Weave GitOps is a continuous delivery product to run apps in any Kubernetes. Weave GitOps accelerates the cloud native transformation empowering developers and creating a meaningful connection between infrastructure and business objectives.
Cloud native companies are faster, more resilient, fulfill market needs better than the competition and even create new markets with less upfront investment. How? By delivering applications to Kubernetes and by continuously operating in multi cloud environments. Weave GitOps strives to make these processes reliable, secure and repeatable at scale by allowing developers and operators to collaborate in a single place, Git.
We’ve rearranged our portfolio to offer one product with two tiers: a free and open source product called Weave GitOps Core and a paid tier called Weave GitOps Enterprise (previously called Weave Kubernetes Platform, our flagship product).
Video and slides synchronized, mp3 and slide download available at URL https://bit.ly/2S7lDiS.
Sasha Rosenbaum shows how a CI/CD pipeline for Machine Learning can greatly improve both productivity and reliability. Filmed at qconsf.com.
Sasha Rosenbaum is a Program Manager on the Azure DevOps engineering team, focused on improving the alignment of the product with open source software. She is a co-organizer of the DevOps Days Chicago and the DeliveryConf conferences, and recently published a book on Serverless computing in Azure with .NET.
Building A Production-Level Machine Learning PipelineRobert Dempsey
With so many options to choose from how do you select the right technologies to use for your machine learning pipeline? Do you purchase bare metal and hire a devops team, install Spark on EC2 instances, use EMR and other AWS services, combine Spark and Elasticsearch?! View this talk to get a first-hand experience of building ML pipelines: what options were looked at, how the final solution was selected, the tradeoffs made and the final results.
Revolutionary container based hybrid cloud solution for MLPlatform
Ness' data science platform, NextGenML, puts the entire machine learning process: modelling, execution and deployment in the hands of data science teams.
The entire paradigm approaches collaboration around AI/ML, being implemented with full respect for best practices and commitment to innovation.
Kubernetes (onPrem) + Docker, Azure Kubernetes Cluster (AKS), Nexus, Azure Container Registry(ACR), GlusterFS
Workflow
Argo->Kubeflow
DevOps
Helm, kSonnet, Kustomize,Azure DevOps
Code Management & CI/CD
Git, TeamCity, SonarQube, Jenkins
Security
MS Active Directory, Azure VPN, Dex (K8s) integrated with GitLab
Machine Learning
TensorFlow (model training, boarding, serving), Keras, Seldon
Storage (Azure)
Storage Gen1 & Gen2, Data Lake, File Storage
ETL (Azure)
Databricks, Spark on K8, Data Factory (ADF), HDInsight (Kafka and Spark), Service Bus (ASB)
Lambda functions & VMs, Cache for Redis
Monitoring and Logging
Graphana, Prometeus, GrayLog
Monitoring AI applications with AI
The best performing offline algorithm can lose in production. The most accurate model does not always improve business metrics. Environment misconfiguration or upstream data pipeline inconsistency can silently kill the model performance. Neither prodops, data science or engineering teams are skilled to detect, monitor and debug such types of incidents.
Was it possible for Microsoft to test Tay chatbot in advance and then monitor and adjust it continuously in production to prevent its unexpected behaviour? Real mission critical AI systems require advanced monitoring and testing ecosystem which enables continuous and reliable delivery of machine learning models and data pipelines into production. Common production incidents include:
Data drifts, new data, wrong features
Vulnerability issues, malicious users
Concept drifts
Model Degradation
Biased Training set / training issue
Performance issue
In this demo based talk we discuss a solution, tooling and architecture that allows machine learning engineer to be involved in delivery phase and take ownership over deployment and monitoring of machine learning pipelines.
It allows data scientists to safely deploy early results as end-to-end AI applications in a self serve mode without assistance from engineering and operations teams. It shifts experimentation and even training phases from offline datasets to live production and closes a feedback loop between research and production.
Technical part of the talk will cover the following topics:
Automatic Data Profiling
Anomaly Detection
Clustering of inputs and outputs of the model
A/B Testing
Service Mesh, Envoy Proxy, trafic shadowing
Stateless and stateful models
Monitoring of regression, classification and prediction models
Simplifying AI integration on Apache SparkDatabricks
Spark is an ETL and Data Processing engine especially suited for big data. Most of the time an organization has different teams working on different languages, frameworks and libraries, which needs to be integrated in the ETL Pipelines or for general data processing. For example, a Spark ETL job may be written in Scala by data engineering team, but there is a need to integrate a machine learning solution written in python/R developed by Data Science team. These kinds of solutions are not very straightforward to integrate with spark engine, and it required great amount of collaboration between different teams, hence increasing overall project time and cost. Furthermore, these solutions will keep on changing/upgrading with time using latest versions of the technologies and with improved design and implementation, especially in Machine Learning domain where ML models/algorithms keep on improving with new data and new approaches. And so there is significant downtime involved in integrating the these upgraded version.
Video and slides synchronized, mp3 and slide download available at URL https://bit.ly/2S7lDiS.
Sasha Rosenbaum shows how a CI/CD pipeline for Machine Learning can greatly improve both productivity and reliability. Filmed at qconsf.com.
Sasha Rosenbaum is a Program Manager on the Azure DevOps engineering team, focused on improving the alignment of the product with open source software. She is a co-organizer of the DevOps Days Chicago and the DeliveryConf conferences, and recently published a book on Serverless computing in Azure with .NET.
Building A Production-Level Machine Learning PipelineRobert Dempsey
With so many options to choose from how do you select the right technologies to use for your machine learning pipeline? Do you purchase bare metal and hire a devops team, install Spark on EC2 instances, use EMR and other AWS services, combine Spark and Elasticsearch?! View this talk to get a first-hand experience of building ML pipelines: what options were looked at, how the final solution was selected, the tradeoffs made and the final results.
Revolutionary container based hybrid cloud solution for MLPlatform
Ness' data science platform, NextGenML, puts the entire machine learning process: modelling, execution and deployment in the hands of data science teams.
The entire paradigm approaches collaboration around AI/ML, being implemented with full respect for best practices and commitment to innovation.
Kubernetes (onPrem) + Docker, Azure Kubernetes Cluster (AKS), Nexus, Azure Container Registry(ACR), GlusterFS
Workflow
Argo->Kubeflow
DevOps
Helm, kSonnet, Kustomize,Azure DevOps
Code Management & CI/CD
Git, TeamCity, SonarQube, Jenkins
Security
MS Active Directory, Azure VPN, Dex (K8s) integrated with GitLab
Machine Learning
TensorFlow (model training, boarding, serving), Keras, Seldon
Storage (Azure)
Storage Gen1 & Gen2, Data Lake, File Storage
ETL (Azure)
Databricks, Spark on K8, Data Factory (ADF), HDInsight (Kafka and Spark), Service Bus (ASB)
Lambda functions & VMs, Cache for Redis
Monitoring and Logging
Graphana, Prometeus, GrayLog
Monitoring AI applications with AI
The best performing offline algorithm can lose in production. The most accurate model does not always improve business metrics. Environment misconfiguration or upstream data pipeline inconsistency can silently kill the model performance. Neither prodops, data science or engineering teams are skilled to detect, monitor and debug such types of incidents.
Was it possible for Microsoft to test Tay chatbot in advance and then monitor and adjust it continuously in production to prevent its unexpected behaviour? Real mission critical AI systems require advanced monitoring and testing ecosystem which enables continuous and reliable delivery of machine learning models and data pipelines into production. Common production incidents include:
Data drifts, new data, wrong features
Vulnerability issues, malicious users
Concept drifts
Model Degradation
Biased Training set / training issue
Performance issue
In this demo based talk we discuss a solution, tooling and architecture that allows machine learning engineer to be involved in delivery phase and take ownership over deployment and monitoring of machine learning pipelines.
It allows data scientists to safely deploy early results as end-to-end AI applications in a self serve mode without assistance from engineering and operations teams. It shifts experimentation and even training phases from offline datasets to live production and closes a feedback loop between research and production.
