Ml ops deployment choices
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How to choose correct framework and define your manifesto for technology practices around Machine Learning Journey. Kubernetes being successor in this space, Seldom Core and Kubeflow is truly winner in this Segment.
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Ml ops intro session
ML Ops case study document which talks about How ML pipelines are implemented and how to choose and implement Continuous Data 4 ML Pipelines.
Using MLOps to Bring ML to Production/The Promise of MLOps
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
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Apache Liminal is an end-to-end platform for data engineers & scientists, allowing them to build, train and deploy machine learning models in a robust and agile way. The platform provides the abstractions and declarative capabilities for data extraction & feature engineering followed by model training and serving; using standard tools and libraries (e.g. Airflow, K8S, Spark, scikit-learn, etc.).
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MLOps itself is a derivative of DevOps, the thought being that there is an entire industry that exists for “Ops” for normal software, and that such an industry will need to emerge for ML as well. But it hasn’t yet. Various technologies has made it easy for people to build predictive models, so people have lots of predictive models now. But to get value out of models you have to deploy, monitor, and maintain them. Very few people know how to do this, even fewer than know how to build a good model in the first place.
This talk will be dedicated to the plans of what is MLOps, what is cases and how it will develop and evolve into a new industry.
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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.
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What MLOps is and why it is crucial for the efficiency of your ML developments.
Kyrylo Perevozchykov "Continuous delivery for Machine Learning, the future of...
MLOps itself is a derivative of DevOps, the thought being that there is an entire industry that exists for “Ops” for normal software, and that such an industry will need to emerge for ML as well. But it hasn’t yet. Various technologies has made it easy for people to build predictive models, so people have lots of predictive models now. But to get value out of models you have to deploy, monitor, and maintain them. Very few people know how to do this, even fewer than know how to build a good model in the first place.
This talk will be dedicated to the plans of what is MLOps, what is cases and how it will develop and evolve into a new industry.
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Talk @ AWS Loft Stockholm, 23/10/2018
But why?
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Open Source tools: AWS Chalice, Serverless Framework
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Resources
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The DevOps landscape is well-understood and tools can be categorised by how they support the dev-build-deploy-monitor workflow. By comparison the MLOps landscape is complex and hard to understand. This presentation looks at the ML workflow that MLOps supports so that we can better understand the MLOps landscape.
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The structure of a Machine Learning code base can have a large impact on effective collaboration and time to production.
In this talk I will present our solution developed for the FutureOps Matching Automation project and talk about lessons learned and best practices.MLOps - The Assembly Line of ML
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.
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Looking to implement MLOps using AWS services and Kubeflow? Come and learn about machine learning from the experts of Provectus and Amazon Web Services (AWS)!
Businesses recognize that machine learning projects are important but go beyond just building and deploying models, which is mostly done by organizations. Successful ML projects entail a complete lifecycle involving ML, DevOps, and data engineering and are built on top of ML infrastructure.
AWS and Amazon SageMaker provide a foundation for building infrastructure for machine learning while Kubeflow is a great open source project, which is not given enough credit in the AWS community. In this webinar, we show how to design and build an end-to-end ML infrastructure on AWS.
Agenda
- Introductions
- Case Study: GoCheck Kids
- Overview of AWS Infrastructure for Machine Learning
- Provectus ML Infrastructure on AWS
- Experimentation
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- Feature Store
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Presenters
- Stepan Pushkarev, Chief Technology Officer, Provectus
- Qingwei Li, ML Specialist Solutions Architect, AWS
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-mlops-and-reproducible-ml-on-aws-with-kubeflow-and-sagemaker-aug-2020/
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* Introduction to talk
* What is MLOps?
* Building an MLOps Pipeline
* Real-world Simulations
* Let’s fix the pipeline
* Wrap-up
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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.
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- Challenges and solutions of Data Testing
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- How to expand validation pipelines for Data Quality
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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.
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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.
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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.
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Microsoft Translator currently supports 100+ languages. We constantly improve the translation quality, add new scenarios, all with a constant team size. This session describes a production scale machine learning architecture using MS Translator as a case study. You will learn the mental model to approach your ML problem and concrete Do’s and Don’ts for the various components of the ML system architecture.
