The document discusses Vertex AI pipelines for MLOps workflows. It begins with an introduction of the speaker and their background. It then discusses what MLOps is, defining three levels of automation maturity. Vertex AI is introduced as Google Cloud's managed ML platform. Pipelines are described as orchestrating the entire ML workflow through components. Custom components and conditionals allow flexibility. Pipelines improve reproducibility and sharing. Changes can trigger pipelines through services like Cloud Build, Eventarc, and Cloud Scheduler to continuously adapt models to new data.
Intro to Vertex AI, unified MLOps platform for Data Scientists & ML EngineersDaniel Zivkovic
#MLOps is a hot buzzword, just like #DevOps before it. It sparked a gold rush for software vendors, so it's hard to choose the best tool for your needs. Vertex AI is a unified MLOps platform for the entire #AI #workflow on #GoogleCloud. It is the 3rd iteration of the Google Cloud #ML platform (since its original launch), and we think they did it right (this time).
That's why #ServerlessTO invited 2 AI/ML gurus from #GCP (Jarek Kazmierczak & Brian Kang) to introduce the #VertexAI you to.
The lecture recording with Q&A is at https://youtu.be/X1S7360ip-k
MEETUP "CODE-ALONG" RESOURCES
Vertex workbench - Managed and User-managed Notebooks
https://cloud.google.com/vertex-ai/docs/workbench/managed/quickstarts
Example that the training code was based on - Fashion MNIST dataset
https://www.tensorflow.org/tutorials/keras/classification
Hyperparameter tuning codelab
https://codelabs.developers.google.com/vertex_hyperparameter_tuning
Vertex pipeline codelabs
https://codelabs.developers.google.com/vertex-pipelines-intro
https://codelabs.developers.google.com/vertex-pipelines-custom-model
CI/CD slides
https://github.com/shivajid/MLOpsCICD/blob/master/presentation/AI%20Workshop%20Day4.pdf
CI/CD github example
https://github.com/shivajid/MLOpsCICD
Model monitoring example
https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/official/model_monitoring/model_monitoring.ipynb
Best practices for MLOps
https://cloud.google.com/architecture/mlops-continuous-delivery-and-automation-pipelines-in-machine-learning
https://cloud.google.com/resources/mlops-whitepaper
Official Vertex AI Github repository
https://github.com/GoogleCloudPlatform/vertex-ai-samples/
MEETUP CHAT LINKS
https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/notebook_template.ipynb
https://github.com/GoogleCloudPlatform/vertex-ai-samples/tree/master/notebooks/official/custom
https://github.com/GoogleCloudPlatform/vertex-ai-samples/tree/master/notebooks/community/sdk
https://cloud.google.com/architecture/ml-on-gcp-best-practices#model-deployment-and-serving
https://www.youtube.com/watch?v=ntBEQdD1IeQ&list=PLd31CCJlr9FrZazLqRg1Lxq7xw9b6VNP6&index=3
Vertex AI - Unified ML Platform for the entire AI workflow on Google CloudMárton Kodok
Vertex AI is a managed ML platform for practitioners to accelerate experiments and deploy AI models.
Enhanced developer experience
- Build with the groundbreaking ML tools that power Google
- Approachable from the non-ML developer perspective (AutoML, managed models, training)
- Ease the life of a data scientist/ML (has feature store, managed datasets, endpoints, notebooks)
- Infrastructure management overhead have been almost completely eliminated
- Unified UI for the entire ML workflow
- End-to-end integration for data and AI with build pipelines that outperform and solve complex ML tasks
- Explainable AI and TensorBoard to visualize and track ML experiments
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.
MLOps and Data Quality: Deploying Reliable ML Models in ProductionProvectus
Looking to build a robust machine learning infrastructure to streamline MLOps? Learn from Provectus experts how to ensure the success of your MLOps initiative by implementing Data QA components in your ML infrastructure.
For most organizations, the development of multiple machine learning models, their deployment and maintenance in production are relatively new tasks. Join Provectus as we explain how to build an end-to-end infrastructure for machine learning, with a focus on data quality and metadata management, to standardize and streamline machine learning life cycle management (MLOps).
Agenda
- Data Quality and why it matters
- Challenges and solutions of Data Testing
- Challenges and solutions of Model Testing
- MLOps pipelines and why they matter
- How to expand validation pipelines for Data Quality
Intro to Vertex AI, unified MLOps platform for Data Scientists & ML EngineersDaniel Zivkovic
#MLOps is a hot buzzword, just like #DevOps before it. It sparked a gold rush for software vendors, so it's hard to choose the best tool for your needs. Vertex AI is a unified MLOps platform for the entire #AI #workflow on #GoogleCloud. It is the 3rd iteration of the Google Cloud #ML platform (since its original launch), and we think they did it right (this time).
That's why #ServerlessTO invited 2 AI/ML gurus from #GCP (Jarek Kazmierczak & Brian Kang) to introduce the #VertexAI you to.
The lecture recording with Q&A is at https://youtu.be/X1S7360ip-k
MEETUP "CODE-ALONG" RESOURCES
Vertex workbench - Managed and User-managed Notebooks
https://cloud.google.com/vertex-ai/docs/workbench/managed/quickstarts
Example that the training code was based on - Fashion MNIST dataset
https://www.tensorflow.org/tutorials/keras/classification
Hyperparameter tuning codelab
https://codelabs.developers.google.com/vertex_hyperparameter_tuning
Vertex pipeline codelabs
https://codelabs.developers.google.com/vertex-pipelines-intro
https://codelabs.developers.google.com/vertex-pipelines-custom-model
CI/CD slides
https://github.com/shivajid/MLOpsCICD/blob/master/presentation/AI%20Workshop%20Day4.pdf
CI/CD github example
https://github.com/shivajid/MLOpsCICD
Model monitoring example
https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/official/model_monitoring/model_monitoring.ipynb
Best practices for MLOps
https://cloud.google.com/architecture/mlops-continuous-delivery-and-automation-pipelines-in-machine-learning
https://cloud.google.com/resources/mlops-whitepaper
Official Vertex AI Github repository
https://github.com/GoogleCloudPlatform/vertex-ai-samples/
MEETUP CHAT LINKS
https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/notebook_template.ipynb
https://github.com/GoogleCloudPlatform/vertex-ai-samples/tree/master/notebooks/official/custom
https://github.com/GoogleCloudPlatform/vertex-ai-samples/tree/master/notebooks/community/sdk
https://cloud.google.com/architecture/ml-on-gcp-best-practices#model-deployment-and-serving
https://www.youtube.com/watch?v=ntBEQdD1IeQ&list=PLd31CCJlr9FrZazLqRg1Lxq7xw9b6VNP6&index=3
Vertex AI - Unified ML Platform for the entire AI workflow on Google CloudMárton Kodok
Vertex AI is a managed ML platform for practitioners to accelerate experiments and deploy AI models.
Enhanced developer experience
- Build with the groundbreaking ML tools that power Google
- Approachable from the non-ML developer perspective (AutoML, managed models, training)
- Ease the life of a data scientist/ML (has feature store, managed datasets, endpoints, notebooks)
- Infrastructure management overhead have been almost completely eliminated
- Unified UI for the entire ML workflow
- End-to-end integration for data and AI with build pipelines that outperform and solve complex ML tasks
- Explainable AI and TensorBoard to visualize and track ML experiments
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.
MLOps and Data Quality: Deploying Reliable ML Models in ProductionProvectus
Looking to build a robust machine learning infrastructure to streamline MLOps? Learn from Provectus experts how to ensure the success of your MLOps initiative by implementing Data QA components in your ML infrastructure.
For most organizations, the development of multiple machine learning models, their deployment and maintenance in production are relatively new tasks. Join Provectus as we explain how to build an end-to-end infrastructure for machine learning, with a focus on data quality and metadata management, to standardize and streamline machine learning life cycle management (MLOps).