Technical part of the talk will cover the following topics:
Automatic Data Profiling
Anomaly Detection
Clustering of inputs and outputs of the model
A/B Testing
Service Mesh, Envoy Proxy, trafic shadowing
Stateless and stateful models
Monitoring of regression, classification and prediction models
Simplifying AI integration on Apache SparkDatabricks
Spark is an ETL and Data Processing engine especially suited for big data. Most of the time an organization has different teams working on different languages, frameworks and libraries, which needs to be integrated in the ETL Pipelines or for general data processing. For example, a Spark ETL job may be written in Scala by data engineering team, but there is a need to integrate a machine learning solution written in python/R developed by Data Science team. These kinds of solutions are not very straightforward to integrate with spark engine, and it required great amount of collaboration between different teams, hence increasing overall project time and cost. Furthermore, these solutions will keep on changing/upgrading with time using latest versions of the technologies and with improved design and implementation, especially in Machine Learning domain where ML models/algorithms keep on improving with new data and new approaches. And so there is significant downtime involved in integrating the these upgraded version.
Managing and Versioning Machine Learning Models in PythonSimon Frid
Practical machine learning is becoming messy, and while there are lots of algorithms, there is still a lot of infrastructure needed to manage and organize the models and datasets. Estimators and Django-Estimators are two python packages that can help version data sets and models, for deployment and effective workflow.
Tutorial for Machine Learning 101 (an all-day tutorial at Strata + Hadoop World, New York City, 2015)
The course is designed to introduce machine learning via real applications like building a recommender image analysis using deep learning.
In this talk we cover deployment of machine learning models.
Managers guide to effective building of machine learning productsGianmario Spacagna
Part 1/2 (Managers)
Data and Machine Learning (ML) technologies are now widespread and adopted by literally all industries. Although recent advancements in the field have reached an unthinkable level of maturity, many organizations still struggle with turning these advances into tangible profits. Unfortunately, many ML projects get stuck in a proof-of-concept stage without ever reaching customers and generating revenue. In order to effectively adopt ML technologies, enterprises need to build the right business cases as well as to be ready to face the inevitable challenges. In this talk, we will share common pitfalls, lessons learned, and best practices, while building different enterprise products. In particular, we will focus on the generic use case of ML as the core technology enabling customer-facing products regardless of the specific industry or application.
You will:
Understand if ML is the right solution for your business and set the right expectations;
Deal with the additional uncertainty of ML projects with respect to traditional software;
Build a balanced ML team and cover the broad spectrum of skills;
Know how to apply the scientific workflow in an agile development framework;
Learn how to turn research into production systems including engineering practices and tools;
Be able to leverage modern cloud and serverless architecture for scalable, autonomous and cheaper deployments.
Deploying and managing machine learning models at scale introduces new complexities. Fortunately, there are tools that simplify this process. In this talk we walk you through an end-to-end hands on example showing how you can go from research to production without much complexity by leveraging the Seldon Core and MLflow frameworks. We will train a set of ML models, and we will showcase a simple way to deploy them to a Kubernetes cluster through sophisticated deployment methods, including canary deployments, shadow deployments and we’ll touch upon richer ML graphs such as explainer deployments.
Developing ML-enabled Data Pipelines on Databricks using IDE & CI/CD at Runta...Databricks
Data & ML projects bring many new complexities beyond the traditional software development lifecycle. Unlike software projects, after they were successfully delivered and deployed, they cannot be abandoned but must be continuously monitored if model performance still satisfies all requirements. We can always get new data with new statistical characteristics that can break our pipelines or influence model performance.
I am an instructor of the MLOps workshop for some anonymous startup incubation program where the objectives are (1) to orchestrate and deploy updates to the application and the deep learning model in a unified way. (2) To design a DevOps pipeline to coordinate retrieving the latest best model from the model registry, packaging the web application, deploying the web application and inferencing web service.
How to Utilize MLflow and Kubernetes to Build an Enterprise ML PlatformDatabricks
In large enterprises, large solutions are sometimes required to tackle even the smallest tasks and ML is no different. At Comcast we are building a comprehensive, configuration based, continuously integrated and deployed platform for data pipeline transformations, model development and deployment. This is accomplished using a range of tools and frameworks such as Databricks, MLflow, Apache Spark and others. With a Databricks environment used by hundreds of researchers and petabytes of data, scale is critical to Comcast, so making it all work together in a frictionless experience is a high priority. The platform consists of a number of components: an abstraction for data pipelines and transformation to allow our data scientists the freedom to combine the most appropriate algorithms from different frameworks , experiment tracking, project and model packaging using MLflow and model serving via the Kubeflow environment on Kubernetes. The architecture, progress and current state of the platform will be discussed as well as the challenges we had to overcome to make this platform work at Comcast scale. As a machine learning practitioner, you will gain knowledge in: an example of data pipeline abstraction; ways to package and track your ML project and experiments at scale; and how Comcast uses Kubeflow on Kubernetes to bring everything together.
Feature Store as a Data Foundation for Machine LearningProvectus
Looking to design and build a centralized, scalable Feature Store for your Data Science & Machine Learning teams to take advantage of? Come and learn from experts of Provectus and Amazon Web Services (AWS) how to!
Feature Store is a key component of the ML stack and data infrastructure, which enables feature engineering and management. By having a Feature Store, organizations can save massive amounts of resources, innovate faster, and drive ML processes at scale. In this webinar, you will learn how to build a Feature Store with a data mesh pattern and see how to achieve consistency between real-time and training features, to improve reproducibility with time-traveling for data.
Agenda
- Modern Data Lakes & Modern ML Infrastructure
- Existing and Emerging Architectural Shifts
- Feature Store: Overview and Reference Architecture
- AWS Perspective on Feature Store
Intended Audience
Technology executives & decision makers, manager-level tech roles, data architects & analysts, data engineers & data scientists, ML practitioners & ML engineers, and developers
Presenters
- Stepan Pushkarev, Chief Technology Officer, Provectus
- Gandhi Raketla, Senior Solutions Architect, AWS
- German Osin, Senior Solutions Architect, Provectus
Feel free to share this presentation with your colleagues and don't hesitate to reach out to us at info@provectus.com if you have any questions!
REQUEST WEBINAR: https://provectus.com/webinar-feature-store-as-data-foundation-for-ml-nov-2020/
Machine Learning operations brings data science to the world of devops. Data scientists create models on their workstations. MLOps adds automation, validation and monitoring to any environment including machine learning on kubernetes. In this session you hear about latest developments and see it in action.
Challenges of Operationalising Data Science in Productioniguazio
The presentation topic for this meet-up was covered in two sections without any breaks in-between
Section 1: Business Aspects (20 mins)
Speaker: Rasmi Mohapatra, Product Owner, Experian
https://www.linkedin.com/in/rasmi-m-428b3a46/
Once your data science application is in the production, there are many typical data science operational challenges experienced today - across business domains - we will cover a few challenges with example scenarios
Section 2: Tech Aspects (40 mins, slides & demo, Q&A )
Speaker: Santanu Dey, Solution Architect, Iguazio
https://www.linkedin.com/in/santanu/
In this part of the talk, we will cover how these operational challenges can be overcome e.g. automating data collection & preparation, making ML models portable & deploying in production, monitoring and scaling, etc.
with relevant demos.
Feature drift monitoring as a service for machine learning models at scaleNoriaki Tatsumi
In this talk, you’ll learn about techniques used to build a feature drift detection as a service capability for your enterprise and beyond. Feature drift monitoring is a way to check volatility of machine learning model inputs. It can trigger investigations for potential model degradation as well as explain why models have shifted.
KFServing, Model Monitoring with Apache Spark and a Feature StoreDatabricks
In recent years, MLOps has emerged to bring DevOps processes to the machine learning (ML) development process, aiming at more automation in the execution of repetitive tasks and at smoother interoperability between tools. Among the different stages in the ML lifecycle, model monitoring involves the supervision of model performance over time, involving the combination of techniques in four categories: outlier detection, data drift detection, explainability and adversarial attacks. Most existing model monitoring tools follow a scheduled batch processing approach or analyse model performance using isolated subsets of the inference data. However, for the continuous monitoring of models, stream processing platforms show several advantages, including support for continuous data analytics, scalable processing of large amounts of data and first-class support for window-based aggregations useful for concept drift detection.
