ML platform meetups are quarterly meetups, where we discuss and share advanced technology on machine learning infrastructure. Companies involved include Airbnb, Databricks, Facebook, Google, LinkedIn, Netflix, Pinterest, Twitter, and Uber.
ODSC webinar "Kubeflow, MLFlow and Beyond — augmenting ML delivery" Stepan Pu...Provectus
What's a machine learning workflow? What open source tools can you use to automate ML workflow?
Reproducible ML pipelines in research and production with monitoring insights from live inference clusters could enable and accelerate the delivery of AI solutions for enterprises. There is a growing ecosystem of tools that augment researchers and machine learning engineers in their day to day operations.
Still, there are big gaps in the machine learning workflow when it comes to training dataset versioning, training performance and metadata tracking, integration testing, inferencing quality monitoring, bias detection, concept drift detection and other aspects that prevent the adoption of AI in organizations of all sizes.
Multi runtime serving pipelines for machine learningStepan Pushkarev
The talk I gave at Scale By The Bay.
Deploying, Serving and monitoring machine learning models built with different ML frameworks in production. Envoy proxy powered serving mesh. TensorFlow, Spark ML, Scikit-learn and custom functions on CPU and GPU.
Modern machine learning systems may be very complex and may fall into many pitfalls. It's very easy to unintendedly introduce technical debt into such a complex structure. One of the approaches solving some of anti-patterns is a feature store. Feature store is a missing piece filling a gap between raw data and machine learning models. Not only it will help you to handle technical debt, but even more importantly speeds up time to develop new model.
Kubeflow: portable and scalable machine learning using Jupyterhub and Kuberne...Akash Tandon
ML solutions in production start from data ingestion and extend upto the actual deployment step. We want this workflow to be scalable, portable and simple. Containers and kubernetes are great at the former two but not the latter if you aren't a devops practitioner. We'll explore how you can leverage the Kubeflow project to deploy best-of-breed open-source systems for ML to diverse infrastructures.
ODSC webinar "Kubeflow, MLFlow and Beyond — augmenting ML delivery" Stepan Pu...Provectus
What's a machine learning workflow? What open source tools can you use to automate ML workflow?
Reproducible ML pipelines in research and production with monitoring insights from live inference clusters could enable and accelerate the delivery of AI solutions for enterprises. There is a growing ecosystem of tools that augment researchers and machine learning engineers in their day to day operations.
Still, there are big gaps in the machine learning workflow when it comes to training dataset versioning, training performance and metadata tracking, integration testing, inferencing quality monitoring, bias detection, concept drift detection and other aspects that prevent the adoption of AI in organizations of all sizes.
Multi runtime serving pipelines for machine learningStepan Pushkarev
The talk I gave at Scale By The Bay.
Deploying, Serving and monitoring machine learning models built with different ML frameworks in production. Envoy proxy powered serving mesh. TensorFlow, Spark ML, Scikit-learn and custom functions on CPU and GPU.
Modern machine learning systems may be very complex and may fall into many pitfalls. It's very easy to unintendedly introduce technical debt into such a complex structure. One of the approaches solving some of anti-patterns is a feature store. Feature store is a missing piece filling a gap between raw data and machine learning models. Not only it will help you to handle technical debt, but even more importantly speeds up time to develop new model.
Kubeflow: portable and scalable machine learning using Jupyterhub and Kuberne...Akash Tandon
ML solutions in production start from data ingestion and extend upto the actual deployment step. We want this workflow to be scalable, portable and simple. Containers and kubernetes are great at the former two but not the latter if you aren't a devops practitioner. We'll explore how you can leverage the Kubeflow project to deploy best-of-breed open-source systems for ML to diverse infrastructures.
Build, Train, & Deploy ML Models Using SageMaker: Machine Learning Week San F...Amazon Web Services
Machine Learning Week at the San Francisco Loft: Build, Train, and Deploy ML Models Using SageMaker
Amazon SageMaker is a fully-managed service that enables data scientists and developers to quickly and easily build, train, and deploy machine learning models, at scale. This session will introduce you the features of Amazon SageMaker, including a one-click training environment, highly-optimized machine learning algorithms with built-in model tuning, and deployment without engineering effort. With zero-setup required, Amazon SageMaker significantly decreases your training time and overall cost of building production machine learning systems.
Level: 200-300
Speaker: Amit Sharma - Principal Solutions Architect, AWS
Continuous integration and deployment has become an increasingly standard and common practice in software development. However, doing this for machine learning models and applications introduces many challenges. Not only do we need to account for standard code quality and integration testing, but how do we best account for changes in model performance metrics coming from changes to code, deployment framework or mechanism, pre- and post-processing steps, changes in data, not to mention the core deep learning model itself?
In addition, deep learning presents particular challenges:
* model sizes are often extremely large and take significant time and resources to train
* models are often more difficult to understand and interpret making it more difficult to debug issues
* inputs to deep learning are often very different from the tabular data involved in most ‘traditional machine learning’ models
* model formats, frameworks and the state-of-the art models and architectures themselves are changing extremely rapidly
* usually many disparate tools are combined to create the full end-to-end pipeline for training and deployment, making it trickier to plug together these components and track down issues.
We also need to take into account the impact of changes on wider aspects such as model bias, fairness, robustness and explainability. And we need to track all of this over time and in a standard, repeatable manner. This talk explores best practices for handling these myriad challenges to create a standardized, automated, repeatable pipeline for continuous deployment of deep learning models and pipelines. I will illustrate this through the work we are undertaking within the free and open-source IBM Model Asset eXchange.