Agenda
- Data Quality and why it matters
- Challenges and solutions of Data Testing
- Challenges and solutions of Model Testing
- MLOps pipelines and why they matter
- How to expand validation pipelines for Data Quality
MLflow is an MLOps tool that enables data scientist to quickly productionize their Machine Learning projects. To achieve this, MLFlow has four major components which are Tracking, Projects, Models, and Registry. MLflow lets you train, reuse, and deploy models with any library and package them into reproducible steps. MLflow is designed to work with any machine learning library and require minimal changes to integrate into an existing codebase. In this session, we will cover the common pain points of machine learning developers such as tracking experiments, reproducibility, deployment tool and model versioning. Ready to get your hands dirty by doing quick ML project using mlflow and release to production to understand the ML-Ops lifecycle.
End to end Machine Learning using Kubeflow - Build, Train, Deploy and ManageAnimesh Singh
With the breadth of sheer functionalities which need to be addressed in the Machine Learning world around building, training, serving and managing models, getting it done in a consistent, composable, portable, and scalable manner is hard. The Kubernetes framework is well suited to address these issues, which is why it's a great foundation for deploying ML workloads. Kubeflow is designed to take advantage of these benefits. In this talk, we are going to address how to make it easy for everyone to develop, deploy, and manage portable, scalable ML everywhere and support the full lifecycle Machine Learning using open source technologies like Kubeflow, Tensorflow, PyTorch,Tekton, Knative, Istio and others. We are going to discuss how to enable distributed training of models, model serving, canary rollouts, drift detection, model explainability, metadata management, pipelines and others. Additionally we will discuss Watson productization in progress based on Kubeflow Pipelines and Tekton, and point to Kubeflow Dojo materials and follow-on workshops.
The catalyst for the success of automobiles came not through the invention of the car but rather through the establishment of an innovative assembly line. History shows us that the ability to mass produce and distribute a product is the key to driving adoption of any innovation, and machine learning is no different. MLOps is the assembly line of Machine Learning and in this presentation we will discuss the core capabilities your organization should be focused on to implement a successful MLOps system.
Productionzing ML Model Using MLflow Model ServingDatabricks
Productionzing ML Models are needs to ensure model integrity while it efficiently replicate runtime environments across servers besides it keep track of how each of our models were created. It helps us better trace the root cause of changes and issues over time as we acquire new data and update our model. We have greater accountability over our models and the results they generate.
MLflow Model Serving delivers cost-effective and on-click deployment of model for real-time inferences. Also the Model Version deployed in the Model Serving can also be conveniently managed with MLflow Model Registry. We will going to cover following topics Deployment, Consumption and Monitoring. For deployment, we will demo the different version deployment and validate the deployment. For consumption, we demo connecting power bi and generate prediction report using ML Model deployed in MLflow serving. Lastly will wrap up with managing the MLflow serving like, access rights and monitoring capabilities.
Using MLOps to Bring ML to Production/The Promise of MLOpsWeaveworks
In this final Weave Online User Group of 2019, David Aronchick asks: have you ever struggled with having different environments to build, train and serve ML models, and how to orchestrate between them? While DevOps and GitOps have made huge traction in recent years, many customers struggle to apply these practices to ML workloads. This talk will focus on the ways MLOps has helped to effectively infuse AI into production-grade applications through establishing practices around model reproducibility, validation, versioning/tracking, and safe/compliant deployment. We will also talk about the direction for MLOps as an industry, and how we can use it to move faster, with more stability, than ever before.
The recording of this session is on our YouTube Channel here: https://youtu.be/twsxcwgB0ZQ
Speaker: David Aronchick, Head of Open Source ML Strategy, Microsoft
Bio: David leads Open Source Machine Learning Strategy at Azure. This means he spends most of his time helping humans to convince machines to be smarter. He is only moderately successful at this. Previously, David led product management for Kubernetes at Google, launched GKE, and co-founded the Kubeflow project. David has also worked at Microsoft, Amazon and Chef and co-founded three startups.
Sign up for a free Machine Learning Ops Workshop: http://bit.ly/MLOps_Workshop_List
Weaveworks will cover concepts such as GitOps (operations by pull request), Progressive Delivery (canary, A/B, blue-green), and how to apply those approaches to your machine learning operations to mitigate risk.
MLOps journey at Swisscom: AI Use Cases, Architecture and Future VisionBATbern
What powers the AI/ML services of Switzerland's leading telecommunication company? In this talk, we will provide an overview of the different AI/ML projects at Swisscom, from Conversational AI and Recommender Systems to Anomaly Detection. Moreover, we will show how we automate, scale, and operationalise these ML pipelines in production, highlighting the MLOps techniques and open source tools that are used. Finally, we will present Swisscom's roadmap towards the cloud with AWS and discuss how we envision a common MLOps solution for the organisation.
MLOps (a compound of “machine learning” and “operations”) is a practice for collaboration and communication between data scientists and operations professionals to help manage the production machine learning lifecycle. Similar to the DevOps term in the software development world, MLOps looks to increase automation and improve the quality of production ML while also focusing on business and regulatory requirements. MLOps applies to the entire ML lifecycle - from integrating with model generation (software development lifecycle, continuous integration/continuous delivery), orchestration, and deployment, to health, diagnostics, governance, and business metrics.
To watch the full presentation click here: https://info.cnvrg.io/mlopsformachinelearning
In this webinar, we’ll discuss core practices in MLOps that will help data science teams scale to the enterprise level. You’ll learn the primary functions of MLOps, and what tasks are suggested to accelerate your teams machine learning pipeline. Join us in a discussion with cnvrg.io Solutions Architect, Aaron Schneider, and learn how teams use MLOps for more productive machine learning workflows.
- Reduce friction between science and engineering
- Deploy your models to production faster
- Health, diagnostics and governance of ML models
- Kubernetes as a core platform for MLOps
- Support advanced use-cases like continual learning with MLOps
MLOps Bridging the gap between Data Scientists and Ops.Knoldus Inc.
Through this session we're going to introduce the MLOps lifecycle and discuss the hidden loopholes that can affect the MLProject. Then we are going to discuss the ML Model lifecycle and discuss the problem with training. We're going to introduce the MLFlow Tracking module in order to track the experiments.
Deep dive into Kubeflow Pipelines, and details about Tekton backend implementation for KFP, including compiler, logging, artifacts and lineage tracking
Given at the MLOps. Summit 2020 - I cover the origins of MLOps in 2018, how MLOps has evolved from 2018 to 2020, and what I expect for the future of MLOps
MLflow: Infrastructure for a Complete Machine Learning Life Cycle with Mani ...Databricks
ML development brings many new complexities beyond the traditional software development lifecycle. Unlike in traditional software development, ML developers want to try multiple algorithms, tools, and parameters to get the best results, and they need to track this information to reproduce work. In addition, developers need to use many distinct systems to productionize models. To address these problems, many companies are building custom “ML platforms” that automate this lifecycle, but even these platforms are limited to a few supported algorithms and to each company’s internal infrastructure. In this session, we introduce MLflow, a new open source project from Databricks that aims to design an open ML platform where organizations can use any ML library and development tool of their choice to reliably build and share ML applications. MLflow introduces simple abstractions to package reproducible projects, track results, and encapsulate models that can be used with many existing tools, accelerating the ML lifecycle for organizations of any size. In this deep-dive session, through a complete ML model life-cycle example, you will walk away with:
MLflow concepts and abstractions for models, experiments, and projects
How to get started with MLFlow
Understand aspects of MLflow APIs
Using tracking APIs during model training
Using MLflow UI to visually compare and contrast experimental runs with different tuning parameters and evaluate metrics
Package, save, and deploy an MLflow model
Serve it using MLflow REST API
What’s next and how to contribute
Bighead: Airbnb’s End-to-End Machine Learning Platform with Krishna Puttaswa...Databricks
Airbnb has a wide variety of ML problems ranging from models on traditional structured data to models built on unstructured data such as user reviews, messages and listing images. The ability to build, iterate on, and maintain healthy machine learning models is critical to Airbnb’s success. Many ML Platforms cover data collection, feature engineering, training, deploying, productionalization, and monitoring but few, if any, do all of the above seamlessly.
Bighead aims to tie together various open source and in-house projects to remove incidental complexity from ML workflows. Bighead is built on Python and Spark and can be used in modular pieces as each ML problem presents unique challenges. Through standardization of the path to production, training environments and the methods for collecting and transforming data on Spark, each model is reproducible and iterable.