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Starting with outlining the history of conventional version control before diving into explaining QoDs (Quantitative Oriented Developers) and the unique problems their ML systems pose from an operations perspective (MLOps). With the only status quo solutions being proprietary in-house pipelines (exclusive to Uber, Google, Facebook) and manual tracking/fragile "glue" code for everyone else.
Datmo works to solve this issue by empowering QoDs in two ways: making MLOps manageable and simple (rather than completely abstracted away) as well as reducing the amount of glue code so to ensure more robust pipelines.
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Model Inferencing use cases are becoming a requirement for models moving into the next phase of production deployments. More and more users are now encountering use cases around canary deployments, scale-to-zero or serverless characteristics. And then there are also advanced use cases coming around model explainability, including A/B tests, ensemble models, multi-armed bandits, etc.
In this talk, the speakers are going to detail how to handle these use cases using Kubeflow Serving and the native Kubernetes stack which is Istio and Knative. Knative and Istio help with autoscaling, scale-to-zero, canary deployments to be implemented, and scenarios where traffic is optimized to the best performing models. This can be combined with KNative eventing, Istio observability stack, KFServing Transformer to handle pre/post-processing and payload logging which consequentially can enable drift and outlier detection to be deployed. We will demonstrate where currently KFServing is, and where it's heading towards.
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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.
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Proof of Concept to solve problems for On-Premise Edge Device and How they can hookup around AWS IoT core and Services
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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.
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Looking to implement MLOps using AWS services and Kubeflow? Come and learn about machine learning from the experts of Provectus and Amazon Web Services (AWS)!
Businesses recognize that machine learning projects are important but go beyond just building and deploying models, which is mostly done by organizations. Successful ML projects entail a complete lifecycle involving ML, DevOps, and data engineering and are built on top of ML infrastructure.
AWS and Amazon SageMaker provide a foundation for building infrastructure for machine learning while Kubeflow is a great open source project, which is not given enough credit in the AWS community. In this webinar, we show how to design and build an end-to-end ML infrastructure on AWS.
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- Overview of AWS Infrastructure for Machine Learning
- Provectus ML Infrastructure on AWS
- Experimentation
- MLOps
- Feature Store
Intended Audience
Technology executives & decision makers, manager-level tech roles, data engineers & data scientists, ML practitioners & ML engineers, and developers
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- Stepan Pushkarev, Chief Technology Officer, Provectus
- Qingwei Li, ML Specialist Solutions Architect, AWS
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-mlops-and-reproducible-ml-on-aws-with-kubeflow-and-sagemaker-aug-2020/
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* Introduction to talk
* What is MLOps?
* Building an MLOps Pipeline
* Real-world Simulations
* Let’s fix the pipeline
* Wrap-up
MLOps Bridging the gap between Data Scientists and Ops.
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.
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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
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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.
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Agile Machine Learning for Real-time Recommender Systems
These are slides presented at MLconf in San Francisco, November 14, 2014. I share the approach to real-time machine learning for recommender systems developed at if(we). We achieve rapid iterative cycles by adhering to a strict approach to structuring and accessing our data, as well as to building the online features that comprise our models. These developments support teams of data scientist and data engineers, who work together to solve complex recommendation problems. We also introduce the Antelope Realtime Events framework, an open source demonstration application which derives from our scalable proprietary software stack.
Shortening the time from analysis to deployment with ml as-a-service — Luiz A...
The daily job of a Data Scientist ranges from a variety of tasks: improving models performance or dealing with framework structure implementations. Machine learning as a service, a hot topic in the field, implies thinking about architecture to allow constant improvements in performance for our products. This presentation shows one architecture design using RESTful resources, document oriented databases and pre-trained pipelines to achieve real-time predictions of time series with high availability, scalability and freedom to Data Scientists work directly on improving the accuracy rate of our products. We fine tunned to work on time series forecasting which is a very challenging field that still needs better solutions in terms of innovative modeling. During the presentation will be shown how these decisions keep our Data Scientists focused on working with real data and thinking about improvements that can reach a large volume of time series instead of singular and localized actions.
