. We will also show you how to run your Python code in AWS Lambda.
With AWS Lambda, you can easily build scalable microservices for mobile, web, and IoT applications or respond to events from other AWS services without managing infrastructure. In this session, you’ll see demonstrations and hear more about newly launched features. We’ll show you how to use Lambda to build web, mobile, or IoT backends and voice-enabled apps, and we'll show you how to extend both AWS and third party services by triggering Lambda functions. We’ll also provide productivity and performance tips for getting the most out of your Lambda functions and show how cloud native architectures use Lambda to eliminate “cold servers” and excess capacity without sacrificing scalability or responsiveness.
High Throughput Genomics on AWS - using containers and serverless technology for science. Talk delivered by AWS's lead genomics and life sciences expert from the Research and Technical Computing team.
Scala eXchange: Building robust data pipelines in ScalaAlexander Dean
Over the past couple of years, Scala has become a go-to language for building data processing applications, as evidenced by the emerging ecosystem of frameworks and tools including LinkedIn's Kafka, Twitter's Scalding and our own Snowplow project (https://github.com/snowplow/snowplow).
In this talk, Alex will draw on his experiences at Snowplow to explore how to build rock-sold data pipelines in Scala, highlighting a range of techniques including:
* Translating the Unix stdin/out/err pattern to stream processing
* "Railway oriented" programming using the Scalaz Validation
* Validating data structures with JSON Schema
* Visualizing event stream processing errors in ElasticSearch
Alex's talk draws on his experiences working with event streams in Scala over the last two and a half years at Snowplow, and by Alex's recent work penning Unified Log Processing, a Manning book.
With AWS you can choose the right database technology and software for the job. Given the myriad of choices, from relational databases to non-relational stores, this session provides details and examples of some of the choices available to you. This session also provides details about real-world deployments from customers using Amazon RDS, Amazon ElastiCache, Amazon DynamoDB, and Amazon Redshift.
AWS re:Invent 2016: Tableau Rules of Engagement in the Cloud (STG306)Amazon Web Services
You have billions of events in your fact table, all of it waiting to be visualized. Enter Tableau… but wait: how can you ensure scalability and speed with your data in Amazon S3, Spark, Amazon Redshift, or Presto? In this talk, you’ll hear how Albert Wong and Srikanth Devidi at Netflix use Tableau on top of their big data stack. Albert and Srikanth also show how you can get the most out of a massive dataset using Tableau, and help guide you through the problems you may encounter along the way. Session sponsored by Tableau.
AWS Competency Partner
How Disney+ uses fast data ubiquity to improve the customer experience Martin Zapletal
Disney+ uses Amazon Kinesis to drive real-time actions like providing title recommendations for customers, sending events across microservices, and delivering logs for operational analytics to improve the customer experience. In this session, you learn how Disney+ built real-time data-driven capabilities on a unified streaming platform. This platform ingests billions of events per hour in Amazon Kinesis Data Streams, processes and analyzes that data in Amazon Kinesis Data Analytics for Apache Flink, and uses Amazon Kinesis Data Firehose to deliver data to destinations without servers or code. Hear how these services helped Disney+ scale its viewing experience to tens of millions of customers with the required quality and reliability.
Learn more about re:Invent 2020 at http://bit.ly/3c4NSdY
Real-time Data Processing with Amazon DynamoDB Streams and AWS LambdaAmazon Web Services
DynamoDB Streams is a feature of DynamoDB that allows you to access a stream of all changes made to your DynamoDB tables in the last rolling 24 hours. You can use AWS Lambda to process event data generated from a DynamoDB Stream.
In this webinar, we will cover key Amazon DynamoDB Streams and AWS Lambda features, walk through sample use cases for real-time data processing, and discuss best practices on using the services together. We'll then demonstrate setting up Amazon DynamoDB Streams and an associated Lambda function to capture and perform custom computations on database table updates, all without setting up any infrastructure
Learning Objectives:
· Understand key Amazon DynamoDB Streams and AWS Lambda features
· Learn how to set up a real-time data modification framework using Amazon DynamoDB Streams AWS Lambda
· Learn sample use cases, best practices and tips on using AWS Lambda with Amazon DynamoDB Streams
Streaming analytics on Google Cloud Platform, by Javier Ramirez, teowakijavier ramirez
Do you think you can write a system to get data from sensors across the world, do real time analytics, and display the data on a dashboard in under 100 lines of code? Would you like to add some monitoring and autoscaling too? And what about serverless? In this talk I'll show you all the technologies GCP offers to build such a system reliably and at scale.