This talk covers the architecture, the problems that each individual component and the overall system aims to solve, and a vision for the future of machine learning infrastructure. It’s widely adapted in Airbnb and we have variety of models running in production. We have seen the overall model development time go down from many months to days on Bighead. We plan to open source Bighead to allow the wider community to benefit from our work.
MLflow: Infrastructure for a Complete Machine Learning Life CycleDatabricks
ML development brings many new complexities beyond the traditional software development lifecycle. Unlike in traditional software development, ML developers want to try multiple algorithms, tools and parameters to get the best results, and they need to track this information to reproduce work. In addition, developers need to use many distinct systems to productionize models. To address these problems, many companies are building custom “ML platforms” that automate this lifecycle, but even these platforms are limited to a few supported algorithms and to each company’s internal infrastructure.
In this talk, we will present MLflow, a new open source project from Databricks that aims to design an open ML platform where organizations can use any ML library and development tool of their choice to reliably build and share ML applications. MLflow introduces simple abstractions to package reproducible projects, track results, and encapsulate models that can be used with many existing tools, accelerating the ML lifecycle for organizations of any size.
MLOps with serverless architectures (October 2018)Julien SIMON
Talk @ AWS Loft Stockholm, 23/10/2018
But why?
A quick recap on Amazon SageMaker
A quick recap on serverless architectures
Open Source tools: AWS Chalice, Serverless Framework
Demos
Resources
DevBCN Vertex AI - Pipelines for your MLOps workflowsMárton Kodok
In recent years, one of the biggest trends in applications development has been the rise of Machine Learning solutions, tools, and managed platforms. Vertex AI is a managed unified ML platform for all your AI workloads. On the MLOps side, Vertex AI Pipelines solutions let you adopt experiment pipelining beyond the classic build, train, eval, and deploy a model. It is engineered for data scientists and data engineers, and it’s a tremendous help for those teams who don’t have DevOps or sysadmin engineers, as infrastructure management overhead has been almost completely eliminated. Based on practical examples we will demonstrate how Vertex AI Pipelines scores high in terms of developer experience, how fits custom ML needs, and analyze results. It’s a toolset for a fully-fledged machine learning workflow, a sequence of steps in the model development, a deployment cycle, such as data preparation/validation, model training, hyperparameter tuning, model validation, and model deployment. Vertex AI comes with all classic resources plus an ML metadata store, a fully managed feature store, and a fully managed pipelines runner. Vertex AI Pipelines is a managed serverless toolkit, which means you don't have to fiddle with infrastructure or back-end resources to run workflows.
MLflow is an MLOps tool that enables data scientist to quickly productionize their Machine Learning projects. To achieve this, MLFlow has four major components which are Tracking, Projects, Models, and Registry. MLflow lets you train, reuse, and deploy models with any library and package them into reproducible steps. MLflow is designed to work with any machine learning library and require minimal changes to integrate into an existing codebase. In this session, we will cover the common pain points of machine learning developers such as tracking experiments, reproducibility, deployment tool and model versioning. Ready to get your hands dirty by doing quick ML project using mlflow and release to production to understand the ML-Ops lifecycle.
End to end Machine Learning using Kubeflow - Build, Train, Deploy and ManageAnimesh Singh
With the breadth of sheer functionalities which need to be addressed in the Machine Learning world around building, training, serving and managing models, getting it done in a consistent, composable, portable, and scalable manner is hard. The Kubernetes framework is well suited to address these issues, which is why it's a great foundation for deploying ML workloads. Kubeflow is designed to take advantage of these benefits. In this talk, we are going to address how to make it easy for everyone to develop, deploy, and manage portable, scalable ML everywhere and support the full lifecycle Machine Learning using open source technologies like Kubeflow, Tensorflow, PyTorch,Tekton, Knative, Istio and others. We are going to discuss how to enable distributed training of models, model serving, canary rollouts, drift detection, model explainability, metadata management, pipelines and others. Additionally we will discuss Watson productization in progress based on Kubeflow Pipelines and Tekton, and point to Kubeflow Dojo materials and follow-on workshops.
The catalyst for the success of automobiles came not through the invention of the car but rather through the establishment of an innovative assembly line. History shows us that the ability to mass produce and distribute a product is the key to driving adoption of any innovation, and machine learning is no different. MLOps is the assembly line of Machine Learning and in this presentation we will discuss the core capabilities your organization should be focused on to implement a successful MLOps system.
Productionzing ML Model Using MLflow Model ServingDatabricks
Productionzing ML Models are needs to ensure model integrity while it efficiently replicate runtime environments across servers besides it keep track of how each of our models were created. It helps us better trace the root cause of changes and issues over time as we acquire new data and update our model. We have greater accountability over our models and the results they generate.
MLflow Model Serving delivers cost-effective and on-click deployment of model for real-time inferences. Also the Model Version deployed in the Model Serving can also be conveniently managed with MLflow Model Registry. We will going to cover following topics Deployment, Consumption and Monitoring. For deployment, we will demo the different version deployment and validate the deployment. For consumption, we demo connecting power bi and generate prediction report using ML Model deployed in MLflow serving. Lastly will wrap up with managing the MLflow serving like, access rights and monitoring capabilities.
Using MLOps to Bring ML to Production/The Promise of MLOpsWeaveworks
In this final Weave Online User Group of 2019, David Aronchick asks: have you ever struggled with having different environments to build, train and serve ML models, and how to orchestrate between them? While DevOps and GitOps have made huge traction in recent years, many customers struggle to apply these practices to ML workloads. This talk will focus on the ways MLOps has helped to effectively infuse AI into production-grade applications through establishing practices around model reproducibility, validation, versioning/tracking, and safe/compliant deployment. We will also talk about the direction for MLOps as an industry, and how we can use it to move faster, with more stability, than ever before.
The recording of this session is on our YouTube Channel here: https://youtu.be/twsxcwgB0ZQ
Speaker: David Aronchick, Head of Open Source ML Strategy, Microsoft
Bio: David leads Open Source Machine Learning Strategy at Azure. This means he spends most of his time helping humans to convince machines to be smarter. He is only moderately successful at this. Previously, David led product management for Kubernetes at Google, launched GKE, and co-founded the Kubeflow project. David has also worked at Microsoft, Amazon and Chef and co-founded three startups.
Sign up for a free Machine Learning Ops Workshop: http://bit.ly/MLOps_Workshop_List
Weaveworks will cover concepts such as GitOps (operations by pull request), Progressive Delivery (canary, A/B, blue-green), and how to apply those approaches to your machine learning operations to mitigate risk.
MLOps journey at Swisscom: AI Use Cases, Architecture and Future VisionBATbern
What powers the AI/ML services of Switzerland's leading telecommunication company? In this talk, we will provide an overview of the different AI/ML projects at Swisscom, from Conversational AI and Recommender Systems to Anomaly Detection. Moreover, we will show how we automate, scale, and operationalise these ML pipelines in production, highlighting the MLOps techniques and open source tools that are used. Finally, we will present Swisscom's roadmap towards the cloud with AWS and discuss how we envision a common MLOps solution for the organisation.
MLOps (a compound of “machine learning” and “operations”) is a practice for collaboration and communication between data scientists and operations professionals to help manage the production machine learning lifecycle. Similar to the DevOps term in the software development world, MLOps looks to increase automation and improve the quality of production ML while also focusing on business and regulatory requirements. MLOps applies to the entire ML lifecycle - from integrating with model generation (software development lifecycle, continuous integration/continuous delivery), orchestration, and deployment, to health, diagnostics, governance, and business metrics.
To watch the full presentation click here: https://info.cnvrg.io/mlopsformachinelearning
In this webinar, we’ll discuss core practices in MLOps that will help data science teams scale to the enterprise level. You’ll learn the primary functions of MLOps, and what tasks are suggested to accelerate your teams machine learning pipeline. Join us in a discussion with cnvrg.io Solutions Architect, Aaron Schneider, and learn how teams use MLOps for more productive machine learning workflows.