In this talk, we present an open-source platform for serving and monitoring models at scale based on Kubeflow’s model serving framework, KFServing, the Hopsworks Online Feature Store for enriching feature vectors with transformer in KFServing, and Spark and Spark Streaming as general purpose frameworks for monitoring models in production.
We also show how Spark Streaming can use the Hopsworks Feature Store to implement continuous data drift detection, where the Feature Store provides statistics on the distribution of feature values in training, and Spark Streaming computes the statistics on live traffic to the model, alerting if the live traffic differs significantly from the training data. We will include a live demonstration of the platform in action.
Observe and command your fleets across any kubernetes with weave git opsWeaveworks
Modern day deployments can often resemble the chaos of navigating the high seas with poor visibility and the dangers of unexpected events. Dev and test environments, running test data sets and feature flags in the public cloud, and production being served from a self-managed site that securely hosts client data can all be a challenge without full observability and control.
In this webinar, we show how you can reliably expand your Kubernetes footprint with Weave GitOps. Confidently observe and control your fleets, all from a single pane of glass across any environment.
Join this webinar to learn how to:
Control the health and propagation of customized clusters
Easily assign and secure clusters across multiple teams for multiple purposes
Observe all actions across all environments all from within Git
Understand managing all deployments across your cluster and fleets
Automated Provisioning, Management & Cost Control for Kubernetes ClustersWeaveworks
In today’s economic climate, IT departments are feeling the pressure to reduce costs which can have a significant effect on development teams, and more specifically, Kubernetes strategies. For many organizations, there is a good chance that many Kubernetes resources are overprovisioned, and it’s often difficult to visualize which processes are responsible for this unnecessary spend.
Weaveworks has joined forces with KubeCost to show you how to “do more with less” by easily integrating a Kubernetes FinOps solution into your existing workflows and seamlessly automating the provisioning and management of FinOps enabled Kubernetes clusters from a single UI / dashboard.
Join this webinar to discover best practices for monitoring and reducing Kubernetes spend, while balancing cost, performance, and reliability.
What you’ll learn:
- Best practices for implementing a FinOps strategy in your organization.
- Cluster management and templating capabilities using Weave GitOps for automating FinOps.
- How to use predefined, automated policies for reliable cost control across your Kubernetes environment.
Managing and Versioning Machine Learning Models in PythonSimon Frid
Practical machine learning is becoming messy, and while there are lots of algorithms, there is still a lot of infrastructure needed to manage and organize the models and datasets. Estimators and Django-Estimators are two python packages that can help version data sets and models, for deployment and effective workflow.
Tutorial for Machine Learning 101 (an all-day tutorial at Strata + Hadoop World, New York City, 2015)
The course is designed to introduce machine learning via real applications like building a recommender image analysis using deep learning.
In this talk we cover deployment of machine learning models.
Managers guide to effective building of machine learning productsGianmario Spacagna
Part 1/2 (Managers)
Data and Machine Learning (ML) technologies are now widespread and adopted by literally all industries. Although recent advancements in the field have reached an unthinkable level of maturity, many organizations still struggle with turning these advances into tangible profits. Unfortunately, many ML projects get stuck in a proof-of-concept stage without ever reaching customers and generating revenue. In order to effectively adopt ML technologies, enterprises need to build the right business cases as well as to be ready to face the inevitable challenges. In this talk, we will share common pitfalls, lessons learned, and best practices, while building different enterprise products. In particular, we will focus on the generic use case of ML as the core technology enabling customer-facing products regardless of the specific industry or application.
You will:
Understand if ML is the right solution for your business and set the right expectations;
Deal with the additional uncertainty of ML projects with respect to traditional software;
Build a balanced ML team and cover the broad spectrum of skills;
Know how to apply the scientific workflow in an agile development framework;
Learn how to turn research into production systems including engineering practices and tools;
Be able to leverage modern cloud and serverless architecture for scalable, autonomous and cheaper deployments.
Deploying and managing machine learning models at scale introduces new complexities. Fortunately, there are tools that simplify this process. In this talk we walk you through an end-to-end hands on example showing how you can go from research to production without much complexity by leveraging the Seldon Core and MLflow frameworks. We will train a set of ML models, and we will showcase a simple way to deploy them to a Kubernetes cluster through sophisticated deployment methods, including canary deployments, shadow deployments and we’ll touch upon richer ML graphs such as explainer deployments.
Developing ML-enabled Data Pipelines on Databricks using IDE & CI/CD at Runta...Databricks
Data & ML projects bring many new complexities beyond the traditional software development lifecycle. Unlike software projects, after they were successfully delivered and deployed, they cannot be abandoned but must be continuously monitored if model performance still satisfies all requirements. We can always get new data with new statistical characteristics that can break our pipelines or influence model performance.
I am an instructor of the MLOps workshop for some anonymous startup incubation program where the objectives are (1) to orchestrate and deploy updates to the application and the deep learning model in a unified way. (2) To design a DevOps pipeline to coordinate retrieving the latest best model from the model registry, packaging the web application, deploying the web application and inferencing web service.
How to Utilize MLflow and Kubernetes to Build an Enterprise ML PlatformDatabricks
In large enterprises, large solutions are sometimes required to tackle even the smallest tasks and ML is no different. At Comcast we are building a comprehensive, configuration based, continuously integrated and deployed platform for data pipeline transformations, model development and deployment. This is accomplished using a range of tools and frameworks such as Databricks, MLflow, Apache Spark and others. With a Databricks environment used by hundreds of researchers and petabytes of data, scale is critical to Comcast, so making it all work together in a frictionless experience is a high priority. The platform consists of a number of components: an abstraction for data pipelines and transformation to allow our data scientists the freedom to combine the most appropriate algorithms from different frameworks , experiment tracking, project and model packaging using MLflow and model serving via the Kubeflow environment on Kubernetes. The architecture, progress and current state of the platform will be discussed as well as the challenges we had to overcome to make this platform work at Comcast scale. As a machine learning practitioner, you will gain knowledge in: an example of data pipeline abstraction; ways to package and track your ML project and experiments at scale; and how Comcast uses Kubeflow on Kubernetes to bring everything together.
Feature Store as a Data Foundation for Machine LearningProvectus
Looking to design and build a centralized, scalable Feature Store for your Data Science & Machine Learning teams to take advantage of? Come and learn from experts of Provectus and Amazon Web Services (AWS) how to!
Feature Store is a key component of the ML stack and data infrastructure, which enables feature engineering and management. By having a Feature Store, organizations can save massive amounts of resources, innovate faster, and drive ML processes at scale. In this webinar, you will learn how to build a Feature Store with a data mesh pattern and see how to achieve consistency between real-time and training features, to improve reproducibility with time-traveling for data.
Agenda
- Modern Data Lakes & Modern ML Infrastructure
- Existing and Emerging Architectural Shifts
- Feature Store: Overview and Reference Architecture
- AWS Perspective on Feature Store
Intended Audience
Technology executives & decision makers, manager-level tech roles, data architects & analysts, data engineers & data scientists, ML practitioners & ML engineers, and developers
Presenters
- Stepan Pushkarev, Chief Technology Officer, Provectus
- Gandhi Raketla, Senior Solutions Architect, AWS
- German Osin, Senior Solutions Architect, Provectus
Feel free to share this presentation with your colleagues and don't hesitate to reach out to us at info@provectus.com if you have any questions!
REQUEST WEBINAR: https://provectus.com/webinar-feature-store-as-data-foundation-for-ml-nov-2020/
Machine Learning operations brings data science to the world of devops. Data scientists create models on their workstations. MLOps adds automation, validation and monitoring to any environment including machine learning on kubernetes. In this session you hear about latest developments and see it in action.