AWS re:Invent 2016: Streaming ETL for RDS and DynamoDB (DAT315)Amazon Web Services
During this session Greg Brandt and Liyin Tang, Data Infrastructure engineers from Airbnb, will discuss the design and architecture of Airbnb's streaming ETL infrastructure, which exports data from RDS for MySQL and DynamoDB into Airbnb's data warehouse, using a system called SpinalTap. We will also discuss how we leverage Spark Streaming to compute derived data from tracking topics and/or database tables, and HBase to provide immediate data access and generate cleanly time-partitioned Hive tables.
In this session, storage experts will walk you through the object storage offering, Amazon S3, a bulk data repository that can deliver 99.999999999% durability and scale past trillions of objects worldwide. Learn about the different ways you can accelerate data transfer to S3 and get a close look at some of the new tools available for you to secure and manage your data more efficiently. Announced at re:Invent 2016, see how you can use Amazon Athena with S3 to run serverless analytics on your data and as a bonus, walk away with some code snippets to use with S3. Hear AWS customers talk about the solutions they have built with S3 to turn their data into a strategic asset, instead of just a cost center. And bring your toughest questions to our experts on hand and walk away that much smarter on how to use object storage from AWS.
Presented at ServerlessConf NYC 2016.
What happens when you give 6k developers access to the cloud? Introducing Cloud Custodian, an opensource project from Capital One, which provides a DSL for AWS management that operates in real-time using cloud watch events and lambda. We use it for the gamut of compliance/encryption/cost controls. What can it do for you?
Taking the Performance of your Data Warehouse to the Next Level with Amazon R...Amazon Web Services
Amazon Redshift gives you fast SQL query performance on large data sets. We will discuss optimisation from end to end, all the way from loading through to querying to ensure your end users get the data they need, when they need it.
Speaker: Russell Nash, Solutions Architect, Amazon Web Services
Featured Customer - Domain
Deep Learning Streaming Platform with Kafka Streams, TensorFlow, DeepLearning...Kai Wähner
Talk from JavaOne 2017: Apache Kafka + Kafka Streams for Scalable, Mission Critical Deep Learning.
Intelligent real time applications are a game changer in any industry. Deep Learning is one of the hottest buzzwords in this area. New technologies like GPUs combined with elastic cloud infrastructure enable the sophisticated usage of artificial neural networks to add business value in real world scenarios. Tech giants use it e.g. for image recognition and speech translation. This session discusses some real-world scenarios from different industries to explain when and how traditional companies can leverage deep learning in real time applications.
This session shows how to deploy Deep Learning models into real time applications to do predictions on new events. Apache Kafka will be used to execute analytic models in a highly scalable and performant way.
The first part introduces the use cases and concepts behind Deep Learning. It discusses how to build Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN) and Autoencoders leveraging open source frameworks like TensorFlow, DeepLearning4J or H2O.
The second part shows how to deploy the built analytic models to real time applications leveraging Apache Kafka as streaming platform and Apache Kafka’s Streams API to embed the intelligent business logic into any external application or microservice.
Some further material around Apache Kafka and Machine Learning:
- Blog Post: How to Build and Deploy Scalable Machine Learning in Production with Apache Kafka: https://www.confluent.io/blog/build-deploy-scalable-machine-learning-production-apache-kafka/
- Video: Build and Deploy Analytic Models with H2O.ai and Apache Kafka: https://www.youtube.com/watch?v=-q7CyIExBKM&feature=youtu.be
- Code: Github Examples using Apache Kafka, TensorFlow, H2O, DeepLearning4J: https://github.com/kaiwaehner/kafka-streams-machine-learning-examples
"The only real mistake is the one from which we learn nothing.” So how do we learn from system failures? This session will move beyond “blameless” postmortems and show how to use data to avoid and mitigate future failures. We will share the best practices for gathering systems-related data and people-related data. You will then learn how to apply the data to formulate actionable response plans and avoid repeating failures.
This session is brought to you by AWS Summit New York City sponsor, Datadog."
Apache Kafka Streams + Machine Learning / Deep LearningKai Wähner
Machine Learning and Deep Learning Applied to Real Time with Apache Kafka Streams...
Big Data and Machine Learning are key for innovation in many industries today. Large amounts of historical data are stored and analyzed in Hadoop, Spark or other clusters to find patterns and insights, e.g. for predictive maintenance, fraud detection or cross-selling.