Any startup has to have a clear go-to-market strategy from the beginning. Similarly, any data science project has to have a go-to-production strategy from its first days, so it could go beyond proof-of-concept. Machine learning and artificial intelligence in production would result in hundreds of training pipelines and machine learning models that are continuously revised by teams of data scientists and seamlessly connected with web applications for tenants and users.
In this demo-based talk we will walk through the best practices for simplifying machine learning operations across the enterprise and providing a serverless abstraction for data scientists and data engineers, so they could train, deploy and monitor machine learning models faster and with better quality.
Advanced Model Inferencing leveraging Kubeflow Serving, KNative and IstioAnimesh Singh
Model Inferencing use cases are becoming a requirement for models moving into the next phase of production deployments. More and more users are now encountering use cases around canary deployments, scale-to-zero or serverless characteristics. And then there are also advanced use cases coming around model explainability, including A/B tests, ensemble models, multi-armed bandits, etc.
In this talk, the speakers are going to detail how to handle these use cases using Kubeflow Serving and the native Kubernetes stack which is Istio and Knative. Knative and Istio help with autoscaling, scale-to-zero, canary deployments to be implemented, and scenarios where traffic is optimized to the best performing models. This can be combined with KNative eventing, Istio observability stack, KFServing Transformer to handle pre/post-processing and payload logging which consequentially can enable drift and outlier detection to be deployed. We will demonstrate where currently KFServing is, and where it's heading towards.
Expanding beyond SPL -- More language support in IBM Streams V4.1lisanl
Dan Debrunner is an architect in the IBM Streams development team. In his presentation, Dan introduces the new language support available in IBM Streams V4.1, including how to build streaming applications for IBM Streams with Java.
Hydrosphere.io for ODSC: Webinar on KubeflowRustem Zakiev
Webinar video: https://www.youtube.com/watch?v=Y3_fcJBgpMw
Kubeflow and Beyond: Automation of Model Training, Deployment, Testing, Monitoring, and Retraining
Speakers:
Stepan Pushkarev, CTO, Hydrosphere.io and Ilnur Garifullin is an ML Engineer, Hydrosphere.io
Abstract: Very often a workflow of training models and delivering them to the production environment contains loads of manual work. Those could be either building a Docker image and deploying it to the Kubernetes cluster or packing the model to the Python package and installing it to your Python application. Or even changing your Java classes with the defined weights and re-compiling the whole project. Not to mention that all of this should be followed by testing your model's performance. It hardly could be named "continuous delivery" if you do it all manually. Imagine you could run the whole process of assembling/training/deploying/testing/running model via a single command in your terminal. In this webinar, we will present a way to build the whole workflow of data gathering/model training/model deployment/model testing into a single flow and run it with a single command.
Kubeflow at Spotify (For the Kubeflow Summit)Josh Baer
A lightning talk discussing some important challenges facing ML engineers and how the introduction of Kubeflow Pipelines will help.
Full slides w/ speaker notes here: https://docs.google.com/presentation/d/12dwhS_x4568G6XQjI9SEUacD-n4hFQczBcRBLdbHNEM/edit
Running Apache Spark Jobs Using KubernetesDatabricks
Apache Spark has introduced a powerful engine for distributed data processing, providing unmatched capabilities to handle petabytes of data across multiple servers. Its capabilities and performance unseated other technologies in the Hadoop world, but while Spark provides a lot of power, it also comes with a high maintenance cost, which is why we now see innovations to simplify the Spark infrastructure.
Cloud Computing Was Built for Web Developers—What Does v2 Look Like for Deep...Databricks
What we call the public cloud was developed primarily to manage and deploy web servers. The target audience for these products is Dev Ops. While this is a massive and exciting market, the world of Data Science and Deep Learning is very different — and possibly even bigger. Unfortunately, the tools available today are not designed for this new audience and the cloud needs to evolve. This talk would cover what the next 10 years of cloud computing will look like.
ML at the Edge: Building Your Production Pipeline with Apache Spark and Tens...Databricks
The explosion of data volume in the years to come challenge the idea of a centralized cloud infrastructure which handles all business needs. Edge computing comes to rescue by pushing the needs of computation and data analysis at the edge of the network, thus avoiding data exchange when makes sense. One of the areas where data exchange could impose a big overhead is scoring ML models especially where data to score are files like images eg. in a computer vision application.
Another concern in some applications, is that of keeping data as private as possible and this is where keeping things local makes sense. In this talk we will discuss current needs and recent advances in model serving, like newly introduced formats for pushing models at the edge nodes eg. mobile phones and how a unified model serving architecture could cover current and future needs for both data scientists and data engineers. This architecture is based among others, on training models in a distributed fashion with TensorFlow and leveraging Spark for cleaning data before training (eg. using TensorFlow connector).
Finally we will describe a microservice based approach for scoring models back at the cloud infrastructure side (where bandwidth can be high) eg. using TensorFlow serving and updating models remotely with a pull model approach for edge devices. We will talk also about implementing the proposed architecture and how that might look on a modern deployment environment eg. Kubernetes.