Challenges of Operationalising Data Science in Productioniguazio
The presentation topic for this meet-up was covered in two sections without any breaks in-between
Section 1: Business Aspects (20 mins)
Speaker: Rasmi Mohapatra, Product Owner, Experian
https://www.linkedin.com/in/rasmi-m-428b3a46/
Once your data science application is in the production, there are many typical data science operational challenges experienced today - across business domains - we will cover a few challenges with example scenarios
Section 2: Tech Aspects (40 mins, slides & demo, Q&A )
Speaker: Santanu Dey, Solution Architect, Iguazio
https://www.linkedin.com/in/santanu/
In this part of the talk, we will cover how these operational challenges can be overcome e.g. automating data collection & preparation, making ML models portable & deploying in production, monitoring and scaling, etc.
with relevant demos.
Feature drift monitoring as a service for machine learning models at scaleNoriaki Tatsumi
In this talk, you’ll learn about techniques used to build a feature drift detection as a service capability for your enterprise and beyond. Feature drift monitoring is a way to check volatility of machine learning model inputs. It can trigger investigations for potential model degradation as well as explain why models have shifted.
KFServing, Model Monitoring with Apache Spark and a Feature StoreDatabricks
In recent years, MLOps has emerged to bring DevOps processes to the machine learning (ML) development process, aiming at more automation in the execution of repetitive tasks and at smoother interoperability between tools. Among the different stages in the ML lifecycle, model monitoring involves the supervision of model performance over time, involving the combination of techniques in four categories: outlier detection, data drift detection, explainability and adversarial attacks. Most existing model monitoring tools follow a scheduled batch processing approach or analyse model performance using isolated subsets of the inference data. However, for the continuous monitoring of models, stream processing platforms show several advantages, including support for continuous data analytics, scalable processing of large amounts of data and first-class support for window-based aggregations useful for concept drift detection.
In this talk, we present an open-source platform for serving and monitoring models at scale based on Kubeflow’s model serving framework, KFServing, the Hopsworks Online Feature Store for enriching feature vectors with transformer in KFServing, and Spark and Spark Streaming as general purpose frameworks for monitoring models in production.
We also show how Spark Streaming can use the Hopsworks Feature Store to implement continuous data drift detection, where the Feature Store provides statistics on the distribution of feature values in training, and Spark Streaming computes the statistics on live traffic to the model, alerting if the live traffic differs significantly from the training data. We will include a live demonstration of the platform in action.
Observe and command your fleets across any kubernetes with weave git opsWeaveworks
Modern day deployments can often resemble the chaos of navigating the high seas with poor visibility and the dangers of unexpected events. Dev and test environments, running test data sets and feature flags in the public cloud, and production being served from a self-managed site that securely hosts client data can all be a challenge without full observability and control.
In this webinar, we show how you can reliably expand your Kubernetes footprint with Weave GitOps. Confidently observe and control your fleets, all from a single pane of glass across any environment.
Join this webinar to learn how to:
Control the health and propagation of customized clusters
Easily assign and secure clusters across multiple teams for multiple purposes
Observe all actions across all environments all from within Git
Understand managing all deployments across your cluster and fleets
Automated Provisioning, Management & Cost Control for Kubernetes ClustersWeaveworks
In today’s economic climate, IT departments are feeling the pressure to reduce costs which can have a significant effect on development teams, and more specifically, Kubernetes strategies. For many organizations, there is a good chance that many Kubernetes resources are overprovisioned, and it’s often difficult to visualize which processes are responsible for this unnecessary spend.
Weaveworks has joined forces with KubeCost to show you how to “do more with less” by easily integrating a Kubernetes FinOps solution into your existing workflows and seamlessly automating the provisioning and management of FinOps enabled Kubernetes clusters from a single UI / dashboard.
Join this webinar to discover best practices for monitoring and reducing Kubernetes spend, while balancing cost, performance, and reliability.
What you’ll learn:
- Best practices for implementing a FinOps strategy in your organization.
- Cluster management and templating capabilities using Weave GitOps for automating FinOps.
- How to use predefined, automated policies for reliable cost control across your Kubernetes environment.
Webinar: Capabilities, Confidence and Community – What Flux GA Means for YouWeaveworks
Flux, the original GitOps project, began its development in a small London office back in 2017 with the goal to bring continuous delivery (CD) to developers, platform and cluster operators working with Kubernetes. From donating the project to the CNCF, its continued growth within the cloud native community, to its achievement of passing rigorous battle tests for security, longevity and governance, it’s little wonder that Flux v2 has reached yet another celebratory milestone – General Availability (GA).
Flux is the GitOps platform of choice for many enterprise companies such as SAP, Volvo Cars, and Axel Springer; and is embedded within AKS, Azure Arc and EKS Anywhere. It provides extensive automation to CI/CD, security and audit trails, and reliability through canary deployments and rollback capabilities.
Join this webinar by Flux maintainers and creators and discover:
* Latest release features and roadmap for the future.
* Interesting use cases for Flux (e.g security).
* Flux capabilities you may not be aware of (e.g. extensions).
* Joining the vibrant Flux community.
* How to leverage Flux in a supported enterprise environment today.
Intro to GitOps with Weave GitOps, Flagger and LinkerdWeaveworks
You may not think of "GitOps" and "service mesh" together – but maybe you should! These two wildly different technologies are each enormously capable independently, and combined they deliver far more than the sum of their parts: a single Git commit can control workflows customized for your exact situation by taking advantage of the service mesh's ability to measure and manipulate traffic anywhere in your application's call graph, and you can rest easy knowing that Git is preserving the complete configuration for your entire application every step of the way.
See how these technologies can work together to tackle complex problems in cloud-native applications.
What you’ll get out of this:
* Understand what GitOps and service meshes can - and can't - do for you.
* Understand basic operations with GitOps and Linkerd.
* Understand the basics of continuous deployment with Weave GitOps and Linkerd.
Migrating from Self-Managed Kubernetes on EC2 to a GitOps Enabled EKSWeaveworks
Did your company start down the path of building a cloud native platform using Kubernetes with the goal of enabling developers to innovate faster and increase productivity, but then run into challenges keeping it operating in an optimal way?
In this session, Weaveworks will discuss how to migrate from self-managed Kubernetes on EC2 to a GitOps managed Shared Services Platform (SSP) on EKS. A SSP built on EKS and managed with Weave GitOps provides developers and operators with common workflows to update both applications and infrastructure. With every change in version control, full audit trails are available, and security is enforced. While at the same time enabling easier rollbacks and faster mean-time-to-recovery (MTTR). In short, a Weave GitOps managed SSP increases developer velocity while boosting stability.
How to operate a hybrid Kubernetes architecture, using managed EKS in the AWS Cloud and EKS-Distro on premises.
How to structure your infrastructure repository to efficiently manage multiple teams.
How to use Kubernetes RBAC to provide secure cluster multi-tenancy.
How to use GitOps to promote releases across a hybrid set of independent clusters.
How to accomplish data and operational sovereignty.
Jordi Mon Companys presents an overview of Weave GitOps Core for the Free GitOps Workshop on August 19, 2021.
Weave GitOps Core is a continuous delivery product to run apps in any Kubernetes. It is free and open source, and you can get started today!
https://www.weave.works/product/gitops-core/
Chat with us on our Slack channel! #weave-gitops http://bit.ly/WeaveGitOpsSlack
If you need to invite yourself to the Slack, visit https://slack.weave.works/
Deploying secure, cloud native stateful applications requires a high level of performance across hybrid and multi-cloud environments.
Using the scalable, highly performant storage provided by Ondat in combination with Weave GitOps Trusted Delivery, you can shift left security and accelerate software development.
Watch this on-demand webinar as we demonstrate how:
- All changes to application configuration are managed through Git workflows
GitOps provides an extra layer of security by removing the need for direct access to Kubernetes clusters.
- Policy-as-Code guarantees security, resilience and coding standards compliance.
- To dynamically provision highly available persistent volumes by simply deploying Ondat anywhere with a simple operator profile.
- All data services such as replication, compression and encryption, are optimized and accelerated to scale on any platform with Ondat’s low latency data plane.