This first part of the session explains how to build analytic models with R, Python and Scala leveraging open source machine learning / deep learning frameworks like Apache Spark, TensorFlow or H2O.ai. The second part discusses how to leverage these built analytic models in your own streaming applications or microservices; leveraging the Apache Kafka cluster and Kafka Streams instead of building an own stream processing cluster. The session focuses on live demos and teaches lessons learned for executing analytic models in a highly scalable and performant way.
The last part explains how Apache Kafka can help to move from a manual build and deployment of analytic models to continuous online model improvement in real time.
Join us for a for a Amazon Kinesis tutorial webinar. In this session we will provide a reference architecture and instructions for building a system that performs real-time sliding-windows analysis over streaming clickstream data. We will use Amazon Kinesis for managed ingestion of streaming data at scale with the ability to replay past data, and run sliding-window computation using Apache Storm. We’ll demonstrate in the webinar on how to build the system and deploy on AWS and walkthrough all the steps from ingestion, processing, and storing to visualizing of the data in real-time.
AWS October Webinar Series - AWS Lambda Best Practices: Python, Scheduled Job...Amazon Web Services
AWS Lambda lets you run code without provisioning or managing servers. We have introduced a few new features this year at re:Invent and would like to share with you some of the best practices.
This webinar will introduce you to scheduled AWS Lambda functions and how to use long running functions to handle large volume data ingestion and processing jobs. We will demonstrate how to use versioning to control which Lambda function version is being executed in your development, testing, and production environments. We will also show you how to run your Python code in AWS Lambda.
With AWS Lambda, you can easily build scalable microservices for mobile, web, and IoT applications or respond to events from other AWS services without managing infrastructure. In this session, you’ll see demonstrations and hear more about newly launched features. We’ll show you how to use Lambda to build web, mobile, or IoT backends and voice-enabled apps, and we'll show you how to extend both AWS and third party services by triggering Lambda functions. We’ll also provide productivity and performance tips for getting the most out of your Lambda functions and show how cloud native architectures use Lambda to eliminate “cold servers” and excess capacity without sacrificing scalability or responsiveness.
Building A Serverless Web Application With AWS Lambda: A Step-By-Step GuideSynergyTop Inc.
Building a serverless web application with AWS Lambda is an exciting endeavor that brings unprecedented scalability and cost-efficiency to the world of web development. With AWS Lambda, developers can create applications without the need to manage servers or worry about infrastructure. For more details, visit: www.synergytop.com/blog/building-a-serverless-web-application-with-aws-lambda-a-step-by-step-guide
AWS Lambda is a compute service that runs your code in response to events and automatically manages the compute resources for you, making it easy to build applications that respond quickly to new information. AWS Lambda starts running your code within milliseconds of an event such as an image upload, in-app activity, website click, or output from a connected device.
An introduction to serverless architectures (February 2017)Julien SIMON
An introduction to serverless
AWS Lambda
Amazon API Gateway
Demo: writing your first Lambda function
Demo: building a serverless pipeline
Additional resources
In this session, you'll learn what’s new and hot with AWS Lambda. Come on a tour with Dr. Tim Wagner, General Manager of AWS Lambda, to learn what we’ve been working on and what we are planning for the future. You'll get a hands-on demonstration of some our newest features which will provide you with a launching pad for some of the later sessions in the day.
Compute Without Servers – Building Applications with AWS LambdaAmazon Web Services
AWS Lambda enables developers to build scalable applications without managing servers. Come learn how AWS Lambda’s event driven approach helps build backend ingestion systems, real time stream processing, and scalable API backends. We will deep dive and provide live demo into the different approaches that customers have taken to building applications with AWS Lambda, the typical architectures that customers use and best practices for authoring, deploying, and managing the functions.
Markku Lepisto, Principal Technology Evangelist, Amazon Web Services, APAC
February 2016 Webinar Series - Introducing VPC Support for AWS LambdaAmazon Web Services
You can now access resources within a Virtual Private Cloud (VPC) using AWS Lambda.
In this webinar, we will show how you can enable your AWS Lambda functions to access resources in a VPC. We will walk through the configuration details on how to set up this functionality, and we will demonstrate two sample scenarios. We will also discuss best practices of how to use AWS Lambda in a VPC and sample application designs.