- Reduce friction between science and engineering
- Deploy your models to production faster
- Health, diagnostics and governance of ML models
- Kubernetes as a core platform for MLOps
- Support advanced use-cases like continual learning with MLOps
MLOps Bridging the gap between Data Scientists and Ops.Knoldus Inc.
Through this session we're going to introduce the MLOps lifecycle and discuss the hidden loopholes that can affect the MLProject. Then we are going to discuss the ML Model lifecycle and discuss the problem with training. We're going to introduce the MLFlow Tracking module in order to track the experiments.
Deep dive into Kubeflow Pipelines, and details about Tekton backend implementation for KFP, including compiler, logging, artifacts and lineage tracking
Given at the MLOps. Summit 2020 - I cover the origins of MLOps in 2018, how MLOps has evolved from 2018 to 2020, and what I expect for the future of MLOps
MLflow: Infrastructure for a Complete Machine Learning Life Cycle with Mani ...Databricks
ML development brings many new complexities beyond the traditional software development lifecycle. Unlike in traditional software development, ML developers want to try multiple algorithms, tools, and parameters to get the best results, and they need to track this information to reproduce work. In addition, developers need to use many distinct systems to productionize models. To address these problems, many companies are building custom “ML platforms” that automate this lifecycle, but even these platforms are limited to a few supported algorithms and to each company’s internal infrastructure. In this session, we introduce MLflow, a new open source project from Databricks that aims to design an open ML platform where organizations can use any ML library and development tool of their choice to reliably build and share ML applications. MLflow introduces simple abstractions to package reproducible projects, track results, and encapsulate models that can be used with many existing tools, accelerating the ML lifecycle for organizations of any size. In this deep-dive session, through a complete ML model life-cycle example, you will walk away with:
MLflow concepts and abstractions for models, experiments, and projects
How to get started with MLFlow
Understand aspects of MLflow APIs
Using tracking APIs during model training
Using MLflow UI to visually compare and contrast experimental runs with different tuning parameters and evaluate metrics
Package, save, and deploy an MLflow model
Serve it using MLflow REST API
What’s next and how to contribute
Bighead: Airbnb’s End-to-End Machine Learning Platform with Krishna Puttaswa...Databricks
Airbnb has a wide variety of ML problems ranging from models on traditional structured data to models built on unstructured data such as user reviews, messages and listing images. The ability to build, iterate on, and maintain healthy machine learning models is critical to Airbnb’s success. Many ML Platforms cover data collection, feature engineering, training, deploying, productionalization, and monitoring but few, if any, do all of the above seamlessly.
Bighead aims to tie together various open source and in-house projects to remove incidental complexity from ML workflows. Bighead is built on Python and Spark and can be used in modular pieces as each ML problem presents unique challenges. Through standardization of the path to production, training environments and the methods for collecting and transforming data on Spark, each model is reproducible and iterable.
This talk covers the architecture, the problems that each individual component and the overall system aims to solve, and a vision for the future of machine learning infrastructure. It’s widely adapted in Airbnb and we have variety of models running in production. We have seen the overall model development time go down from many months to days on Bighead. We plan to open source Bighead to allow the wider community to benefit from our work.
MLflow: Infrastructure for a Complete Machine Learning Life CycleDatabricks
ML development brings many new complexities beyond the traditional software development lifecycle. Unlike in traditional software development, ML developers want to try multiple algorithms, tools and parameters to get the best results, and they need to track this information to reproduce work. In addition, developers need to use many distinct systems to productionize models. To address these problems, many companies are building custom “ML platforms” that automate this lifecycle, but even these platforms are limited to a few supported algorithms and to each company’s internal infrastructure.
In this talk, we will present MLflow, a new open source project from Databricks that aims to design an open ML platform where organizations can use any ML library and development tool of their choice to reliably build and share ML applications. MLflow introduces simple abstractions to package reproducible projects, track results, and encapsulate models that can be used with many existing tools, accelerating the ML lifecycle for organizations of any size.
MLOps with serverless architectures (October 2018)Julien SIMON
Talk @ AWS Loft Stockholm, 23/10/2018
But why?
A quick recap on Amazon SageMaker
A quick recap on serverless architectures
Open Source tools: AWS Chalice, Serverless Framework
Demos
Resources
DevBCN Vertex AI - Pipelines for your MLOps workflowsMárton Kodok
In recent years, one of the biggest trends in applications development has been the rise of Machine Learning solutions, tools, and managed platforms. Vertex AI is a managed unified ML platform for all your AI workloads. On the MLOps side, Vertex AI Pipelines solutions let you adopt experiment pipelining beyond the classic build, train, eval, and deploy a model. It is engineered for data scientists and data engineers, and it’s a tremendous help for those teams who don’t have DevOps or sysadmin engineers, as infrastructure management overhead has been almost completely eliminated. Based on practical examples we will demonstrate how Vertex AI Pipelines scores high in terms of developer experience, how fits custom ML needs, and analyze results. It’s a toolset for a fully-fledged machine learning workflow, a sequence of steps in the model development, a deployment cycle, such as data preparation/validation, model training, hyperparameter tuning, model validation, and model deployment. Vertex AI comes with all classic resources plus an ML metadata store, a fully managed feature store, and a fully managed pipelines runner. Vertex AI Pipelines is a managed serverless toolkit, which means you don't have to fiddle with infrastructure or back-end resources to run workflows.
Tech leaders guide to effective building of machine learning productsGianmario Spacagna
Part 2/2 (Tech Leaders)
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.
MLOps Virtual Event | Building Machine Learning Platforms for the Full LifecycleDatabricks
Successfully building a machine learning model is hard enough. Reproducing your results at scale — enabling others to reproduce pipelines, comparing results from other versions, moving models into production, redeploying and rolling out updated models — is exponentially harder. To address these challenges and accelerate innovation, many companies are building custom “ML platforms” to automate the end-to-end ML lifecycle.
Watch a replay of this MLOps Virtual Event to hear more about the latest developments and best practices for managing the full ML lifecycle on Databricks with MLflow. We covered a checklist of capabilities you’ll need, common pitfalls, technological and organizational challenges, and how to overcome them.
https://www.youtube.com/playlist?list=PLTPXxbhUt-YUFNBwBsSIlknoNbS7GExZw
ODSC East 2020 Accelerate ML Lifecycle with Kubernetes and Containerized Da...Abhinav Joshi
This deck provide an overview of containers and Kubernetes, and how these technologies can help solve the challenges faced by data scientists, ML engineers, and application developers. Next, it showcases the key capabilities required in a containers and kubernetes platform to help data scientists easily use technologies like Jupyter Notebooks, ML frameworks, programming languages to innovate faster. Finally it discusses the available platform options (e.g. KubeFlow, Open Data Hub, etc.), and some examples of how data scientists are accelerating their ML initiatives with containers and kubernetes platform.
Building a MLOps Platform Around MLflow to Enable Model Productionalization i...Databricks
Getting machine learning models to production is notoriously difficult: it involves multiple teams (data scientists, data and machine learning engineers, operations, …), who often does not speak to each other very well; the model can be trained in one environment but then productionalized in completely different environment; it is not just about the code, but also about the data (features) and the model itself… At DataSentics, as a machine learning and cloud engineering studio, we see this struggle firsthand – on our internal projects and client’s projects as well.
GDG Cloud Southlake #16: Priyanka Vergadia: Scalable Data Analytics in Google...James Anderson
Do you know The Cloud Girl? She makes the cloud come alive with pictures and storytelling.
The Cloud Girl, Priyanka Vergadia, Chief Content Officer @Google, joins us to tell us about Scaleable Data Analytics in Google Cloud.
Maybe, with her explanation, we'll finally understand it!
Priyanka is a technical storyteller and content creator who has created over 300 videos, articles, podcasts, courses and tutorials which help developers learn Google Cloud fundamentals, solve their business challenges and pass certifications! Checkout her content on Google Cloud Tech Youtube channel.
Priyanka enjoys drawing and painting which she tries to bring to her advocacy.
Check out her website The Cloud Girl: https://thecloudgirl.dev/ and her new book: https://www.amazon.com/Visualizing-Google-Cloud-Illustrated-References/dp/1119816327
What MLflow is; what problem it solves for machine learning lifecycle; and how it solves; How it will be used with Databricks; and CI/CD pipeline with Databricks.