Grokking Techtalk #42: Engineering challenges on building data platform for M...Grokking VN
Đến với Techtalk #42, các bạn sẽ được chia sẻ về cách thiết kế và hiện thực một platform phục vụ các bài toán về machine learning thông qua một case study về việc phân tích các bình luận của người dùng.
Nội dung chủ đề lần này sẽ xoay quanh một số thách thức trong quá trình xây dựng bao gồm các khó khăn về mặt kỹ thuật và phân tích khi:
+ Cần phải thu thập lượng lớn bình luận của người dùng
+ Tổ chức lưu trữ và xử lý dữ liệu để dễ dàng mở rộng, thuận tiện cho việc giám sát, vận hành
+ Thiết kế các thành phần trong hệ thống đảm báo tính tái sử dụng cao, tránh lãng phí tài nguyên
Ngôn ngữ: Tiếng Việt
---
Speakers:
- Anh Hiền Hoàng - Principal Big Data Engineer & TPP
- Anh Hiếu Hoàng - Data Scientist & TPP
Machine learning at scale by Amy Unruh from GoogleBill Liu
Presented at AI NEXTCon Seattle 1/17-20, 2018
http://aisea18.xnextcon.com
join our free online AI group with 50,000+ tech engineers to learn and practice AI technology, including: latest AI news, tech articles/blogs, tech talks, tutorial videos, and hands-on workshop/codelabs, on machine learning, deep learning, data science, etc..
Justin Basilico, Research/ Engineering Manager at Netflix at MLconf SF - 11/1...MLconf
Recommendations for Building Machine Learning Software: Building a real system that uses machine learning can be a difficult both in terms of the algorithmic and engineering challenges involved. In this talk, I will focus on the engineering side and discuss some of the practical lessons we’ve learned from years of developing the machine learning systems that power Netflix. I will go over what it takes to get machine learning working in a real-life feedback loop with our users and how that imposes different requirements and a different focus than doing machine learning only within a lab environment. This involves lessons around challenges such as where to place algorithmic components, how to handle distribution and parallelism, what kinds of modularity are useful, how to support both production experimentation, and how to test machine learning systems.
Orchestrating Cloud Workloads with RightScale Self-Service RightScale
Organizations are seeking to drive agility by offering developers a self-service portal to access cloud resources. In order to provide push-button access to the cloud, IT DevOps teams need to orchestrate the deployment, configuration and integration of entire technology stacks or applications.
Build, Train, & Deploy ML Models Using SageMaker: Machine Learning Week San F...Amazon Web Services
Machine Learning Week at the San Francisco Loft: Build, Train, and Deploy ML Models Using SageMaker
Amazon SageMaker is a fully-managed service that enables data scientists and developers to quickly and easily build, train, and deploy machine learning models, at scale. This session will introduce you the features of Amazon SageMaker, including a one-click training environment, highly-optimized machine learning algorithms with built-in model tuning, and deployment without engineering effort. With zero-setup required, Amazon SageMaker significantly decreases your training time and overall cost of building production machine learning systems.
Level: 200-300
Speaker: Amit Sharma - Principal Solutions Architect, AWS
Continuous integration and deployment has become an increasingly standard and common practice in software development. However, doing this for machine learning models and applications introduces many challenges. Not only do we need to account for standard code quality and integration testing, but how do we best account for changes in model performance metrics coming from changes to code, deployment framework or mechanism, pre- and post-processing steps, changes in data, not to mention the core deep learning model itself?
In addition, deep learning presents particular challenges:
* model sizes are often extremely large and take significant time and resources to train
* models are often more difficult to understand and interpret making it more difficult to debug issues
* inputs to deep learning are often very different from the tabular data involved in most ‘traditional machine learning’ models
* model formats, frameworks and the state-of-the art models and architectures themselves are changing extremely rapidly
* usually many disparate tools are combined to create the full end-to-end pipeline for training and deployment, making it trickier to plug together these components and track down issues.
We also need to take into account the impact of changes on wider aspects such as model bias, fairness, robustness and explainability. And we need to track all of this over time and in a standard, repeatable manner. This talk explores best practices for handling these myriad challenges to create a standardized, automated, repeatable pipeline for continuous deployment of deep learning models and pipelines. I will illustrate this through the work we are undertaking within the free and open-source IBM Model Asset eXchange.
Any startup has to have a clear go-to-market strategy from the beginning. Similarly, any data science project has to have a go-to-production strategy from its first days, so it could go beyond proof-of-concept. Machine learning and artificial intelligence in production would result in hundreds of training pipelines and machine learning models that are continuously revised by teams of data scientists and seamlessly connected with web applications for tenants and users.
In this demo-based talk we will walk through the best practices for simplifying machine learning operations across the enterprise and providing a serverless abstraction for data scientists and data engineers, so they could train, deploy and monitor machine learning models faster and with better quality.
Advanced Model Inferencing leveraging Kubeflow Serving, KNative and IstioAnimesh Singh
Model Inferencing use cases are becoming a requirement for models moving into the next phase of production deployments. More and more users are now encountering use cases around canary deployments, scale-to-zero or serverless characteristics. And then there are also advanced use cases coming around model explainability, including A/B tests, ensemble models, multi-armed bandits, etc.
In this talk, the speakers are going to detail how to handle these use cases using Kubeflow Serving and the native Kubernetes stack which is Istio and Knative. Knative and Istio help with autoscaling, scale-to-zero, canary deployments to be implemented, and scenarios where traffic is optimized to the best performing models. This can be combined with KNative eventing, Istio observability stack, KFServing Transformer to handle pre/post-processing and payload logging which consequentially can enable drift and outlier detection to be deployed. We will demonstrate where currently KFServing is, and where it's heading towards.