Shift Deployment Security Left with Weave GitOps & Upbound’s Universal Crossp...Weaveworks
In this session, we’ve partnered with Upbound to showcase how to effectively manage application delivery while maintaining a high level of security using Weave GitOps and Upbound. Managing a stateful application deployment with a relational database, Weave GitOps can recognize if there is a policy violation and correct it before deploying the application.
Join us as we demonstrate the scenarios where:
All changes to application configuration are managed through Git workflows
Upbound’s Universal Crossplane allows you to build, deploy, and manage your cloud platforms
GitOps provides an extra layer of security by removing the need for direct access to Kubernetes clusters
Policy-as-Code guarantees security, resilience and coding standards compliance
Watch the recording: xx
Join this info-packed and hands-on workshop where we will cover:
Introduction to Kubernetes & GitOps talk:
We'll cover the most popular path that has brought success to many users already - GitOps as a natural evolution of Kubernetes. We'll give an overview of how you can benefit from Kubernetes and GitOps: greater security, reliability, velocity and more. Importantly, we cover definitions and principles standardized by the CNCF's OpenGitOps group and what it means for you.
Get Started with GitOps:
You'll have GitOps up and running in about 30 mins using our free and open source tools! We'll give a brief vision of where you want to be with those security, reliability, and velocity benefits, and then we'll support you while go through the getting started steps. During the workshop, you'll also experience in action and see demos for:
* an opinionated repo structure to minimize decision fatigue
* disaster recovery using GitOps
* Helm charts example
* Multi-cluster example
* all with free and open source tools mostly in the CNCF (eg. Flux and Helm).
If you have questions before or after the workshop, talk to us at #weave-gitops http://bit.ly/WeaveGitOpsSlack (If you need to invite yourself to the Slack, visit https://slack.weave.works/)
In this webinar we will be discussing how Orange Business Services, a global IT and communications services provider, and its large scale distributed cloud and edge network can achieve sovereignty with the hybrid EKS and Weave GitOps shared services platform.
Topics we are covering:
How EKSD (EKS on premise) and EKS (AWS managed Kubernetes) is used to establish common workflows that minimize operational overhead
How to lower operational costs with the use of ephemeral cloud environments for development and testing
How to achieve operational Sovereignty by enabling the operation of the shared services platform in on premise, air gapped and non-tethered configurations
Hybrid and Multi-Cloud Strategies for Kubernetes with GitOpsSonja Schweigert
One of the biggest advantages Kubernetes has to offer is that it is agnostic to infrastructure and capable of managing diverse workloads running on different compute resources. This allows organizations to manage multiple developer platforms, who can operate across many environments such as on premise, hybrid and multiple clouds.
Streamlined processes and automation is pivotal for operations when managing clusters at scale and maintaining security and policy checks. Paul Curtis, Principal Solutions Architect will demonstrate GitOps and Weave Kubernetes Platform in a hybrid and multi-cloud setup.
Learn how to:
Use model-driven automation to increases reliability and stability across environments
Simplify multi-cluster management with GitOps
Enable developers to push code to production daily (self-service)
Improve utilization and capacity management through Kubernetes platforms on cloud and on-premise infrastructure
Hybrid and Multi-Cloud Strategies for Kubernetes with GitOpsWeaveworks
One of the biggest advantages Kubernetes has to offer is that it is agnostic to infrastructure and capable of managing diverse workloads running on different compute resources. This allows organizations to manage multiple developer platforms, who can operate across many environments such as on premise, hybrid and multiple clouds.
Streamlined processes and automation is pivotal for operations when managing clusters at scale and maintaining security and policy checks. Paul Curtis, Principal Solutions Architect will demonstrate GitOps and Weave Kubernetes Platform in a hybrid and multi-cloud setup.
Learn how to:
Use model-driven automation to increases reliability and stability across environments
Simplify multi-cluster management with GitOps
Enable developers to push code to production daily (self-service)
Improve utilization and capacity management through Kubernetes platforms on cloud and on-premise infrastructure
Free GitOps Workshop (with Intro to Kubernetes & GitOps)Weaveworks
View this video on Youtube here: https://youtu.be/tK4S8y3j5TA
In this info-packed and hands-on workshop we covered:
Introduction to Kubernetes & GitOps talk:
We covered the most popular path that has brought success to many users already - GitOps as a natural evolution of Kubernetes. We'll give an overview of how you can benefit from Kubernetes and GitOps: greater security, reliability, velocity and more. Importantly, we cover definitions and principles standardized by the CNCF's OpenGitOps group and what it means for you.
Get Started with GitOps:
You'll have GitOps up and running in about 30 mins using our free and open source tools! We'll give a brief vision of where you want to be with those security, reliability, and velocity benefits, and then we'll support you while go through the getting started steps. During the workshop, you'll also experience in action and see demos for:
- an opinionated repo structure to minimize decision fatigue
- disaster recovery using GitOps
- Helm charts example
- Multi-cluster example
- all with free and open source tools mostly in the CNCF (eg. Flux and Helm).
If you have questions before or after the workshop, talk to us at #weave-gitops http://bit.ly/WeaveGitOpsSlack (If you need to invite yourself to the Slack, visit https://slack.weave.works/)
Mindtree provides devops service that builds continuous delivery capabilities with tool choices through a DevSecOps maturity assessment framework. Click here to know more.
GitOps, Driving NGN Operations Teams 211127 #kcdgt 2021William Caban
The adoption of cloud-native principles brings new challenges. Scaling and evolving operations teams and staying up to date requires the adoption of new operational models and paradigms.
This deck presents how modern paradigms map to GitOps principles and the charactersitics that must be supported by any software used for GitOps.
Docker Enterprise Edition Overview by Steven Thwaites, Technical Solutions En...Ashnikbiz
This was presented by Steven Thwaites, Technical Solutions Engineer at Docker at Cloud Expo Asia. Docker is the only Containers-as-a-Service platform for IT that manages and secures diverse applications across disparate infrastructure, both on-premises and in the cloud. It covers topics like:
VMs vs Containers
The Docker Ecosystem
How to Build and Ship your Docker Image
Unique Advantages with Docker EE and more
Similar to Weave GitOps - continuous delivery for any Kubernetes (20)
Weave AI Controllers (Weave GitOps Office Hours)Weaveworks
LLMs are one of the rising workloads on Kubernetes and so are the complexities of deploying, managing and fine-tuning them. With this latest extension we can offer a strong blueprint for enterprises on how to keep LLMs OCI contained with the use of Kubernetes, Flux and Weave AI Controllers.
The Highlights:
* Simplified deployment, management, and fine-tuning of LLMs on any Kubernetes infrastructure.
* Strong security and governance ensured through GitOps workflows and a robust signing and verification process.
The Whys:
* Security, Governance & Compliance: Ensures vulnerability-free and compliant deployments.
* Seamless Integration: Works with existing systems, including Red Hat OpenShift.
* GitOps for Productivity & Collaboration: Leverages the power of Flux and Kubernetes for automated, streamlined workflows.
The Weave AI Controllers are an out of the box extension for Flux and are shipped and supported with Weave GitOps Assured (https://www.weave.works/product/gitops) and Enterprise (https://www.weave.works/product/gitops-enterprise/).
Read our latest blog for more information (https://www.weave.works/blog/weave-ai-controllers) and visit GitHub to get started - https://github.com/weave-ai/weave-ai
Flamingo: Expand ArgoCD with Flux (Office Hours)Weaveworks
Flamingo is an open source tool that allows for integrated use of both Flux and ArgoCD, the two leading GitOps solutions available today.
* See how to integrate the two most used CNCF projects together to create flexible and extensible GitOps solutions.
* Learn how to use Flux’s powerful and secure controllers with ArgoCD’s web-based GUI.
* Understand how Flamingo provides a path towards Platform Engineering for ArgoCD users.
* Explore extending ArgoCD to manage Infrastructure as Code through Flux’s Terraform Controller.
For more information visit: https://github.com/flux-subsystem-argo/flamingo
Although not an entirely new concept, Platform Engineering and Internal Developer Platforms (IDPs) are all the rage due to their potential to increase development velocity and deployment frequency while boosting reliability and security.