The ODAHU project is focused on creating services, extensions for third party systems and tools which help to accelerate building enterprise level systems with automated AI/ML models life cycle.
When it comes to Large Scale data processing and Machine Learning, Apache Spark is no doubt one of the top battle-tested frameworks out there for handling batched or streaming workloads. The ease of use, built-in Machine Learning modules, and multi-language support makes it a very attractive choice for data wonks. However bootstrapping and getting off the ground could be difficult for most teams without leveraging a Spark cluster that is already pre-provisioned and provided as a managed service in the Cloud, while this is a very attractive choice to get going, in the long run, it could be a very expensive option if it’s not well managed.
As an alternative to this approach, our team has been exploring and working a lot with running Spark and all our Machine Learning workloads and pipelines as containerized Docker packages on Kubernetes. This provides an infrastructure-agnostic abstraction layer for us, and as a result, it improves our operational efficiency and reduces our overall compute cost. Most importantly, we can easily target our Spark workload deployment to run on any major Cloud or On-prem infrastructure (with Kubernetes as the common denominator) by just modifying a few configurations.
In this talk, we will walk you through the process our team follows to make it easy for us to run a production deployment of our Machine Learning workloads and pipelines on Kubernetes which seamlessly allows us to port our implementation from a local Kubernetes set up on the laptop during development to either an On-prem or Cloud Kubernetes environment
Gen Apps on Google Cloud PaLM2 and Codey APIs in ActionMárton Kodok
Build applications with generative AI on Google Cloud! We are going to see in action what Gen App Builder is for developers to build and deploy AI-driven applications. We will explore Model Garden powered experiences, then we are going to learn more about the integration of these generative AI APIs. Vertex AI includes a suite of models that work with code. Together these code models are referred to as the PaLM and Codey APIs. The Vertex AI Codey APIs include the code generation API which supports generating code using a natural language description. We will show strategies for creating prompts that work with the model to generate code. At the end of the session, developers will understand how to innovate with generative AI and develop apps using the generative AI industry trends.
Scaling AI/ML with Containers and Kubernetes Tushar Katarki
AI is popular and yet faces several challenges in the industry: 1) self-service and automation 2) Deployment into production 3) Access to data. These challenges can be addressed with containers and Kubernetes. They help you build AI-as-a-service with open source tools and Kuberentes. Data Scientists can use the service for data, experimentation and to deliver models into production iteratively with self-service and automation. Using Kubernetes, one is able to run massive machine learning pipelines iteratively in an automated fashion that can be repeated.
Introducing MlFlow: An Open Source Platform for the Machine Learning Lifecycl...DataWorks Summit
Specialized tools for machine learning development and model governance are becoming essential. MlFlow is an open source platform for managing the machine learning lifecycle. Just by adding a few lines of code in the function or script that trains their model, data scientists can log parameters, metrics, artifacts (plots, miscellaneous files, etc.) and a deployable packaging of the ML model. Every time that function or script is run, the results will be logged automatically as a byproduct of those lines of code being added, even if the party doing the training run makes no special effort to record the results. MLflow application programming interfaces (APIs) are available for the Python, R and Java programming languages, and MLflow sports a language-agnostic REST API as well. Over a relatively short time period, MLflow has garnered more than 3,300 stars on GitHub , almost 500,000 monthly downloads and 80 contributors from more than 40 companies. Most significantly, more than 200 companies are now using MLflow. We will demo MlFlow Tracking , Project and Model components with Azure Machine Learning (AML) Services and show you how easy it is to get started with MlFlow on-prem or in the cloud.
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
In this session, we will take a deep-dive into the DevOps process that comes with Azure Machine Learning service, a cloud service that you can use to track as you build, train, deploy and manage models. We zoom into how the data science process can be made traceable and deploy the model with Azure DevOps to a Kubernetes cluster.
At the end of this session, you will have a good grasp of the technological building blocks of Azure machine learning services and can bring a machine learning project safely into production.
"Managing the Complete Machine Learning Lifecycle with MLflow"Databricks
Machine Learning development brings many new complexities beyond the traditional software development lifecycle. Unlike in traditional software development, ML developers want to try multiple algorithms, tools, and parameters to get the best results, and they need to track this information to reproduce work. In addition, developers need to use many distinct systems to productionize models. To address these problems, many companies are building custom “ML platforms” that automate this lifecycle, but even these platforms are limited to a few supported algorithms and to each company’s internal infrastructure.
In this session, we introduce MLflow, a new open-source project from Databricks that aims to design an open ML platform where organizations can use any ML library and development tool of their choice to reliably build and share ML applications. MLflow introduces simple abstractions to package reproducible projects, track results, and encapsulate models that can be used with many existing tools, accelerating the ML lifecycle for organizations of any size.
Similar to Vertex AI: Pipelines for your MLOps workflows (20)
Discover BigQuery ML, build your own CREATE MODEL statementMárton Kodok
With BigQuery ML, you can build machine learning models without leaving the database environment and training it on massive datasets. In this demo session we are going to demonstrate common marketing Machine Learning use cases of how to build, train, eval, and predict, your own scalable machine learning models using SQL language in Google BigQuery and to address the following use cases: - Customer Segmentation + Product cross sale recommendation - Conversion/Purchase prediction - Inference with other in-built >20 models The audience will get first-hand experience with how to write CREATE MODEL sql syntax to build machine learning models such as: - Multiclass logistic regression for classification - K-means clustering - Matrix factorization - ARIMA time series predictions ... and more Models are trained and accessed in BigQuery using SQL — a language data analysts know. This enables business decision-making through predictive analytics across the organization without leaving the query editor. In the end, the audience will learn how everyday developers can build/train/run their own machine-learning models straight from the database query editor, by issuing CREATE MODEL statements
Cloud Run - the rise of serverless and containerizationMárton Kodok
Two of the biggest trends in applications development in recent years have been the rise of serverless and containerization. And Cloud Run has become a defacto container runtime service to production in seconds. Based on practical examples we will demonstrate how Cloud Run scores high in terms of developer experience. It differs from functions runtime as You can bring your own container, your own code, a folder, or binarys and it pairs great with the container ecosystem: Cloud Build, Cloud Code, Artifact Registry, and Docker. Each Cloud Run service gets an out-of-the-box stable HTTPS endpoint, with TLS termination handled for you. Map your services to your own domains and use either for web sites, backend APIs, workflows, invoke and connect services with the newest protocols of HTTP/2, WebSockets or gRPC (unary and streaming). Cloud Run is serverless containers, which means you don't have to fiddle with infrastructure or back-end resources to run applications.
BigQuery best practices and recommendations to reduce costs with BI Engine, S...Márton Kodok
best practices and recommendations for tuning BI Engine for your existing BigQuery workloads for cheaper and faster queries. Learn how we at REEA are orchestrating BI Engine reservations, on a 5TB dataset, considered small for BigQuery but with big cost savings and accelerated queries. We are seeing many presentations for big enterprises, but now we are showcasing how our queries perform better with lower costs. We are going to address the top considerations when to turn on BI Engine, how to use cloud orchestration for making this an automatic process, and combined with BigQuery and Datastudio query complexity that might save precious development time, lower bills, faster queries.
Cloud Workflows What's new in serverless orchestration and automationMárton Kodok
understand how Cloud Workflows resolves challenges in connecting services, HTTP based service orchestration and automation. We are going to dive deep how serverless HTTP service automation works to automate step engines. Based on practical examples we will demonstrate the newest features that lets you automate the cloud and integration with any Google Cloud product without worrying about authentication
Serverless orchestration and automation with Cloud WorkflowsMárton Kodok
Join this session to understand how Cloud Workflows resolves challenges in connecting services, HTTP based service orchestration and automation. We are going to dive deep how serverless HTTP service automation works to automate step engines. Based on practical examples we will demonstrate the built-in decision and conditional executions, subworkflows, support for external built-in API calls, and integration with any Google Cloud product without worrying about authentication. We are going to cover Marketing, Retail, Industrial and Developer possibilities, such as event driven marketing workflow execution, or inventory chain operations, generating and automatic state machines, or orchestrate DevOps workflows and automating the Cloud.