Expanding beyond SPL -- More language support in IBM Streams V4.1lisanl
Dan Debrunner is an architect in the IBM Streams development team. In his presentation, Dan introduces the new language support available in IBM Streams V4.1, including how to build streaming applications for IBM Streams with Java.
Hydrosphere.io for ODSC: Webinar on KubeflowRustem Zakiev
Webinar video: https://www.youtube.com/watch?v=Y3_fcJBgpMw
Kubeflow and Beyond: Automation of Model Training, Deployment, Testing, Monitoring, and Retraining
Speakers:
Stepan Pushkarev, CTO, Hydrosphere.io and Ilnur Garifullin is an ML Engineer, Hydrosphere.io
Abstract: Very often a workflow of training models and delivering them to the production environment contains loads of manual work. Those could be either building a Docker image and deploying it to the Kubernetes cluster or packing the model to the Python package and installing it to your Python application. Or even changing your Java classes with the defined weights and re-compiling the whole project. Not to mention that all of this should be followed by testing your model's performance. It hardly could be named "continuous delivery" if you do it all manually. Imagine you could run the whole process of assembling/training/deploying/testing/running model via a single command in your terminal. In this webinar, we will present a way to build the whole workflow of data gathering/model training/model deployment/model testing into a single flow and run it with a single command.
Kubeflow at Spotify (For the Kubeflow Summit)Josh Baer
A lightning talk discussing some important challenges facing ML engineers and how the introduction of Kubeflow Pipelines will help.
Full slides w/ speaker notes here: https://docs.google.com/presentation/d/12dwhS_x4568G6XQjI9SEUacD-n4hFQczBcRBLdbHNEM/edit
Running Apache Spark Jobs Using KubernetesDatabricks
Apache Spark has introduced a powerful engine for distributed data processing, providing unmatched capabilities to handle petabytes of data across multiple servers. Its capabilities and performance unseated other technologies in the Hadoop world, but while Spark provides a lot of power, it also comes with a high maintenance cost, which is why we now see innovations to simplify the Spark infrastructure.
Cloud Computing Was Built for Web Developers—What Does v2 Look Like for Deep...Databricks
What we call the public cloud was developed primarily to manage and deploy web servers. The target audience for these products is Dev Ops. While this is a massive and exciting market, the world of Data Science and Deep Learning is very different — and possibly even bigger. Unfortunately, the tools available today are not designed for this new audience and the cloud needs to evolve. This talk would cover what the next 10 years of cloud computing will look like.
ML at the Edge: Building Your Production Pipeline with Apache Spark and Tens...Databricks
The explosion of data volume in the years to come challenge the idea of a centralized cloud infrastructure which handles all business needs. Edge computing comes to rescue by pushing the needs of computation and data analysis at the edge of the network, thus avoiding data exchange when makes sense. One of the areas where data exchange could impose a big overhead is scoring ML models especially where data to score are files like images eg. in a computer vision application.
Another concern in some applications, is that of keeping data as private as possible and this is where keeping things local makes sense. In this talk we will discuss current needs and recent advances in model serving, like newly introduced formats for pushing models at the edge nodes eg. mobile phones and how a unified model serving architecture could cover current and future needs for both data scientists and data engineers. This architecture is based among others, on training models in a distributed fashion with TensorFlow and leveraging Spark for cleaning data before training (eg. using TensorFlow connector).
Finally we will describe a microservice based approach for scoring models back at the cloud infrastructure side (where bandwidth can be high) eg. using TensorFlow serving and updating models remotely with a pull model approach for edge devices. We will talk also about implementing the proposed architecture and how that might look on a modern deployment environment eg. Kubernetes.
Grokking Techtalk #42: Engineering challenges on building data platform for M...Grokking VN
Đến với Techtalk #42, các bạn sẽ được chia sẻ về cách thiết kế và hiện thực một platform phục vụ các bài toán về machine learning thông qua một case study về việc phân tích các bình luận của người dùng.
Nội dung chủ đề lần này sẽ xoay quanh một số thách thức trong quá trình xây dựng bao gồm các khó khăn về mặt kỹ thuật và phân tích khi:
+ Cần phải thu thập lượng lớn bình luận của người dùng
+ Tổ chức lưu trữ và xử lý dữ liệu để dễ dàng mở rộng, thuận tiện cho việc giám sát, vận hành
+ Thiết kế các thành phần trong hệ thống đảm báo tính tái sử dụng cao, tránh lãng phí tài nguyên
Ngôn ngữ: Tiếng Việt
---
Speakers:
- Anh Hiền Hoàng - Principal Big Data Engineer & TPP
- Anh Hiếu Hoàng - Data Scientist & TPP
Machine learning at scale by Amy Unruh from GoogleBill Liu
Presented at AI NEXTCon Seattle 1/17-20, 2018
http://aisea18.xnextcon.com
join our free online AI group with 50,000+ tech engineers to learn and practice AI technology, including: latest AI news, tech articles/blogs, tech talks, tutorial videos, and hands-on workshop/codelabs, on machine learning, deep learning, data science, etc..