Join Joe Dahlquist, VP of PMM and Mohamed Ahmed, VP of Developer Platforms at Weaveworks to learn the 6 tell-tale signs your company should implement a platform engineering approach. The webinar draws on hundreds of conversations with SRE’s, developers, and platform engineering teams to help you better understand what works, what doesn’t and what might be missing from your strategy. Attendees can apply these learnings to their first (or next) developer platform regardless of your build vs. buy journey.
You will learn:
* The difference between Internal Developer Platforms and Platform Engineering
* Why platform engineering now?
* How Dev and Ops benefit from an IDP
* 6 tell-tale signs to start platform engineering
* Drafting your platform engineering strategy - where to begin and what to avoid
SRE and GitOps for Building Robust Kubernetes Platforms.pdfWeaveworks
In today's technology-driven landscape, ensuring the reliability and stability of systems is critical for organizations to deliver exceptional user experiences. Site Reliability Engineering (SRE) has emerged as a proven methodology to achieve operational excellence and elevate performance.
By combining SRE and GitOps, organizations can leverage the benefits of both methodologies. GitOps provides a reliable and auditable approach to managing infrastructure and application changes, ensuring that all deployments are version-controlled and consistent across environments. This aligns with the SRE principle of implementing standardized and automated processes for maintaining system reliability.
Join our live webinar as we introduce the fundamentals and significance of SRE and GitOps, and provide actionable strategies for implementation. We’ll also explore the features of Weave GitOps that integrate SRE and GitOps practices to streamline workflows to support system reliability and stability.
You will learn:
An overview and correlation of key SRE and GitOps best practices
The 5 keys DORA metrics for measuring performance of software delivery.
How to leverage continuous delivery and progressive delivery to enhance application stability.
How Weave GitOps can reliably simplify the management of infrastructure and applications, with real-world customer examples illustrating their impact.
Webinar: End to End Security & Operations with Chainguard and Weave GitOpsWeaveworks
One of the key values of GitOps relies on its fully declarative single source of truth in Git for the desired state of your entire system – configuration that continuously reconciles with the runtime of the system.
Validating committer identity in your Git repository is a critical component towards a secure GitOps solution. Although basic capabilities are provided by Git service providers, more granular controls for governance and compliance are a requirement to satisfy most enterprise grade implementations.
How do you keep that end to end process secure, from Git to Runtime?
Join Weaveworks and Chainguard for a live webinar where we will look at how Chainguard Enforce for Git together with Weave GitOps Enterprise Policy Engine allows you to secure your end to end GitOps workflows, from Git to Runtime.
You will learn how to:
- Use Chainguard Enforce for Git to ensure only authorized GitOps tooling can modify your desired state.
- Provide a secure identity to Weave GitOps Enterprise for all Git operations.
- Use Weave GitOps Policy Engine to guarantee compliance on admission.
Flux Beyond Git Harnessing the Power of OCIWeaveworks
Watch the recap: https://youtu.be/gKR95Kmc5ac
In this KubeCon Europe 2023 session, Stefan and Hidde will talk about the latest developments of Flux around the Open Container Initiative (OCI). The focus will be on how OCI can serve as the single source of truth for both application code (container images) and configuration (OCI artifacts). We will start by explaining how Flux can be used as a package manager for distributing Kubernetes configs and Terraform modules as OCI artifacts. Afterwards, we will demonstrate how to build a secure delivery pipeline that leverages Flux integrations with GitHub Actions and keyless signatures from Sigstore Cosign. Lastly, we will touch upon the upcoming plans for 2023 and the significance of OCI in the future of continuous delivery with Flux.
How to Avoid Kubernetes Multi-tenancy CatastrophesWeaveworks
Picture this… It’s the middle of the night on a Saturday, and the sound of slack messages rolling in rouses you from slumber. Then two text messages chime in quick succession. As you grab your phone and pry open an eye to figure out WTF, the phone rings - and it’s your boss!? You stammer out a “Hello?”
She sounds alarmed. “Wake up, we have a big problem”
“It’s two-in-the-morning, what problem?” you croak back.
“I guess you missed the alerts while you were sleeping…API endpoints in prod are getting knocked over, and the tokens responsible are yours.”
“They’re what? How?”
“Get to your machine and jump on the meeting link I just sent - everybody’s waiting”
Yikes. Join Weaveworks for some real-world tales from the trenches, and learn about the 5 simple things you can do to prevent making a royal mess of Tenancy in Kubernetes. Hear from developers that got that late night call because of a bone-headed accident, and teams affected by gob-smacking access and permissions foul-ups. Luckily for us, they were happy to tell us the tales so we can learn from their pain.
Weave GitOps Workspaces is a new feature that enables multi-tenancy so platform engineers can scale their GitOps workflows across numerous development teams. Oh yeah, it also wards -off wake-up calls in the middle of the night, which is nice.
Watch this webinar recording to learn:
- How Weave GitOps simplifies tenancy management
- How security guardrails keep you from blowing a hole in your app, and across your team
- 5 takeaways for enabling Kubernetes tenancy safely and effectively for your teams
Building internal developer platform with EKS and GitOpsWeaveworks
An internal developer platform (IDP) is a set of standardized tools and technologies that enables development teams to self-service, offering convenient access to resources they need to create and deploy compliant code. The ultimate goal is to facilitate automation, autonomy and productivity across large teams. However, creating an IDP is highly complex, especially when bridging hybrid scenarios. In fact, build timelines can take anywhere between one to two years!
In this Techstrong Learning Experience, we will discuss how platform engineers can more efficiently build an IDP with Amazon EKS and Weave GitOps and accelerate cloud-native adoption while speeding up migration of existing applications to the cloud.
Our experts will also introduce EKS Blueprints, a collection of infrastructure-as-code (IaC) modules like Terraform and AWS Cloud Development Kit (AWS CDK) that will help you configure and deploy consistent EKS clusters across on-premises and cloud.
Key Takeaways:
- Why you should build a self-service IDP
- How to leverage EKS, GitOps and EKS Blueprints to build your IDP
- A review of use cases and benefits of an IDP
GitOps Testing in Kubernetes with Flux and Testkube.pdfWeaveworks
GitOps is amazing... until you can't apply it! This has been the case mostly for testing where it continues to be more of a push than a pull in organizations' DevOps pipelines.
Join us in this talk to learn the benefits of improving your existing testing pipeline with Testkube, an open source project that brings tests inside your Kubernetes cluster, and FluxCD adding the GitOps sprinkles to testing!
Speaker: Abdallah Abedraba, Product Leader at Testkube
Abdallah works at Testkube, a Kubernetes native testing framework. In his prior experiences, he has tried everything from software engineering to product management, and now working as a Developer Advocate, on open source (a dream of his!) evangelizing all things Testing and Kubernetes. In his free time, he enjoys attending developer conferences and meetups, as well as spending time at the movies and actively listening to music.
Implementing Flux for Scale with Soft Multi-tenancyWeaveworks
Soft multi-tenancy can be hard to achieve and secure. Multiple tenants sharing the same cluster means there are global objects, like Custom Resource Definitions (CRDs), namespaces, and so on, that you don’t want tenants controlling. Platform admins, cluster admins, and tenants, should be separated, with dedicated namespaces, role bindings, node groups, taints and tolerations, etc.
With Flux, tenant isolation is enforced by default, so you don’t have to worry about accidental tenant cross-over / cross-contamination.
In this session, Priyanka “Pinky” Ravi, Developer Experience Engineer at Weaveworks, will walk you through how to set up multi-tenancy on an existing Kubernetes cluster and manage several tenants within the cluster.
Take advantage of the benefits that come with infrastructure as code.
Accelerating Hybrid Multistage Delivery with Weave GitOps on EKSWeaveworks
Join Leo Murillo, Principal Solutions Architect at Weaveworks and Rama Ponnuswami, Sr. Container Specialist at AWS, as they walk through accelerating Multi-stage delivery on GitOps. If you already have EKS-A, you are ready to automate the release of multistage delivery. Thus, allowing you to deploy more often and reliably with less overhead.
In this Webinar, we cover:
- Best practices for CI/CD, GitOps and Application Pipeline Management.