Learning Objectives:
Learn how to access resources in a VPC with AWS Lambda
Who Should Attend:
Developers
Introduction to AWS lambda & Serverless Application1.pptxMohammed Shefeeq
AWS lambda is a serverless computing platform that allows developers to create and deploy applications without provisioning or managing servers.
It was released in November 2014. The service runs code in response to events, such as API calls or page views, and automatically manages the compute resources required by that code, scaling up or down in response to demand.
In this PPT we will cover the basics of AWS lambda and how it can be used for building serverless applications.
We will also take a look at the tools needed for creating and testing AWS lambda-based applications.
AWS SAM is a tool for developing serverless applications on AWS. It helps you create and deploy functions that are triggered by events such as HTTP requests, Amazon S3 bucket events, DynamoDB table events, or other AWS services.
We will also cover Building and deploying a hello world using SAM in this section
Build and run applications without thinking about serversAmazon Web Services
Organizations need to gain insight and knowledge from a growing number of Internet of Things (IoT) APIs clickstreams comprised of unstructured and log data sources. However, organizations are often limited by legacy data warehouses and ETL processes that were designed for transactional data. In this session, we’ll introduce the key ETL features of AWS Glue through use cases ranging from scheduled nightly data warehouse loads to near real-time, event-driven ETL flows for your data lake. We’ll also discuss how to build scalable, efficient and serverless ETL pipelines using AWS Glue.
AWS Lambda and Amazon API Gateway have changed how developers build and run their applications or services. But what are the best practices for tasks such as deployment, monitoring, and debugging in a serverless world? In this session, we’ll dive into best practices that serverless developers can use for application lifecycle management, CI/CD, monitoring, and diagnostics. We’ll talk about how you can build CI/CD pipelines that automatically build, test, and deploy your serverless applications using AWS CodePipeline, AWS CodeBuild, and AWS CloudFormation. We’ll also cover the built-in capabilities of Lambda and API Gateway for creating multiple versions, stages, and environments of your functions and APIs. Finally, we’ll cover monitoring and diagnostics of your Lambda functions with Amazon CloudWatch and AWS X-Ray.
With AWS Lambda, you can easily build scalable microservices for mobile, web, and IoT applications or respond to events from other AWS services without managing infrastructure. In this session, you’ll see demonstrations and hear more about newly launched features. We’ll show you how to use Lambda to build web, mobile, or IoT backends and voice-enabled apps, and we'll show you how to extend both AWS and third party services by triggering Lambda functions. We’ll also provide productivity and performance tips for getting the most out of your Lambda functions and show how cloud native architectures use Lambda to eliminate “cold servers” and excess capacity without sacrificing scalability or responsiveness.
Serverless DevOps to the Rescue - SRV330 - re:Invent 2017Amazon Web Services
Join this workshop for a crash course in serverless DevOps! This workshops presents a scenario in which you help out Wild Rydes (www.wildrydes.com), the world’s leading unicorn transportation startup! After building the first iteration of its serverless web application, Wild Rydes needs serverless DevOps experts like yourself to help it rapidly build and iterate upon its web app. In this workshop, you’ll help Wild Rydes set up a CI/CD pipeline that enables the company to rapidly build, test, and deploy changes to its serverless application. You’ll also learn to monitor and diagnose issues for its application. This workshop will teach you how to model and deploy serverless apps with the AWS Serverless Application Model. You’ll learn to use AWS CodePipeline and AWS CodeBuild to create a CI/CD pipeline for AWS Lambda and other services. Finally, you’ll learn to use AWS X-Ray to diagnose issues in your Lambda functions.
Requirements: Laptop, AWS account, basic Git experience. Recommended: Previous experience with the AWS Management Console and AWS CloudFormation templates, some familiarity with the AWS Developer Tools services, and preferably one of the AWS Associate certifications.
With AWS Lambda, you can easily build scalable microservices for mobile, web, and IoT applications or respond to events from other AWS services without managing infrastructure. In this session, you’ll see demonstrations and hear more about newly launched features. We’ll show you how to use Lambda to build web, mobile, or IoT backends and voice-enabled apps, and we'll show you how to extend both AWS and third party services by triggering Lambda functions. We’ll also provide productivity and performance tips for getting the most out of your Lambda functions and show how cloud native architectures use Lambda to eliminate “cold servers” and excess capacity without sacrificing scalability or responsiveness.