Serverless orchestration and automation with Cloud WorkflowsMárton Kodok
Join this session to understand how Cloud Workflows resolves challenges in connecting services, HTTP based service orchestration and automation. We are going to dive deep how serverless HTTP service automation works to automate step engines. Based on practical examples we will demonstrate the built-in decision and conditional executions, subworkflows, support for external built-in API calls, and integration with any Google Cloud product without worrying about authentication. We are going to cover Marketing, Retail, Industrial and Developer possibilities, such as event driven marketing workflow execution, or inventory chain operations, generating and automatic state machines, or orchestrate DevOps workflows and automating the Cloud.
Serverless orchestration and automation with Cloud WorkflowsMárton Kodok
Join this session to understand how Cloud Workflows resolves challenges in connecting services, HTTP based service orchestration and automation. We are going to dive deep how serverless HTTP service automation works to automate step engines. Based on practical examples we will demonstrate the built-in decision and conditional executions, subworkflows, support for external built-in API calls, and integration with any Google Cloud product without worrying about authentication. We are going to cover Marketing, Retail, Industrial and Developer possibilities, such as event driven marketing workflow execution, or inventory chain operations, generating and automatic state machines, or orchestrate DevOps workflows and automating the Cloud.
BigdataConference Europe - BigQuery MLMárton Kodok
One of the hottest topics in database land these days is BigQuery ML. A new way to use machine learning on top of tabular data straight on your tables without leaving the query editor.
With BigQuery ML, you can build machine learning models without leaving the database environment and training it on massive datasets.
In this demo session, we are going to demonstrate common marketing Machine Learning use cases how to build, train, eval and predict, your own scalable machine learning models using SQL language.
The audience will get first hand experience how to write CREATE MODEL sql syntax to build machine learning models such as:
– Multiclass logistic regression for classification
– K-means clustering
– Matrix factorization
– ARIMA time series predictions
– Import TensorFlow models for prediction in BigQuery
Models are trained and accessed in BigQuery using SQL — a language data analysts know. This enables business decision making through predictive analytics across the organization without leaving the query editor.
DevFest Romania 2020 Keynote: Bringing the Cloud to you.Márton Kodok
Next OnAir 20 in review,
Real-time AI solutions
like anomaly detection, pattern recognition, and predictive forecasting
2. Recommendations AI rich experience to personalized product recommendations
3. Media Translation API real-time speech translation from streaming audio
4. Lending DocAI solution powered by Document AI for mortgage industry
5. Contact Center AI support over chat/voice calls by identifying intent and providing assistance
Confidential VMs are a breakthrough technology that allow customers to encrypt their most sensitive data in the cloud while it's being processed
Cloud Run: - Minimum idle instances
- Allocate 4 vCPUs and 4GiB memory
- Requests up to 60 minutes
- Server-side HTTP + gRPC streaming
- VPC access support
- External Load Balancing
Serverless orchestration and automation with Cloud Workflows (beta)
- Steps defined in YAML
- Built-in decision and conditional exec
- Subworkflows
- Support for external API calls
- Custom predicate for retries
Predict, recommend and forecast with BigQuery ML
CREATE MODEL syntax in BigQuery to run Machine Learning tasks
Supported models:
- K-means clustering for data segmentation
- Recommend with Matrix Factorization
- Perform time-series forecast
- Import TensorFlow models
Single interface for multiple services with API Gateway
Find Your Topic and Skill Level
Qwiklabs + New Tutorials Center
BigQuery ML - Machine learning at scale using SQLMárton Kodok
With BigQuery ML, you can build machine learning models without leaving the data warehouse environment and training it on massive datasets. We are going to demonstrate how to build, train, eval and predict, your own scalable machine learning models using standard SQL language in Google BigQuery.
We will see how can we use CREATE MODEL sql syntax to build different models such as:
-Linear regression
-Multiclass logistic regression for classification
-K-means clustering
-Import TensorFlow models for prediction in BigQuery
We will see how we can apply these models on tabular data in retail and marketing use cases.
Models are trained and accessed in BigQuery using SQL — a language data analysts know. This enables business decision making through predictive analytics across the organization without leaving the query editor.
Applying BigQuery ML on e-commerce data analyticsMárton Kodok
With BigQuery ML, you can build machine learning models without leaving the database environment and training it on massive datasets. We are going to demonstrate common marketing Machine Learning use cases we do at REEA.net to build, train, eval and predict, your own scalable machine learning models using SQL language in Google BigQuery and to address the following use cases:
Customer Segmentation
Customer Lifetime Value (LTV) prediction
Conversion/Purchase prediction
The audience will get first hand experience how to write CREATE MODEL sql syntax to build machine learning models such as:
Multiclass logistic regression for classification
K-means clustering
Import TensorFlow models for prediction in BigQuery
Models are trained and accessed in BigQuery using SQL — a language data analysts know. This enables business decision making through predictive analytics across the organization without leaving the query editor
Supercharge your data analytics with BigQueryMárton Kodok
Powering interactive data analysis require massive architecture, and Know-How to build a fast real-time computing system. BigQuery solves this problem by enabling super-fast, SQL-like queries against petabytes of data using the processing power of Google’s infrastructure. We will cover its core features, creating tables, columns, views, working with partitions, clustering for cost optimizations, streaming inserts, User Defined Functions, and several use cases for everydaay developer: funnel analytics, behavioral analytics, exploring unstructured data.
The other part will be about BigQuery ML, which enables users to create and execute machine learning models in BigQuery using standard SQL queries. BigQuery ML democratizes machine learning by enabling SQL practitioners to build models using existing SQL tools and skills. BigQuery ML increases development speed by eliminating the need to move data.
Vibe Koli 2019 - Utazás az egyetem padjaitól a Google Developer ExpertigMárton Kodok
VIBE Koli 2019 - Vibe Garázs - Gokart.
Kodok Márton, miután elvégezte tanulmányait a Sapientián, IT-s karriert épített ki magának, ma pedig már tagja a Google Developer Expert (GDE) csapatának, így az ország kiemelkedő szakemberei közé tartozik. A VIBE Kolin abban segít neked, hogy megtaláld a saját utad. Bebizonyítja, csak akaraterő kell ahhoz, hogy egy társadhoz képest mást, többet csinálj.
BigQuery ML - Machine learning at scale using SQLMárton Kodok
With BigQuery ML, you can build machine learning models without leaving the data warehouse environment and training it on massive datasets. We are going to demonstrate how to build, train, eval and predict, your own scalable machine learning models using standard SQL language in Google BigQuery.
We will see how can we use CREATE MODEL sql syntax to build different models such as:
Linear regression
Multiclass logistic regression for classification
K-means clustering
Import TensorFlow models for prediction in BigQuery
We will see how we can apply these models on tabular data in retail and marketing use cases.
Models are trained and accessed in BigQuery using SQL — a language data analysts know. This enables business decision making through predictive analytics across the organization without leaving the query editor.
Google Cloud Platform Solutions for DevOps EngineersMárton Kodok
learn the DevOps essentials about cloud components, FaaS, PaaS architectural patterns that make use of Cloud Functions, Pub/Sub, Dataflow, Kubernetes and how we develop and deploy cloud software. You will get hands on information how to build, run, monitor highly scalable and flexible applications optimized to run on GCP. We will discuss cloud concepts and highlights various design patterns and best practices.
GDG DevFest Romania - Architecting for the Google Cloud PlatformMárton Kodok
Learn about FaaS, PaaS architectural patterns that make use of Cloud Functions, Pub/Sub, Dataflow, Kubernetes and platforms that hides the management of servers from the user and have changed how we develop and deploy future software.
We discuss the difference between an event-driven approach - this means that you can trigger a function whenever something interesting happens within the cloud environment - and the simpler HTTP approach. Quota and pricing of per invocation, and the advantages and disadvantages of the serverless systems.