Justin Basilico, Research/ Engineering Manager at Netflix at MLconf SF - 11/1...MLconf
Recommendations for Building Machine Learning Software: Building a real system that uses machine learning can be a difficult both in terms of the algorithmic and engineering challenges involved. In this talk, I will focus on the engineering side and discuss some of the practical lessons we’ve learned from years of developing the machine learning systems that power Netflix. I will go over what it takes to get machine learning working in a real-life feedback loop with our users and how that imposes different requirements and a different focus than doing machine learning only within a lab environment. This involves lessons around challenges such as where to place algorithmic components, how to handle distribution and parallelism, what kinds of modularity are useful, how to support both production experimentation, and how to test machine learning systems.
Orchestrating Cloud Workloads with RightScale Self-Service RightScale
Organizations are seeking to drive agility by offering developers a self-service portal to access cloud resources. In order to provide push-button access to the cloud, IT DevOps teams need to orchestrate the deployment, configuration and integration of entire technology stacks or applications.
Establishing SOA Focused Enterprise ArchitectureChris Haddad
Enterprise architecture frameworks (i.e. TOGAF) define data, application, technology, and business domains. Where do services, APIs , and streams fit into the blueprint? Teams can enhance architectural integrity and coherence by establishing a SOA-focused and API-centric foundation for their architecture efforts. In this presentation, Chris will describe key Enterprise Architecture patterns and practices that accelerate project delivery and create a SOA-focused architecture. During this session, you will learn:
Why SOA-focused Enterprise Architecture and API-centric approaches accelerate project delivery and increase
What patterns and practices help overcome common SOA and Enterprise Architecture challenges
How to fit project-oriented service development into an Enterprise Architecture picture
A discussion of deployment options for IBM Collaborative Life Cycle Management. The IBM Rational CLM products consist of Rational Team Concert, Rational Requirement Composer and Rational Quality Manager. This presentation covers the different options of integrating them into an existing software development environment.
This presentation discusses SQL Server 2008 Migration tools, planning and execution. You will learn about the SQL Server Featuer Pack, the SQL Server Migration Assistant, and Performance Benchmarks of SQL Server 2005 vs. 2008.
DesignMind is located in Emeryville, California.
www.designmind.com
This topic introduces the need of a unique architecture style for Cloud Native application deployments. Further, the fitment of DevOps, usage of Microservices and the runtime of Cloud Native application (* as a Service) are covered in detail. The need of distributed computing in Cloud for Cloud Native applications is trivial to understand. Insights on the same are covered.
vCloud Automation Center and Pivotal Cloud Foundry – Better PaaS Solution (VM...VMware Tanzu
David Benedict - Member of Technical Staff, VMware
Cornelia Davis - Platform Engineer, Cloud Foundry, Pivotal
Vipul Shah - Director of Product Management, VMware
vCloud Automation Center provides powerful capabilities for policy-based orchestration of complex infrastructure and application deployments. A Platform as a Service (PaaS) such as Pivotal CF, built on the open-source Cloud Foundry, presents a set of abstractions and capabilities that focus on the application implementation and the run-time services it will leverage.
The value of a PaaS installation is equally driven by the set of application-centric capabilities provided, such as performance monitoring or logging, and by the set of services that can easily be integrated into an application; exposing the offerings in the vCloud Automation Center services catalog for leverage by apps deployed into Pivotal CF allows an enterprise faster time to value. And a vCloud Automation Center user can model system deployments, automating infrastructure provisioning and software deployments; this modeling is equally valuable even when the targets of the orchestrations are the PaaS abstractions of applications and services.
These products are very complementary and we’ll show you how. Understand how the combined vCloud Automation Center / Pivotal CF solutions provide the basis for a comprehensive PaaS solution. See a demo of and roadmap for the integrated solution. Learn how to use vCloud Automation Center to model applications for deployment into Pivotal CF and how to draw vCloud Automation Center services into Pivotal CF.
After a brief overview of both products, we will describe the capabilities and derived value of the joint solution that will have early access availability at the time of the conference.
This is a must-read for all engineers interested in developing a Micro services architecture. Turn your monolithic server into a prolific and multiple instance solution! Includes well-known example such as Netflix. Please contact me for more details.
Tokyo Azure Meetup #7 - Introduction to Serverless Architectures with Azure F...Tokyo Azure Meetup
Serverless architecture is the next big shift in computing - completely abstracting the underlying infrastructure and focusing 100% on the business logic.
Today we can create applications directly in our browser and leave the decision how they are hosted and scaled to the cloud provider. Moreover, this approach give us incredible control over the granularity of our applications since most of the time we are dealing with single function at a time.
In this presentation we will cover:
• Introduce Serverless Architectures
• Talk about the advantages of Serverless Architectures
• Discuss in details in event-driven computing
• Cover common Serverless approaches
• See practical applications with Azure Functions
• Compare AWS Lambda and Azure Functions
• Talk about open source alternatives
• Explore the relation between Microservices and Serverless Architectures
Developing scalable enterprise serverless applications on azure with .netCallon Campbell
Over the years we have seen an accelerated shift to adopting serverless and cloud-native application architectures. Benefits to these architectures include decreased infrastructure costs and improved time to market, however, it's still important to consider high availability and resiliency in your application design. In this session, Callon will talk about developing scalable enterprise serverless applications on Azure with .NET and use a real-world example of a solution he developed and running in production.
Hadoop for the Data Scientist: Spark in Cloudera 5.5Cloudera, Inc.