Building LLM Solutions using Open Source and Closed Source Solutions in Coher...Sri Ambati
Sandeep Singh, Head of Applied AI Computer Vision, Beans.ai
H2O Open Source GenAI World SF 2023
In the modern era of machine learning, leveraging both open-source and closed-source solutions has become paramount for achieving cutting-edge results. This talk delves into the intricacies of seamlessly integrating open-source Large Language Model (LLM) solutions like Vicuna, Falcon, and Llama with industry giants such as ChatGPT and Google's Palm. As the demand for fine-tuned and specialized datasets grows, it is imperative to understand the synergy between these tools. Attendees will gain insights into best practices for building and enriching datasets tailored for fine-tuning tasks, ensuring that their LLM projects are both robust and efficient. Through real-world examples and hands-on demonstrations, this talk will equip attendees with the knowledge to harness the power of both open and closed-source tools in a coherent and effective manner.
Patrick Hall, Professor, AI Risk Management, The George Washington University
H2O Open Source GenAI World SF 2023
Language models are incredible engineering breakthroughs but require auditing and risk management before productization. These systems raise concerns about toxicity, transparency and reproducibility, intellectual property licensing and ownership, disinformation and misinformation, supply chains, and more. How can your organization leverage these new tools without taking on undue or unknown risks? While language models and associated risk management are in their infancy, a small number of best practices in governance and risk are starting to emerge. If you have a language model use case in mind, want to understand your risks, and do something about them, this presentation is for you!
Dr. Alexy Khrabrov, Open Source Science Community Director, IBM
H2O Open Source GenAI World SF 2023
In this talk, Dr. Alexy Khrabrov, recently elected Chair of the new Generative AI Commons at Linux Foundation for AI & Data, outlines the OSS AI landscape, challenges, and opportunities. With new models and frameworks being unveiled weekly, one thing remains constant: community building and validation of all aspects of AI is key to reliable and responsible AI we can use for business and society needs. Industrial AI is one key area where such community validation can prove invaluable.
Michelle Tanco, Head of Product, H2O.ai
H2O Open Source GenAI World SF 2023
Learn how the makers at H2O.ai are building internal tools to solve real use cases using H2O Wave and h2oGPT. We will walk through an end-to-end use case and discuss how to incorporate business rules and generated content to rapidly develop custom AI apps using only Python APIs.
Applied Gen AI for the Finance Vertical Sri Ambati
Megan Kurka, Vice President, Customer Data Scientist, H2O.ai
H2O Open Source GenAI World SF 2023
Discover the transformative power of Applied Gen AI. Learn how the H2O team builds customized applications and workflows that integrate capabilities of Gen AI and AutoML specifically designed to address and enhance financial use cases. Explore real world examples, learn best practices, and witness firsthand how our innovative solutions are reshaping the landscape of finance technology.
Practitioner's Guide to LLMs: Exploring Use Cases and a Glimpse Beyond Curren...Sri Ambati
Pascal Pfeiffer, Principal Data Scientist, H2O.ai
H2O Open Source GenAI World SF 2023
This talk dives into the expansive ecosystem of Large Language Models (LLMs), offering practitioners an insightful guide to various relevant applications, from natural language understanding to creative content generation. While exploring use cases across different industries, it also honestly addresses the current limitations of LLMs and anticipates future advancements.
Introducción al Aprendizaje Automatico con H2O-3 (1)Sri Ambati
En esta reunión virtual, damos una introducción a la plataforma de aprendizaje automático de código abierto número 1, H2O-3 y te mostramos cómo puedes usarla para desarrollar modelos para resolver diferentes casos de uso.
From Rapid Prototypes to an end-to-end Model Deployment: an AI Hedge Fund Use...Sri Ambati
Numerai is an open, crowd-sourced hedge fund powered by predictions from data scientists around the world. In return, participants are rewarded with weekly payouts in crypto.
In this talk, Joe will give an overview of the Numerai tournament based on his own experience. He will then explain how he automates the time-consuming tasks such as testing different modelling strategies, scoring new datasets, submitting predictions to Numerai as well as monitoring model performance with H2O Driverless AI and R.
AI Foundations Course Module 1 - Shifting to the Next Step in Your AI Transfo...Sri Ambati
In this session, you will learn about what you should do after you’ve taken an AI transformation baseline. Over the span of this session, we will discuss the next steps in moving toward AI readiness through alignment of talent and tools to drive successful adoption and continuous use within an organization.