Tech leaders guide to effective building of machine learning products
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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.
Machine Learning system architecture – Microsoft Translator, a Case Study : ...
Microsoft Translator currently supports 100+ languages. We constantly improve the translation quality, add new scenarios, all with a constant team size. This session describes a production scale machine learning architecture using MS Translator as a case study. You will learn the mental model to approach your ML problem and concrete Do’s and Don’ts for the various components of the ML system architecture.
Version Control in AI/Machine Learning by Datmo
Starting with outlining the history of conventional version control before diving into explaining QoDs (Quantitative Oriented Developers) and the unique problems their ML systems pose from an operations perspective (MLOps). With the only status quo solutions being proprietary in-house pipelines (exclusive to Uber, Google, Facebook) and manual tracking/fragile "glue" code for everyone else.
Datmo works to solve this issue by empowering QoDs in two ways: making MLOps manageable and simple (rather than completely abstracted away) as well as reducing the amount of glue code so to ensure more robust pipelines.
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Model Inferencing use cases are becoming a requirement for models moving into the next phase of production deployments. More and more users are now encountering use cases around canary deployments, scale-to-zero or serverless characteristics. And then there are also advanced use cases coming around model explainability, including A/B tests, ensemble models, multi-armed bandits, etc.
In this talk, the speakers are going to detail how to handle these use cases using Kubeflow Serving and the native Kubernetes stack which is Istio and Knative. Knative and Istio help with autoscaling, scale-to-zero, canary deployments to be implemented, and scenarios where traffic is optimized to the best performing models. This can be combined with KNative eventing, Istio observability stack, KFServing Transformer to handle pre/post-processing and payload logging which consequentially can enable drift and outlier detection to be deployed. We will demonstrate where currently KFServing is, and where it's heading towards.
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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.
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Kubecon 2023 EU - KServe - The State and Future of Cloud-Native Model Serving
Kubecon 2023 EU - KServe - The State and Future of Cloud-Native Model ServingTheofilos Papapanagiotou
KServe is a cloud-native open source project for serving production ML models built on CNCF projects like Knative and Istio. In this talk, we’ll update you on KServe’s progress towards 1.0, the latest developments, such as ModelMesh and InferenceGraph, and its future roadmap. We’ll discuss the Kubernetes design patterns used in KServe to achieve the core ML inference capability, as well as the design philosophy behind KServe and how it integrates the CNCF ecosystem so you can walk up and down the stack to use features to meet your production model deployment requirements. The well-designed InferenceService interface encapsulates the complexity of networking, lifecycle, server configurations and allows you to easily add serverless capabilities to model servers like TensorFlow Serving, TorchServe, and Triton on CPU/GPU. You can also turn on full service mesh mode to secure your InferenceServices. We’ll walk through different scenarios to show how you can quickly start with KServe and evolve to a production-ready setup with scalability, security, observability, and auto-scaling acceleration using CNCF projects like Knative, Istio, SPIFFE/SPIRE, OpenTelemetry, and Fluid.Serverless Functions and Machine Learning: Putting the AI in APIs
Machine Learning has become an integral part of all major apps. From face recognition to product recommender engines, emotion detection to automated analytics. Every product you touch contains, or can benefit from, AI — so why is it still so difficult to identify, tune, and integrate Machine Learning?
We’ll investigate a number of approaches to this problem, from off-the-shelf APIs to options for training and hosting your own ML models. You’ll walk away ready to hook thousands of different ready-to-run models into your app, or to productionize your own models in an on-demand, autoscaled, language-agnostic environment.
Clipper at UC Berkeley RISECamp 2017
Clipper: http://clipper.ai
RISE Camp: https://risecamp.berkeley.edu/
Machine Learning para devs com ML.NET
Vamos explorar como podemos utilizar aprendizagem de máquina, de forma fácil, nas aplicações que desenvolvemos no dia a dia utilizando nossas habilidades em .NET através do ML.NET, um framework open source e cross-platform!