- Simple cluster management across Kubernetes hybrid infrastructure.
- Multistage deployments using Weave GitOps for EKS and EKS-A using a single UI dashboard.
Securing Your App Deployments with Tunnels, OIDC, RBAC, and Progressive Deliv...Weaveworks
In a joint webinar with Traefik Labs, we show how Traefik Hub, a SaaS-based cloud native networking platform, helps you publish your containers securely in seconds with tunnels, OIDC authentication and automated TLS certificate management. And, how you can combine that with Weave GitOps to achieve continuous application delivery using progressive delivery strategies for risk-free and reliable deployments.
Security is key, so we showcase multi-tenancy for full RBAC across the different deployment stages, and trusted delivery best practices for continuous security and compliance baked in.
Learn how:
- To utilize canary deployments for reliable and risk-free application deployments.
- GitOps lets you automate and secure the publishing of containers at the edge consistently.
- Easy it is to deploy, update and manage your application workloads on Kubernetes.
- To publish containers securely using tunnels, OIDC authentication and TLS certificate management.
Flux’s Security & Scalability with OCI & Helm Slides.pdfWeaveworks
During this session Kingdon Barrett, OSS Engineer at Weaveworks & Flux Maintainer, will show you how to quickly create scalable and Cosign-verified GitOps configurations with Flux using the same process with two demo environments: one will be a Kustomize Environment and the other a Helm-based environment.
Flux Security & Scalability using VS Code GitOps Extension Weaveworks
Recently Flux has released two new features (OCI and Cosign) for scalable and secure GitOps. Juozas Gaigalas, a Developer Experience Engineer at Weaveworks, will demonstrate how developers and platform engineers can quickly create scalable and Cosign-verified GitOps configurations using VS Code GitOps Tools extension. New and experienced Flux users can learn about Flux’s OCI and Cosign support through this demo.
Robust Network Security and Observability with GitOps and CiliumWeaveworks
While GitOps is known as a paradigm for managing cloud native applications, not many know it fits within platform management as well. Automating the provisioning and management of Kubernetes clusters abstracts away the issue of inconsistency that you get with cluster sprawl, all while shortening provisioning time by consistent automation.
But that’s not enough. A networking layer is a standard requirement when managing Kubernetes environments, yet traditional IT networking and security methods do not work. By default, Kubernetes environments allow any pod to connect to any other pod, creating security risks. Furthermore, legacy approaches to network security visibility do not allow for performance of threat detection, compliance monitoring, or incident investigations for Kubernetes workloads. Cilium is a zero-trust cloud-native networking layer providing the necessary security and observability of your Kubernetes environments.
What if you were to add your network and security operations into your GitOps workflows?
In our webinar with Isovalent, we walk through how to easily add Cilium as a robust Container Network Interface solution using GitOps, and explore some of the Observability and Security features it provides.
You'll learn how:
- GitOps helps you manage cloud native chaos
- To save time creating secure, “user-ready” Kubernetes clusters
- To apply Weave GitOps to Kubernetes platform management
- To improve network security and network observability using Cilium
→ Intro to Gitops & Flux
→ How to bootstrap Flux on a Kubernetes Cluster
→ How to deploy a sample application using Flux, and customised application configuration through Kustomize patches.
→ An overview of new things that you can do with Flux
Weave GitOps 2022.09 Release: A Fast & Reliable Path to Production with Progr...Weaveworks
Weave GitOps 2022.09 Features Launch Event
The latest release of Weave GitOps introduces new features enabling progressive delivery, policy as code, and accelerated application onboarding.
Weave GitOps is the leading full-stack GitOps platform to automate trusted application delivery and secure infrastructure operations on premise, in the cloud and at the edge. Trusted by Customers, including Deutsche Telekom and The Department of Defense, Platform and Application Teams, Weave GitOps unlocks the benefits of increased efficiency and compliance, while boosting deployment velocity and confidence.
Join us where we’ll do a live demo of Weave GitOps showcasing:
- Advanced Deployment Patterns—Progressive Delivery has never been easier
- Multi-tenancy and Application Portability—More collaboration and control
- Strengthened GitOps Security—If you can code it, you can secure it.
Building a Security First Approach Across Hybrid Cloud with GitOps and Policy...Weaveworks
In this webinar, Darren Madams, Weaveworks Solution Architect and Steve Waterworth, Weaveworks Technical Marketing Manager demonstrate how to shift security best practices further left. They’ll walk through a practical example of how Weave GitOps helped a financial services organization move to a hybrid cloud environment for fully automated deployment and cluster provisioning that met their strict security, governance and compliance requirements.
Learn:
- The need for deploying clusters in on-premise environments because of compliance requirements such as PCI-DSS
- How to shift from manual to automated cluster provisioning with policy and security checks in place
- How to seamlessly expand automated processes across environments using Weave GitOps
- How Weave GitOps features 100+ policies out-of-the box for shifting security further left in your SDLC
Enhancing Project Management Efficiency_ Leveraging AI Tools like ChatGPT.pdfJay Das
With the advent of artificial intelligence or AI tools, project management processes are undergoing a transformative shift. By using tools like ChatGPT, and Bard organizations can empower their leaders and managers to plan, execute, and monitor projects more effectively.
First Steps with Globus Compute Multi-User EndpointsGlobus
In this presentation we will share our experiences around getting started with the Globus Compute multi-user endpoint. Working with the Pharmacology group at the University of Auckland, we have previously written an application using Globus Compute that can offload computationally expensive steps in the researcher's workflows, which they wish to manage from their familiar Windows environments, onto the NeSI (New Zealand eScience Infrastructure) cluster. Some of the challenges we have encountered were that each researcher had to set up and manage their own single-user globus compute endpoint and that the workloads had varying resource requirements (CPUs, memory and wall time) between different runs. We hope that the multi-user endpoint will help to address these challenges and share an update on our progress here.
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.
Enhancing Research Orchestration Capabilities at ORNL.pdfGlobus
Cross-facility research orchestration comes with ever-changing constraints regarding the availability and suitability of various compute and data resources. In short, a flexible data and processing fabric is needed to enable the dynamic redirection of data and compute tasks throughout the lifecycle of an experiment. In this talk, we illustrate how we easily leveraged Globus services to instrument the ACE research testbed at the Oak Ridge Leadership Computing Facility with flexible data and task orchestration capabilities.
Exploring Innovations in Data Repository Solutions - Insights from the U.S. G...Globus
The U.S. Geological Survey (USGS) has made substantial investments in meeting evolving scientific, technical, and policy driven demands on storing, managing, and delivering data. As these demands continue to grow in complexity and scale, the USGS must continue to explore innovative solutions to improve its management, curation, sharing, delivering, and preservation approaches for large-scale research data. Supporting these needs, the USGS has partnered with the University of Chicago-Globus to research and develop advanced repository components and workflows leveraging its current investment in Globus. The primary outcome of this partnership includes the development of a prototype enterprise repository, driven by USGS Data Release requirements, through exploration and implementation of the entire suite of the Globus platform offerings, including Globus Flow, Globus Auth, Globus Transfer, and Globus Search. This presentation will provide insights into this research partnership, introduce the unique requirements and challenges being addressed and provide relevant project progress.
top nidhi software solution freedownloadvrstrong314
This presentation emphasizes the importance of data security and legal compliance for Nidhi companies in India. It highlights how online Nidhi software solutions, like Vector Nidhi Software, offer advanced features tailored to these needs. Key aspects include encryption, access controls, and audit trails to ensure data security. The software complies with regulatory guidelines from the MCA and RBI and adheres to Nidhi Rules, 2014. With customizable, user-friendly interfaces and real-time features, these Nidhi software solutions enhance efficiency, support growth, and provide exceptional member services. The presentation concludes with contact information for further inquiries.
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/
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.
Globus Connect Server Deep Dive - GlobusWorld 2024Globus
We explore the Globus Connect Server (GCS) architecture and experiment with advanced configuration options and use cases. This content is targeted at system administrators who are familiar with GCS and currently operate—or are planning to operate—broader deployments at their institution.
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/
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.