To find additional videos on AI courses, earn badges, join the courses at H2O.ai Learning Center: https://training.h2o.ai/products/ai-foundations-course
To find the Youtube video about this presentation: https://youtu.be/K1Cl3x3rd8g
Speaker:
Chemere Davis (H2O.ai - Senior Data Scientist Training Specialist)
Check out the webinar slides to learn more about how XfilesPro transforms Salesforce document management by leveraging its world-class applications. For more details, please connect with sales@xfilespro.com
If you want to watch the on-demand webinar, please click here: https://www.xfilespro.com/webinars/salesforce-document-management-2-0-smarter-faster-better/
Listen to the keynote address and hear about the latest developments from Rachana Ananthakrishnan and Ian Foster who review the updates to the Globus Platform and Service, and the relevance of Globus to the scientific community as an automation platform to accelerate scientific discovery.
Modern design is crucial in today's digital environment, and this is especially true for SharePoint intranets. The design of these digital hubs is critical to user engagement and productivity enhancement. They are the cornerstone of internal collaboration and interaction within enterprises.
Developing Distributed High-performance Computing Capabilities of an Open Sci...Globus
COVID-19 had an unprecedented impact on scientific collaboration. The pandemic and its broad response from the scientific community has forged new relationships among public health practitioners, mathematical modelers, and scientific computing specialists, while revealing critical gaps in exploiting advanced computing systems to support urgent decision making. Informed by our team’s work in applying high-performance computing in support of public health decision makers during the COVID-19 pandemic, we present how Globus technologies are enabling the development of an open science platform for robust epidemic analysis, with the goal of collaborative, secure, distributed, on-demand, and fast time-to-solution analyses to support public health.
Large Language Models and the End of ProgrammingMatt Welsh
Talk by Matt Welsh at Craft Conference 2024 on the impact that Large Language Models will have on the future of software development. In this talk, I discuss the ways in which LLMs will impact the software industry, from replacing human software developers with AI, to replacing conventional software with models that perform reasoning, computation, and problem-solving.
Innovating Inference - Remote Triggering of Large Language Models on HPC Clus...Globus
Large Language Models (LLMs) are currently the center of attention in the tech world, particularly for their potential to advance research. In this presentation, we'll explore a straightforward and effective method for quickly initiating inference runs on supercomputers using the vLLM tool with Globus Compute, specifically on the Polaris system at ALCF. We'll begin by briefly discussing the popularity and applications of LLMs in various fields. Following this, we will introduce the vLLM tool, and explain how it integrates with Globus Compute to efficiently manage LLM operations on Polaris. Attendees will learn the practical aspects of setting up and remotely triggering LLMs from local machines, focusing on ease of use and efficiency. This talk is ideal for researchers and practitioners looking to leverage the power of LLMs in their work, offering a clear guide to harnessing supercomputing resources for quick and effective LLM inference.
Climate Science Flows: Enabling Petabyte-Scale Climate Analysis with the Eart...Globus
The Earth System Grid Federation (ESGF) is a global network of data servers that archives and distributes the planet’s largest collection of Earth system model output for thousands of climate and environmental scientists worldwide. Many of these petabyte-scale data archives are located in proximity to large high-performance computing (HPC) or cloud computing resources, but the primary workflow for data users consists of transferring data, and applying computations on a different system. As a part of the ESGF 2.0 US project (funded by the United States Department of Energy Office of Science), we developed pre-defined data workflows, which can be run on-demand, capable of applying many data reduction and data analysis to the large ESGF data archives, transferring only the resultant analysis (ex. visualizations, smaller data files). In this talk, we will showcase a few of these workflows, highlighting how Globus Flows can be used for petabyte-scale climate analysis.
Globus Compute wth IRI Workflows - GlobusWorld 2024Globus
As part of the DOE Integrated Research Infrastructure (IRI) program, NERSC at Lawrence Berkeley National Lab and ALCF at Argonne National Lab are working closely with General Atomics on accelerating the computing requirements of the DIII-D experiment. As part of the work the team is investigating ways to speedup the time to solution for many different parts of the DIII-D workflow including how they run jobs on HPC systems. One of these routes is looking at Globus Compute as a way to replace the current method for managing tasks and we describe a brief proof of concept showing how Globus Compute could help to schedule jobs and be a tool to connect compute at different facilities.
Paketo Buildpacks : la meilleure façon de construire des images OCI? DevopsDa...Anthony Dahanne
Les Buildpacks existent depuis plus de 10 ans ! D’abord, ils étaient utilisés pour détecter et construire une application avant de la déployer sur certains PaaS. Ensuite, nous avons pu créer des images Docker (OCI) avec leur dernière génération, les Cloud Native Buildpacks (CNCF en incubation). Sont-ils une bonne alternative au Dockerfile ? Que sont les buildpacks Paketo ? Quelles communautés les soutiennent et comment ?