Inefficient data workloads are all too common across enterprises - causing costly delays, breakages, hard-to-maintain complexity, and ultimately lost productivity. For a typical enterprise with multiple data warehouses, thousands of reports, and hundreds of thousands of ETL jobs being executed every day, this loss of productivity is a real problem. Add to all of this the complex handwritten SQL queries, and there can be nearly a million queries executed every month that desperately need to be optimized, especially to take advantage of the benefits of Apache Hadoop. How can enterprises dig through their workloads and inefficiencies to easily see which are the best fit for Hadoop and what’s the fastest path to get there?
Cloudera Navigator Optimizer is the solution - analyzing existing SQL workloads to provide instant insights into your workloads and turns that into an intelligent optimization strategy so you can unlock peak performance and efficiency with Hadoop. As the newest addition to Cloudera’s enterprise Hadoop platform, and now available in limited beta, Navigator Optimizer has helped customers profile over 1.5 million queries and ultimately save millions by optimizing for Hadoop.
Similar to ML Platform Q1 Meetup: An introduction to LinkedIn's Ranking and Federation Libraries (20)
Software Engineering, Software Consulting, Tech Lead, Spring Boot, Spring Cloud, Spring Core, Spring JDBC, Spring Transaction, Spring MVC, OpenShift Cloud Platform, Kafka, REST, SOAP, LLD & HLD.
Mobile App Development Company In Noida | Drona InfotechDrona Infotech
Looking for a reliable mobile app development company in Noida? Look no further than Drona Infotech. We specialize in creating customized apps for your business needs.
Visit Us For : https://www.dronainfotech.com/mobile-application-development/
Enterprise Resource Planning System includes various modules that reduce any business's workload. Additionally, it organizes the workflows, which drives towards enhancing productivity. Here are a detailed explanation of the ERP modules. Going through the points will help you understand how the software is changing the work dynamics.
To know more details here: https://blogs.nyggs.com/nyggs/enterprise-resource-planning-erp-system-modules/
GraphSummit Paris - The art of the possible with Graph TechnologyNeo4j
Sudhir Hasbe, Chief Product Officer, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
Need for Speed: Removing speed bumps from your Symfony projects ⚡️Łukasz Chruściel
No one wants their application to drag like a car stuck in the slow lane! Yet it’s all too common to encounter bumpy, pothole-filled solutions that slow the speed of any application. Symfony apps are not an exception.
In this talk, I will take you for a spin around the performance racetrack. We’ll explore common pitfalls - those hidden potholes on your application that can cause unexpected slowdowns. Learn how to spot these performance bumps early, and more importantly, how to navigate around them to keep your application running at top speed.
We will focus in particular on tuning your engine at the application level, making the right adjustments to ensure that your system responds like a well-oiled, high-performance race car.
Quarkus Hidden and Forbidden ExtensionsMax Andersen
Quarkus has a vast extension ecosystem and is known for its subsonic and subatomic feature set. Some of these features are not as well known, and some extensions are less talked about, but that does not make them less interesting - quite the opposite.
Come join this talk to see some tips and tricks for using Quarkus and some of the lesser known features, extensions and development techniques.
A Study of Variable-Role-based Feature Enrichment in Neural Models of CodeAftab Hussain
Understanding variable roles in code has been found to be helpful by students
in learning programming -- could variable roles help deep neural models in
performing coding tasks? We do an exploratory study.
- These are slides of the talk given at InteNSE'23: The 1st International Workshop on Interpretability and Robustness in Neural Software Engineering, co-located with the 45th International Conference on Software Engineering, ICSE 2023, Melbourne Australia
Introducing Crescat - Event Management Software for Venues, Festivals and Eve...Crescat
Crescat is industry-trusted event management software, built by event professionals for event professionals. Founded in 2017, we have three key products tailored for the live event industry.
Crescat Event for concert promoters and event agencies. Crescat Venue for music venues, conference centers, wedding venues, concert halls and more. And Crescat Festival for festivals, conferences and complex events.
With a wide range of popular features such as event scheduling, shift management, volunteer and crew coordination, artist booking and much more, Crescat is designed for customisation and ease-of-use.
Over 125,000 events have been planned in Crescat and with hundreds of customers of all shapes and sizes, from boutique event agencies through to international concert promoters, Crescat is rigged for success. What's more, we highly value feedback from our users and we are constantly improving our software with updates, new features and improvements.
If you plan events, run a venue or produce festivals and you're looking for ways to make your life easier, then we have a solution for you. Try our software for free or schedule a no-obligation demo with one of our product specialists today at crescat.io
AI Genie Review: World’s First Open AI WordPress Website CreatorGoogle
AI Genie Review: World’s First Open AI WordPress Website Creator
👉👉 Click Here To Get More Info 👇👇
https://sumonreview.com/ai-genie-review
AI Genie Review: Key Features
✅Creates Limitless Real-Time Unique Content, auto-publishing Posts, Pages & Images directly from Chat GPT & Open AI on WordPress in any Niche
✅First & Only Google Bard Approved Software That Publishes 100% Original, SEO Friendly Content using Open AI
✅Publish Automated Posts and Pages using AI Genie directly on Your website
✅50 DFY Websites Included Without Adding Any Images, Content Or Doing Anything Yourself
✅Integrated Chat GPT Bot gives Instant Answers on Your Website to Visitors
✅Just Enter the title, and your Content for Pages and Posts will be ready on your website
✅Automatically insert visually appealing images into posts based on keywords and titles.