6. DISZ - Webalkalmazások skálázhatósága a Google Cloud PlatformonMárton Kodok
Az előadás témája hogyan építhető fel egy rugalmas, jól skálázható szolgáltatás a felhőszolgáltatók platformjain. Hogyan lehet megoldani, hogy a szolgáltatás, amelynek induláskor legfeljebb néhány tíz vagy száz felhasználót kell kiszolgálnia, akár több ezer vagy nagyságrendekkel több felhasználót is képes legyen kiszolgálni rugalmasan? Hátradőlni és csodálni az autoscaling funkciót a Black Friday napján. Beszélni fogunk virtualizációról, platformszintű virtualizációről, szuperkönnyű alkalmazáskonténerekről, a munkaterhek közel valósidejű “pakolgatásával”. Bemutatásra kerül a Google Cloud Platform számos komponense. Bankok, biztosítók, webshopok és így tovább mind a cloudban látják a kitörési pontot.
GDG Heraklion - Architecting for the Google Cloud PlatformMárton Kodok
Learn about cloud components, architecture overviews to build an app using GCP components.
You will get hands-on information on how to build highly scalable and flexible applications optimized to run in GCP on the same infrastructure that powers Google. We will discuss cloud concepts and highlights various design patterns and best practices.
By the end of the session you will have hands-on experience to build a basic cloud application, it could be a simple web tier, powered by highly distributed database, background tasks executed on a pub/subsystem, and you get information how to go next level with advanced concepts like analytics warehouse, recommendation engines, and ML.
Code reviews are vital for ensuring good code quality. They serve as one of our last lines of defense against bugs and subpar code reaching production.
Yet, they often turn into annoying tasks riddled with frustration, hostility, unclear feedback and lack of standards. How can we improve this crucial process?
In this session we will cover:
- The Art of Effective Code Reviews
- Streamlining the Review Process
- Elevating Reviews with Automated Tools
By the end of this presentation, you'll have the knowledge on how to organize and improve your code review proces
Why React Native as a Strategic Advantage for Startup Innovation.pdfayushiqss
Do you know that React Native is being increasingly adopted by startups as well as big companies in the mobile app development industry? Big names like Facebook, Instagram, and Pinterest have already integrated this robust open-source framework.
In fact, according to a report by Statista, the number of React Native developers has been steadily increasing over the years, reaching an estimated 1.9 million by the end of 2024. This means that the demand for this framework in the job market has been growing making it a valuable skill.
But what makes React Native so popular for mobile application development? It offers excellent cross-platform capabilities among other benefits. This way, with React Native, developers can write code once and run it on both iOS and Android devices thus saving time and resources leading to shorter development cycles hence faster time-to-market for your app.
Let’s take the example of a startup, which wanted to release their app on both iOS and Android at once. Through the use of React Native they managed to create an app and bring it into the market within a very short period. This helped them gain an advantage over their competitors because they had access to a large user base who were able to generate revenue quickly for them.
TROUBLESHOOTING 9 TYPES OF OUTOFMEMORYERRORTier1 app
Even though at surface level ‘java.lang.OutOfMemoryError’ appears as one single error; underlyingly there are 9 types of OutOfMemoryError. Each type of OutOfMemoryError has different causes, diagnosis approaches and solutions. This session equips you with the knowledge, tools, and techniques needed to troubleshoot and conquer OutOfMemoryError in all its forms, ensuring smoother, more efficient Java applications.
Modern design is crucial in today's digital environment, and this is especially true for SharePoint intranets. The design of these digital hubs is critical to user engagement and productivity enhancement. They are the cornerstone of internal collaboration and interaction within enterprises.
Your Digital Assistant.
Making complex approach simple. Straightforward process saves time. No more waiting to connect with people that matter to you. Safety first is not a cliché - Securely protect information in cloud storage to prevent any third party from accessing data.
Would you rather make your visitors feel burdened by making them wait? Or choose VizMan for a stress-free experience? VizMan is an automated visitor management system that works for any industries not limited to factories, societies, government institutes, and warehouses. A new age contactless way of logging information of visitors, employees, packages, and vehicles. VizMan is a digital logbook so it deters unnecessary use of paper or space since there is no requirement of bundles of registers that is left to collect dust in a corner of a room. Visitor’s essential details, helps in scheduling meetings for visitors and employees, and assists in supervising the attendance of the employees. With VizMan, visitors don’t need to wait for hours in long queues. VizMan handles visitors with the value they deserve because we know time is important to you.
Feasible Features
One Subscription, Four Modules – Admin, Employee, Receptionist, and Gatekeeper ensures confidentiality and prevents data from being manipulated
User Friendly – can be easily used on Android, iOS, and Web Interface
Multiple Accessibility – Log in through any device from any place at any time
One app for all industries – a Visitor Management System that works for any organisation.
Stress-free Sign-up
Visitor is registered and checked-in by the Receptionist
Host gets a notification, where they opt to Approve the meeting
Host notifies the Receptionist of the end of the meeting
Visitor is checked-out by the Receptionist
Host enters notes and remarks of the meeting
Customizable Components
Scheduling Meetings – Host can invite visitors for meetings and also approve, reject and reschedule meetings
Single/Bulk invites – Invitations can be sent individually to a visitor or collectively to many visitors
VIP Visitors – Additional security of data for VIP visitors to avoid misuse of information
Courier Management – Keeps a check on deliveries like commodities being delivered in and out of establishments
Alerts & Notifications – Get notified on SMS, email, and application
Parking Management – Manage availability of parking space
Individual log-in – Every user has their own log-in id
Visitor/Meeting Analytics – Evaluate notes and remarks of the meeting stored in the system
Visitor Management System is a secure and user friendly database manager that records, filters, tracks the visitors to your organization.
"Secure Your Premises with VizMan (VMS) – Get It Now"
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.
Strategies for Successful Data Migration Tools.pptxvarshanayak241
Data migration is a complex but essential task for organizations aiming to modernize their IT infrastructure and leverage new technologies. By understanding common challenges and implementing these strategies, businesses can achieve a successful migration with minimal disruption. Data Migration Tool like Ask On Data play a pivotal role in this journey, offering features that streamline the process, ensure data integrity, and maintain security. With the right approach and tools, organizations can turn the challenge of data migration into an opportunity for growth and innovation.
In 2015, I used to write extensions for Joomla, WordPress, phpBB3, etc and I ...Juraj Vysvader
In 2015, I used to write extensions for Joomla, WordPress, phpBB3, etc and I didn't get rich from it but it did have 63K downloads (powered possible tens of thousands of websites).
In software engineering, the right architecture is essential for robust, scalable platforms. Wix has undergone a pivotal shift from event sourcing to a CRUD-based model for its microservices. This talk will chart the course of this pivotal journey.
Event sourcing, which records state changes as immutable events, provided robust auditing and "time travel" debugging for Wix Stores' microservices. Despite its benefits, the complexity it introduced in state management slowed development. Wix responded by adopting a simpler, unified CRUD model. This talk will explore the challenges of event sourcing and the advantages of Wix's new "CRUD on steroids" approach, which streamlines API integration and domain event management while preserving data integrity and system resilience.
Participants will gain valuable insights into Wix's strategies for ensuring atomicity in database updates and event production, as well as caching, materialization, and performance optimization techniques within a distributed system.
Join us to discover how Wix has mastered the art of balancing simplicity and extensibility, and learn how the re-adoption of the modest CRUD has turbocharged their development velocity, resilience, and scalability in a high-growth environment.
SOCRadar Research Team: Latest Activities of IntelBrokerSOCRadar
The European Union Agency for Law Enforcement Cooperation (Europol) has suffered an alleged data breach after a notorious threat actor claimed to have exfiltrated data from its systems. Infamous data leaker IntelBroker posted on the even more infamous BreachForums hacking forum, saying that Europol suffered a data breach this month.
The alleged breach affected Europol agencies CCSE, EC3, Europol Platform for Experts, Law Enforcement Forum, and SIRIUS. Infiltration of these entities can disrupt ongoing investigations and compromise sensitive intelligence shared among international law enforcement agencies.
However, this is neither the first nor the last activity of IntekBroker. We have compiled for you what happened in the last few days. To track such hacker activities on dark web sources like hacker forums, private Telegram channels, and other hidden platforms where cyber threats often originate, you can check SOCRadar’s Dark Web News.