Venez le découvrir lors de cette session ignite
In software engineering, the right architecture is essential for robust, scalable platforms. Wix has undergone a pivotal shift from event sourcing to a CRUD-based model for its microservices. This talk will chart the course of this pivotal journey.
Event sourcing, which records state changes as immutable events, provided robust auditing and "time travel" debugging for Wix Stores' microservices. Despite its benefits, the complexity it introduced in state management slowed development. Wix responded by adopting a simpler, unified CRUD model. This talk will explore the challenges of event sourcing and the advantages of Wix's new "CRUD on steroids" approach, which streamlines API integration and domain event management while preserving data integrity and system resilience.
Participants will gain valuable insights into Wix's strategies for ensuring atomicity in database updates and event production, as well as caching, materialization, and performance optimization techniques within a distributed system.
Join us to discover how Wix has mastered the art of balancing simplicity and extensibility, and learn how the re-adoption of the modest CRUD has turbocharged their development velocity, resilience, and scalability in a high-growth environment.
Strategies for Successful Data Migration Tools.pptxvarshanayak241
Data migration is a complex but essential task for organizations aiming to modernize their IT infrastructure and leverage new technologies. By understanding common challenges and implementing these strategies, businesses can achieve a successful migration with minimal disruption. Data Migration Tool like Ask On Data play a pivotal role in this journey, offering features that streamline the process, ensure data integrity, and maintain security. With the right approach and tools, organizations can turn the challenge of data migration into an opportunity for growth and innovation.
How to Position Your Globus Data Portal for Success Ten Good PracticesGlobus
Science gateways allow science and engineering communities to access shared data, software, computing services, and instruments. Science gateways have gained a lot of traction in the last twenty years, as evidenced by projects such as the Science Gateways Community Institute (SGCI) and the Center of Excellence on Science Gateways (SGX3) in the US, The Australian Research Data Commons (ARDC) and its platforms in Australia, and the projects around Virtual Research Environments in Europe. A few mature frameworks have evolved with their different strengths and foci and have been taken up by a larger community such as the Globus Data Portal, Hubzero, Tapis, and Galaxy. However, even when gateways are built on successful frameworks, they continue to face the challenges of ongoing maintenance costs and how to meet the ever-expanding needs of the community they serve with enhanced features. It is not uncommon that gateways with compelling use cases are nonetheless unable to get past the prototype phase and become a full production service, or if they do, they don't survive more than a couple of years. While there is no guaranteed pathway to success, it seems likely that for any gateway there is a need for a strong community and/or solid funding streams to create and sustain its success. With over twenty years of examples to draw from, this presentation goes into detail for ten factors common to successful and enduring gateways that effectively serve as best practices for any new or developing gateway.
SOCRadar Research Team: Latest Activities of IntelBrokerSOCRadar
The European Union Agency for Law Enforcement Cooperation (Europol) has suffered an alleged data breach after a notorious threat actor claimed to have exfiltrated data from its systems. Infamous data leaker IntelBroker posted on the even more infamous BreachForums hacking forum, saying that Europol suffered a data breach this month.
The alleged breach affected Europol agencies CCSE, EC3, Europol Platform for Experts, Law Enforcement Forum, and SIRIUS. Infiltration of these entities can disrupt ongoing investigations and compromise sensitive intelligence shared among international law enforcement agencies.
However, this is neither the first nor the last activity of IntekBroker. We have compiled for you what happened in the last few days. To track such hacker activities on dark web sources like hacker forums, private Telegram channels, and other hidden platforms where cyber threats often originate, you can check SOCRadar’s Dark Web News.
Stay Informed on Threat Actors’ Activity on the Dark Web with SOCRadar!
In 2015, I used to write extensions for Joomla, WordPress, phpBB3, etc and I ...Juraj Vysvader
In 2015, I used to write extensions for Joomla, WordPress, phpBB3, etc and I didn't get rich from it but it did have 63K downloads (powered possible tens of thousands of websites).
Into the Box Keynote Day 2: Unveiling amazing updates and announcements for modern CFML developers! Get ready for exciting releases and updates on Ortus tools and products. Stay tuned for cutting-edge innovations designed to boost your productivity.
Software Engineering, Software Consulting, Tech Lead.