✅Choose the temperature of the content and control its randomness.
✅Control the length of the content to be generated.
✅Never Worry About Paying Huge Money Monthly To Top Content Creation Platforms
✅100% Easy-to-Use, Newbie-Friendly Technology
✅30-Days Money-Back Guarantee
See My Other Reviews Article:
(1) TubeTrivia AI Review: https://sumonreview.com/tubetrivia-ai-review
(2) SocioWave Review: https://sumonreview.com/sociowave-review
(3) AI Partner & Profit Review: https://sumonreview.com/ai-partner-profit-review
(4) AI Ebook Suite Review: https://sumonreview.com/ai-ebook-suite-review
#AIGenieApp #AIGenieBonus #AIGenieBonuses #AIGenieDemo #AIGenieDownload #AIGenieLegit #AIGenieLiveDemo #AIGenieOTO #AIGeniePreview #AIGenieReview #AIGenieReviewandBonus #AIGenieScamorLegit #AIGenieSoftware #AIGenieUpgrades #AIGenieUpsells #HowDoesAlGenie #HowtoBuyAIGenie #HowtoMakeMoneywithAIGenie #MakeMoneyOnline #MakeMoneywithAIGenie
AI Fusion Buddy Review: Brand New, Groundbreaking Gemini-Powered AI AppGoogle
AI Fusion Buddy Review: Brand New, Groundbreaking Gemini-Powered AI App
👉👉 Click Here To Get More Info 👇👇
https://sumonreview.com/ai-fusion-buddy-review
AI Fusion Buddy Review: Key Features
✅Create Stunning AI App Suite Fully Powered By Google's Latest AI technology, Gemini
✅Use Gemini to Build high-converting Converting Sales Video Scripts, ad copies, Trending Articles, blogs, etc.100% unique!
✅Create Ultra-HD graphics with a single keyword or phrase that commands 10x eyeballs!
✅Fully automated AI articles bulk generation!
✅Auto-post or schedule stunning AI content across all your accounts at once—WordPress, Facebook, LinkedIn, Blogger, and more.
✅With one keyword or URL, generate complete websites, landing pages, and more…
✅Automatically create & sell AI content, graphics, websites, landing pages, & all that gets you paid non-stop 24*7.
✅Pre-built High-Converting 100+ website Templates and 2000+ graphic templates logos, banners, and thumbnail images in Trending Niches.
✅Say goodbye to wasting time logging into multiple Chat GPT & AI Apps once & for all!
✅Save over $5000 per year and kick out dependency on third parties completely!
✅Brand New App: Not available anywhere else!
✅ Beginner-friendly!
✅ZERO upfront cost or any extra expenses
✅Risk-Free: 30-Day Money-Back Guarantee!
✅Commercial License included!
See My Other Reviews Article:
(1) AI Genie Review: https://sumonreview.com/ai-genie-review
(2) SocioWave Review: https://sumonreview.com/sociowave-review
(3) AI Partner & Profit Review: https://sumonreview.com/ai-partner-profit-review
(4) AI Ebook Suite Review: https://sumonreview.com/ai-ebook-suite-review
#AIFusionBuddyReview,
#AIFusionBuddyFeatures,
#AIFusionBuddyPricing,
#AIFusionBuddyProsandCons,
#AIFusionBuddyTutorial,
#AIFusionBuddyUserExperience
#AIFusionBuddyforBeginners,
#AIFusionBuddyBenefits,
#AIFusionBuddyComparison,
#AIFusionBuddyInstallation,
#AIFusionBuddyRefundPolicy,
#AIFusionBuddyDemo,
#AIFusionBuddyMaintenanceFees,
#AIFusionBuddyNewbieFriendly,
#WhatIsAIFusionBuddy?,
#HowDoesAIFusionBuddyWorks
Zoom is a comprehensive platform designed to connect individuals and teams efficiently. With its user-friendly interface and powerful features, Zoom has become a go-to solution for virtual communication and collaboration. It offers a range of tools, including virtual meetings, team chat, VoIP phone systems, online whiteboards, and AI companions, to streamline workflows and enhance productivity.
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
Navigating the Metaverse: A Journey into Virtual Evolution"Donna Lenk
Join us for an exploration of the Metaverse's evolution, where innovation meets imagination. Discover new dimensions of virtual events, engage with thought-provoking discussions, and witness the transformative power of digital realms."
Artificia Intellicence and XPath Extension FunctionsOctavian Nadolu
The purpose of this presentation is to provide an overview of how you can use AI from XSLT, XQuery, Schematron, or XML Refactoring operations, the potential benefits of using AI, and some of the challenges we face.
Top Features to Include in Your Winzo Clone App for Business Growth (4).pptxrickgrimesss22
Discover the essential features to incorporate in your Winzo clone app to boost business growth, enhance user engagement, and drive revenue. Learn how to create a compelling gaming experience that stands out in the competitive market.
Top Features to Include in Your Winzo Clone App for Business Growth (4).pptx
ML Platform Q1 Meetup: An introduction to LinkedIn's Ranking and Federation Libraries
1. Quasar and ReMix
An introduction to LinkedIn's Ranking and Federation libraries
Andris Birkmanis & Lance Wall
1
2. Relevance: Verticals & Infrastructure
2
Relevance
Isolated ML
models
Integrated ML
models
Relevance
Infra
Relevance
Verticals
Deployed ML
services
ML algos Scoring
and Ranking
Tools
Relevance
service platform
Quasar ReMix
3. Quasar
Quick Scoring and Ranking
Our mission is making efficient feature transformation, scoring, and ranking simple.