A Comprehensive Look at Generative AI in Retail App Testing.pdfkalichargn70th171
Traditional software testing methods are being challenged in retail, where customer expectations and technological advancements continually shape the landscape. Enter generative AI—a transformative subset of artificial intelligence technologies poised to revolutionize software testing.
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.
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."
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
2. Webinar Platform - FAQs
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3. Paul Fremantle
VP of Product Engineering Weaveworks
Paul is an experienced open source software executive, who
previously co-founded WSO2. As CTO he helped build WSO2 into a
highly successful profitable Open Source company with recurring
revenues of more than $45m, 600 employees and over 500
enterprise customers.
Paul has an MSc in computer science from Oxford University and a
PhD from the University of Portsmouth, where his thesis offered a
cloud-based approach to improving security and privacy for IoT
systems.
Paul has two patents and has co-authored three books.
@pzfreo
✉ paul.fremantle@weave.works
3
Speaker Introduction
Paul Curtis
Principal Solutions Architect, Weaveworks
Paul comes from the big data world and machine learning world, having
spent seven years at MapR. Paul has served as Senior Operations
Engineer for Unami, a startup founded to deliver on the promise of
interactive TV for consumers, and was Systems Manager for Spiral
Universe, a company providing school administration software as a
service.
He has also held senior support engineer positions at Sun Microsystems,
as well as enterprise account technical management positions for both
Netscape and FileNet. Earlier in his career, Paul worked in financial
application development for Applix, IBM Service Bureau, and Ticketron.
@pfcurtis_NY
✉ paulc@weave.works
4. • Founding chair of the
CNCF technical oversight
committee (TOC)
• Coined the term GitOps,
and created the open
source tools that make
it work
• Creator of eksctl, the most
used way to work with
AWS EKS
• Invented open source
solutions to run
Kubernetes at scale for our
own Weave Cloud SaaS
product
Team Thought Leadership
• Alexis Richardson, CEO
• Cornelia Davis, CTO
• Steve George, COO
• Global Presence:
– US East, Central, West
– Europe
– India, Thailand
– South America
Notable Facts
• Founded in 2014
• Investors include: Accel,
AWS, Deutsche Telekom,
Ericsson, Google Ventures,
Orange and Redline
• Top 10 contributor to the
CNCF
• Multiple - thousand plus
star open source projects
Weaveworks
4
5. 5
The GitOps Company
Our mission is to provide a developer centric operating model
for cloud native technologies
● Weaveworks provides a modular solution for customers
transitioning to a cloud native platform
● We are a neutral vendor adding value to any flavor of
managed Kubernetes
● We deliver consistent management and monitoring
workflows to simplify operations
We are leaders in “GitOps” – best practices for consistent
management of cloud native apps
6. Weave GitOps Enterprise (Subscription)
Scaled GitOps
● Fleet Management with MCCP
● Advanced Curated Weave Policies using tools
Enterprise GitOps
● Curated model/profiles with cluster components
● Application tenancy through workspaces
● Authorization models consistent across the environment.
Kubernetes native
Weave GitOps Core (open-source)
Core GitOps
● Curated GitOps toolset, installer, runtime, and proven
example configurations
Prerequisites
● Infrastructure provisioner
● Source code repo platform
● Container registry
Weave GitOps
8. Weave GitOps Core
● Built on CNCF Flux
● Open source / open core base for our Weave GitOps Enterprise
Product
● Just two commands to get GitOps running
○ wego gitops install
○ wego app add .
8
9. App repo (default)
● Simple single repository
approach
● One application deployed to one
or more clusters
● GitOps automation
configuration lives in a .wego
directory
● Instant-on approach with no
extra repositories
Application repository or Platform repository
9
Platform repo
● “GitOps” repository
● GitOps automation for multiple
applications and clusters
● Simple upgrade to cluster
management and fleet management
● Supports “GitOps at Scale”
12. What’s New?
● Application Management User Interface
● Fleet Management
● Profiles
● Team Workspaces Updates
12
13. Application
Management UI
The graphical user interface that provides a
complete view of the GitOps application
delivery lifecycle.
13
Weave GitOps Enterprise Application
Management User Interface allows
users to understand and manage
application lifecycle in a GitOps enabled
cluster. From this interface, users can
immediately detect drift between states
as well as cluster health problems. From
this interface, users can inform roll back
actions as well as monitor continuous
operations.
14. Application Management UI
● Represents the repositories that
store a collection of a
declarative description of
runnable units
● Describes for the platform how
to deploy, start, operate, and
retire the corresponding service
artifact.
● Presents which of those repos is
being polled by the Weave
GitOps controllers
● Presents the services and the
workloads running in instances
in a specific environment,
including status
14
15. Profiles
The simplest and most secure way to
organize Kubernetes applications and
resources at scale
15
Weave GitOps Enterprise Profiles
provide a secure and easy method to
organize the applications and services
that run in your Kubernetes clusters. A
profile contains the artifacts that
configure and deploy your services, all
using GitOps. A profile manager
provides securable methods to install
profiles and manage catalogs of profiles
in a GitOps way
16. Profiles
A profile can contain manifests, Helm releases, and Kustomize templates. These artifacts
can declare any Kubernetes resource
A profile can contain other profiles.
The profile manager runs inside the cluster handle the installation of a profile. No
additional credentials are required.
All profiles are defined in git repository. This is also true of the catalog of profiles.
Profiles can be used on multiple clusters, in any combination. This allows definitions of
“classes” of clusters which are a combination of applications and services.
As profiles themselves are Kubernetes resources, they are secured using Kubernetes
standard role based authorization.
16
17. Profiles
17
Artifacts
Profile 2
Artifacts
Profile 1
● Profiles contain Kubernetes resources
● As Kubernetes resources, access and
use authorization is handled with
standard Kubernetes RBAC
● Profiles are easily managed and are a
portable way to define required
Kubernetes resources
● Profiles can contain other profiles,
allowing for complex deployment
patterns for applications and services.
18. Profiles
18
Provider Profile
Security Profile
App Profile
2
1
3
Deployment
Repository
● Profiles can be managed in Git by one or more teams or
groups
● Profiles are applied to the Deployment Repository
● GitOps then applies the Profile to all the clusters that use that
same Deployment Repository
“pctl install”
19. Profiles
19
Provider Profile
Security Profile
App Profile
2
1
3
Deployment
Repositories
“pctl install”
“pctl install”
This Profile
contains a second
Flux git source
definition
Multiple repositories/branches/directories can be utilized to keep
different profile functionality separate
20. Managing Fleets of
Clusters
Reliable, repeatable management of
Kubernetes clusters across any platform or
managed service
Operational performance is improved
with Cluster fleet management. Weave
GitOps Enterprise users can reuse
cluster templates easily from git. These
templates are consistent and
immutable making system behavior
predictable. Improvement of
infrastructure code is open to
contributions from anyone as anything
defined as code is.
20
21. Fleet Management
Cluster fleet management allows users to manage clusters across all platforms and
environments through the Cluster API.
Weaveworks GitOps Enterprise ensures tested, curated, and supported Cluster API
providers
Cluster templates for the Cluster API providers makes declaring new clusters very
straightforward
All clusters managed by Weave GitOps Enterprise are maintained in git
Cluster life cycle management is controlled by the Cluster API provider for each service or
platform
21
22. Fleet Management
22
● Cluster life cycle management
using the Cluster API (CAPI)
● Cluster infrastructure templates to
make cluster provisioning
repeatable and reliable
● Credentials for CAPI providers
stored as secrets in the git
repository
● All cluster templates, along with
the cluster bootstrap profile, are
stored in git making day zero
recovery simple
● Fleet management across all
platforms and environments: CAPI
provides platform independence
26. 26
Further info
Paul Fremantle
paul.fremantle@weave.works
@pzfreo
Paul Curtis
paulc@weave.works
@pfcurtis_NY
Try Weave GitOps Core:
https://www.weave.works/product/gitops-core/
Weave GitOps Enterprise
https://www.weave.works/product/gitops-enterprise/
Sign up for a free GitOps workshop (July 15):
https://bit.ly/3yybDD4