Spring Boot, Spring Cloud, Spring Core, Spring JDBC, Spring Security,
Spring Transaction, Spring MVC,
Log4j, REST/SOAP WEB-SERVICES.
Unleash Unlimited Potential with One-Time Purchase
BoxLang is more than just a language; it's a community. By choosing a Visionary License, you're not just investing in your success, you're actively contributing to the ongoing development and support of BoxLang.
Accelerate Enterprise Software Engineering with PlatformlessWSO2
Key takeaways:
Challenges of building platforms and the benefits of platformless.
Key principles of platformless, including API-first, cloud-native middleware, platform engineering, and developer experience.
How Choreo enables the platformless experience.
How key concepts like application architecture, domain-driven design, zero trust, and cell-based architecture are inherently a part of Choreo.
Demo of an end-to-end app built and deployed on Choreo.
Code reviews are vital for ensuring good code quality. They serve as one of our last lines of defense against bugs and subpar code reaching production.
Yet, they often turn into annoying tasks riddled with frustration, hostility, unclear feedback and lack of standards. How can we improve this crucial process?
In this session we will cover:
- The Art of Effective Code Reviews
- Streamlining the Review Process
- Elevating Reviews with Automated Tools
By the end of this presentation, you'll have the knowledge on how to organize and improve your code review proces
2. BUILDING A MACHINE LEARNING APPLICATION
WITH AWS LAMBDA
Q: What is AWS Lambda?
A: AWS Lambda is a compute service that runs code –
a Lambda function - on-demand. It simplifies the process
of running code in the cloud by managing compute
resources automatically.
Offloads DevOps tasks related to VMs:
• Server and operating system maintenance
• Capacity provisioning
• Scaling
• Code monitoring and logging
• Security patches
14. TROUBLESHOOTING
• Common Py errors
o Another H2O is already running
• Py script can’t find the data in h2o.import_file()
• Common Java errors
o Java not installed at all
• Also, must install a JDK (Java Development Kit) so that the Java compiler is
available (JRE is not sufficient)
o Not connected to the internet
• Gradle needs to fetch some dependencies from the internet
• Common Lambda errors
o Error in uploading .zip file
• Check if the function already exists and, if not, try again. For slower internet
connections, try uploading .zip file with S3 link.
o Timeout error when testing Lambda function
• Go to advanced settings and increase Timeout value
o Gateway Timeout (504 error)
• This is Lambda’s cold start behavior. Keep trying, eventually Lambda kicks in
15. CAVEATS
• Stateless
o Can access stateful data by calling other web services,
such as Amazon S3 or Amazon DynamoDB.
• Cold start behavior
o containers are instantiated and reused after the first
request and stay active for a window of time (10-20
minutes)
o “the longer I leave it between invocations, the longer
the function takes to warm up”
• API Gateway timeout of 10 secs
o Can request longer timeout
17. LAMBDA RESOURCE LIMITS
Resource Default Limit
Memory 512 MB
Number of file descriptors 1,024
Number of processes and threads
(combined total)
1,024
Maximum execution duration per request 300 seconds
Invoke request body payload size 6 MB
Invoke response body payload size 6 MB
Concurrent executions per region 100
Item Default Limit
Lambda function deployment package size
(.zip/.jar file)
50 MB
Size of code/dependencies that you can
zip into a deployment package
(uncompressed zip/jar size)
250 MB
18. LAMBDA PRICING
• Lambda
o Requests
• First 1 million per month are free
• $0.20 per 1 million requests thereafter
o Duration
• First 400,000 GB-seconds of compute time per month are free
• $0.00001667 for every GB-second thereafter
• API Gateway
o $3.50 per million API calls received plus data transfer costs
• Estimate for Malicious Domain Application:
• Lambda: $0.37/hour with 10 threads after free-tier
• API Gateway: $0.71/hour
• Total: ~$1/hr
22. RESOURCES ON THE WEB
• Slides
o GitHub h2oai/h2o-tutorials/tree/master/tutorials/aws-lambda-app
• Source code
o GitHub h2oai/app-malicious-domains
• Latest stable H2O for Python release
o http://h2o.ai/download/h2o/python
• Generated POJO model Javadoc
o http://h2o-release.s3.amazonaws.com/h2o/rel-turan/3/docs-
website/h2o-genmodel/javadoc/index.html
• AWS Lambda
o http://docs.aws.amazon.com/lambda/latest/dg/welcome.html