3
6. Relevance Models: DAGs of Computations
Filter BY
interest-match
> 0.5
Filter BY
skill-match
> 0.7
TOP 50 BY
content-match
All
Documents
member
interest category
interest-
match-
score
news
feed
skill content
skill-
match-
score
content-
match-
score
10,000
500 500
50
7. ML and Training
• Tracking training dependencies between ML models
• Integrating with training engines via Training API
• Automatic type conversion for features and model parameters
• Reuse of feature transformations between training and prediction
7
8. Quasar Components
• Domain Specific Language (DSL)
▪ Oriented towards scoring and ranking concepts
▪ Supports various machine learning models
▪ Supports various ranking operators
▪ Supports pluggable feature transformers
▪ Supports arithmetical and logical expressions
• Library
▪ Includes out-of-box feature transformers tuned for performance (dense/sparse vectors, bags of
words, etc.)
▪ Extensible with custom transformers and ranking operators
• Execution engine
▪ Supports multiple evaluation strategies for different objectives (lazy/eager/batching/etc.)
▪ Debuggability, logging, and other cross-cutting concerns
▪ API for scoring, ranking, read/write access to features, training
10. Future directions
• Better training support for external models (XGBoost, Tensorflow)
• Making feature transformers and operators more reusable
• Better type information
• Standardized storage formats for features and model parameters
• See the upcoming LinkedIn engineering blog for technical details
10
12. Example relevance workflows at LinkedIn
Member ID
Fetch
Member
Profile
Fetch
Member
Profile
Compute
Job
Recommendations
Compute
People
Recommendations
Format
Results
Member ID
Format
Results
13. Motivation
• Multitenant relevance workflow services with tens of engineers on
multiple teams contributing
• Each relevance workflow service has different APIs and conventions
• Lack of abstraction of system-level concerns from application logic
• Diminished productivity, operability, and leverage
14. ReMix’s Mission
Provide an easy to use platform for building relevance services
with a focus on optimizing leverage and automating common
operability concerns.
15. Design Goals
• Consolidation of various relevance service stacks
• Ease of support
• Ease of development
• Ease of operation
16. Features of ReMix
• Leverages ParSeq for easy asynchronous I/O
• Exposes declarative API for composing workflows
• Provides automated monitoring instrumentation and tooling
• Provides robust, extensible solutions for common workflow
functionality
• Provides isolation and robustness to downstream instability
17. How does ReMix work?
Operator
is assembled into
Workflow
is submitted to
WorkflowEngine
18. Operator
• Modular functional component of a Workflow
• ReMix provides Operators for common functionality
• ReMix provides decorative interfaces for common optimizations
• ReMix provides generic support for asynchronous execution
19. Example relevance workflows at LinkedIn
Member ID
Fetch
Member
Profile
Fetch
Member
Profile
Compute
Job
Recommendations
Compute
People
Recommendations
Format
Results
Member ID
Format
Results
20. Workflow
• Declaration of deferred execution
• Easy to understand declarative language
• Leverages ParSeq and exposes a simpler API
• Abstraction of execution behavior & optimizations
• Independent of environment or service (i.e. portable)
21. Example relevance workflows at LinkedIn
Member ID
Fetch
Member
Profile
Fetch
Member
Profile
Compute
Job
Recommendations
Compute
People
Recommendations
Format
Results
Member ID
Format
Results
22. WorkflowEngine
• Executor of Workflows
• Translates Workflows to ParSeq Tasks
• Provides special considerations for async/RPC operations
• Provides common operability functionality
23. Project Status & Planned Work
• ReMix adopters include job recommendations and blended search
• Working on integration with Quasar
▪ Complete solution for model serving from offline to online
• ReMix Cloud
▪ Simple toolkit/UI for creating a Workflow and deploying it to production
▪ Hosts Workflows in a managed service, with little to no operational cost to
Workflow developers
▪ Increased leverage due to reuse of common components in multitenant platform
27. Candidate list of documents
Filter Documents
getInterest
s
getCateg
ories
getPublish
edTime
getSimilari
ty
Bucketize
LinearSco
re
getCateg
ories
getPublish
edTime
getSimilari
ty
Bucketize
LinearSco
re
getCateg
ories
getPublish
edTime
getSimilari
ty
Bucketize
LinearSco
re
getCateg
ories
getPublish
edTime
getSimilari
ty
Bucketize
LinearSco
re
1 3 4Request
1 3 4
3 1 4
Order
Documents
Pass 1
2
Pass 2
Decision
Tree
LinearSc
ore
Decision
Tree
LinearSc
ore
getVie
wTimes
Bucke
tize
Decision
Tree
LinearSc
ore
The multipass ensemble
model
at runtime
28. Vector Math and Expression Support
• Vector as first class citizen in DSL
• State-of-art Java Vector implementation
▪ Compact and efficient data structure
▪ Efficient Vector math computation
C++
Java
Networ
k
1.0
1.0
3.0
1.0
Linux
Member/Job
Similarity
Score
=
member.skill
Hadoop
Scala
Gradle
2.0
1.0
2.0
job.required_skill
dot
product