Stay Informed on Threat Actors’ Activity on the Dark Web with SOCRadar!
Cyaniclab : Software Development Agency Portfolio.pdfCyanic lab
CyanicLab, an offshore custom software development company based in Sweden,India, Finland, is your go-to partner for startup development and innovative web design solutions. Our expert team specializes in crafting cutting-edge software tailored to meet the unique needs of startups and established enterprises alike. From conceptualization to execution, we offer comprehensive services including web and mobile app development, UI/UX design, and ongoing software maintenance. Ready to elevate your business? Contact CyanicLab today and let us propel your vision to success with our top-notch IT solutions.
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.
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1. Vertex AI
Pipelines for your MLOps workflows
GDG DevFest, November 2021
Márton Kodok
Google Developer Expert at REEA.net
2. ● Among the Top3 romanians on Stackoverflow 195k reputation
● Google Developer Expert on Cloud technologies
● Crafting Web/Mobile backends at REEA.net
● BigQuery + Redis database engine expert
Slideshare: martonkodok
Articles: martonkodok.medium.com
Twitter: @martonkodok
StackOverflow: pentium10
GitHub: pentium10
Vertex AI: Pipelines for your MLOps workflows @martonkodok
About me
3. 1. What is MLOps?
2. What is Vertex AI?
3. Build, train and deploy ML solutions
4. Using Pipelines throughout your ML workflow
5. Adapting to changes of data
6. Conclusions
Agenda
Vertex AI: Pipelines for your MLOps workflows @martonkodok
8. MLOps level 0: Manual process
MLOps level 1: ML pipeline automation
MLOps level 2: CI/CD pipeline automation
Levelsofautomation defines maturity of theMLprocess
@martonkodok
9. MLOps level 0: Manual process - Process for building and deploying ML models is entirely manual.
Infrequent release iterations. No CI, No CD. Disconnection between ML and operations.
MLOps level 1: ML pipeline automation - Continuous training of the model by automating the ML pipeline;
achieve continuous delivery of model prediction service. New pipelines mostly based on new data.
MLOps level 2: CI/CD pipeline automation -iteratively try out new ML algorithms and new modeling where
the experiment steps are orchestrated. The output of this stage is the source code of the ML pipeline steps
that are then pushed to a source repository. Build source. Run test. Output is pipeline.
Levelsofautomation defines maturity of theMLprocess
@martonkodok
13. “VertexAI is a managed ML platform for practitioners
to accelerate experiments and deploy AI models.
Vertex AI: Pipelines for your MLOps workflows @martonkodok
14. What’s included in VertexAI?
Vertex AI: Pipelines for your MLOps workflows @martonkodok
Data Labeling
AutoML
DL Environment (DL VM + DL Container)
Prediction
Feature Store Training
Experiments
Data Readiness
Feature
Engineering
Training/
HP-Tuning
Model
Monitoring
Model serving
Understanding/
Tuning
Edge
Model
Management
Notebooks
Pipelines (Orchestration)
Explainable AI
Hybrid AI
Model
Monitoring
Metadata
Vision Translation Tables
Language
Video
AI Accelerators
Models
Datasets
Custom Models
Containers
Python
Endpoints
Vizier Optimization
15. VertexAI is a unified MLOps platform
Vertex AI: Pipelines for your MLOps workflows @martonkodok
Operational
Model
Programming
Model
No Infra Management Managed Security Pay only for usage
Model-as-a-service
oriented
Streamlined model
development
Open SDKs,
integrates with ML frameworks
17. VertexAI: Pipelines - Orchestrate your ML workflow
Vertex AI: Pipelines for your MLOps workflows @martonkodok
Data Labeling
AutoML
DL Environment (DL VM + DL Container)
Prediction
Feature Store Training
Experiments
Data Readiness
Feature
Engineering
Training/
HP-Tuning
Model
Monitoring
Model serving
Understanding/
Tuning
Edge
Model
Management
Notebooks
Pipelines (Orchestration)
Explainable AI
Hybrid AI
Model
Monitoring
Metadata
Vision Translation Tables
Language
Video
AI Accelerators
Models
Datasets
Custom Models
Containers
Python
Endpoints
Vizier Optimization
18. “ Why are MLpipelines useful?
Vertex AI: Pipelines for your MLOps workflows @martonkodok
19. 1. Orchestrate ML workflow steps as a process.
We no longer handle all data gathering, model training, tuning, evaluation, deployment as a monolith.
2. Adopt MLOps for production models. We need a repeatable, verifiable, and automatic process for
making any change to a production model.
3. Develop steps independently -as you scale out, enables you to share your ML workflow with others on
your team, so they can run it, and contribute code. Enablesyoutotracktheinputandoutputfromeach
stepinareproducibleway.
Why are ML pipelines useful?
@martonkodok
20. Vertex AI: Pipelines
Vertex AI: Pipelines for your MLOps workflows
Source: Piero Esposito
https://github.com/piEsposito/vertex-ai-tutorials
21. Using Pipelines throughout your ML workflow
Vertex AI: Pipelines for your MLOps workflows @martonkodok
Gather data Train model
Deploy
model
22. Pipeline Components
Vertex AI: Pipelines for your MLOps workflows
pipeline_components_automl_images.ipynb
github.com/GoogleCloudPlatform/vertex-ai-samples
23. Using Pipelines throughout your ML workflow
Vertex AI: Pipelines for your MLOps workflows @martonkodok
Gather data Train model
Evaluate
model
Scalably
deploy
model
24. Pipeline SDK: Condition
Vertex AI: Pipelines for your MLOps workflows
automl_tabular_classification_beans.ipynb
github.com/GoogleCloudPlatform/vertex-ai-samples
25. 1. Use of the Google Cloud Pipeline Components, which support easy access to Vertex AI services
2. Custom Components - function that compiles to a task ‘factory’ function that can be used by pipelines
3. No more Kubeflow Pipelines that must be deployed on a Kubernetes Cluster.
4. Sharing component specifications - the YAML format allows the component to be put under version
control and shared with others, or be used by other pipelines by calling the load_from_url function.
5. Leveraging Pipeline step caching to develop and debug
6. Vertex AI Metadata service + Artifacts Lineage tracking - inverse of pipeline DAG
Developer friendly components
@martonkodok
26. Recap
Vertex AI: Pipelines for your MLOps workflows @martonkodok
Data Labeling
AutoML
DL Environment (DL VM + DL Container)
Prediction
Feature Store Training
Experiments
Data Readiness
Feature
Engineering
Training/
HP-Tuning
Model
Monitoring
Model serving
Understanding/
Tuning
Edge
Model
Management
Notebooks
Pipelines (Orchestration)
Explainable AI
Hybrid AI
Model
Monitoring
Metadata
Vision Translation Tables
Language
Video
AI Accelerators
Models
Datasets
Custom Models
Containers
Python
Endpoints
Vizier Optimization
28. Automatic CI / CD Perspective with GCP Services
Vertex AI: Pipelines for your MLOps workflows @martonkodok
Eventarc
• Detect changes on data
• React to events from Cloud services
• Handle events on Cloud Workflows,
Cloud Functions, Cloud Run
• Reuse pipeline spec.json from GCS
• Trigger Vertex AI pipeline
• Detect changes in codebase
• Build pipeline
• Pipeline spec.json to Cloud Storage
• Image to Cloud Registry
• Trigger Vertex AI pipeline
Cloud Build
Cloud Scheduler
• Poll for changes of any data
• Launch based on schedule
• In tandem with Cloud Workflows
• Trigger Vertex AI pipeline
30. 1. Build with the groundbreaking ML tools that power Google
2. Approachable from the non-ML developer perspective (AutoML, managed models, training)
3. Ease the life of a data scientist/ML (has feature store, managed datasets, endpoints, notebooks)
4. End-to-end integration for data and AI with build pipelines that outperform and solve complex ML tasks
5. GitOps-style continuous delivery with Cloud Build
6. Explainable AI and TensorBoard to visualize and track ML experiments
Vertex AI: Enhanced developer experience
Vertex AI: Pipelines for your MLOps workflows @martonkodok
31. Thank you. Q&A.
Slides available on:
slideshare.net/martonkodok
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