Introducing MlFlow: An Open Source Platform for the Machine Learning Lifecycl...
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.
Scaling AI/ML with Containers and Kubernetes
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.
Vertex AI: Pipelines for your MLOps workflows
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 standard 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.
Deploying and Monitoring Heterogeneous Machine Learning Applications with Cli...
Machine learning is being deployed in a growing number of applications which demand real-time, accurate, and robust predictions under heavy serving loads. However, most machine learning frameworks and systems only address model training and not deployment.
Clipper is an open-source, general-purpose model-serving system that addresses these challenges. Interposing between applications that consume predictions and the machine-learning models that produce predictions, Clipper simplifies the model deployment process by adopting a modular serving architecture and isolating models in their own containers, allowing them to be evaluated using the same runtime environment as that used during training. Clipper’s modular architecture provides simple mechanisms for scaling out models to meet increased throughput demands and performing fine-grained physical resource allocation for each model. Further, by abstracting models behind a uniform serving interface, Clipper allows developers to compose many machine-learning models within a single application to support increasingly common techniques such as ensemble methods, multi-armed bandit algorithms, and prediction cascades.
In this talk I will provide an overview of the Clipper serving system and discuss how to get started using Clipper to serve Apache Spark and TensorFlow models on Kubernetes. I will then discuss some recent work on statistical performance monitoring for machine learning models.
Microservices with Azure Service Fabric
Microservices have become a trendy development strategy. Hosting and running such services used to be pretty painful… but here it comes Service Fabric! Let's take a closer look at this platform, it's different development models and all the features it offers. And not only for microservices!
Portable Streaming Pipelines with Apache Beam
Speaker: Frances Perry, Google
Kafka Summit NYC 2017 - Streams track
OS for AI: Elastic Microservices & the Next Gen of ML
AI has been a hot topic lately, with advances being made constantly in what is possible, there has not been as much discussion of the infrastructure and scaling challenges that come with it. How do you support dozens of different languages and frameworks, and make them interoperate invisibly? How do you scale to run abstract code from thousands of different developers, simultaneously and elastically, while maintaining less than 15ms of overhead?
At Algorithmia, we’ve built, deployed, and scaled thousands of algorithms and machine learning models, using every kind of framework (from scikit-learn to tensorflow). We’ve seen many of the challenges faced in this area, and in this talk I’ll share some insights into the problems you’re likely to face, and how to approach solving them.
In brief, we’ll examine the need for, and implementations of, a complete “Operating System for AI” – a common interface for different algorithms to be used and combined, and a general architecture for serverless machine learning which is discoverable, versioned, scalable and sharable.
Clipper: A Low-Latency Online Prediction Serving System
Machine learning is being deployed in a growing number of applications which demand real-time, accurate, and robust predictions under heavy serving loads. However, most machine learning frameworks and systems only address model training and not deployment.
Clipper is a general-purpose model-serving system that addresses these challenges. Interposing between applications that consume predictions and the machine-learning models that produce predictions, Clipper simplifies the model deployment process by isolating models in their own containers and communicating with them over a lightweight RPC system. This architecture allows models to be deployed for serving in the same runtime environment as that used during training. Further, it provides simple mechanisms for scaling out models to meet increased throughput demands and performing fine-grained physical resource allocation for each model.
In this talk, I will provide an overview of the Clipper serving system and then discuss how to get started using Clipper to serve Spark and TensorFlow models in a production serving environment.
MLflow with Databricks
A basic mlflow introduction and how to work with databricks. The CI/CD workflow with Databricks is also introduced.
Mlflow with databricks
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.
2018 11 14 Artificial Intelligence and Machine Learning in Azure
Slides used during my session "Artificial Intelligence and Machine Learning in Azure" for The Azure Group (Canada's Azure User Community) on November 14 2018.
Public group
201908 Overview of Automated ML
Automated machine learning (automated ML) automates feature engineering, algorithm and hyperparameter selection to find the best model for your data. The mission: Enable automated building of machine learning with the goal of accelerating, democratizing and scaling AI. This presentation covers some recent announcements of technologies related to Automated ML, and especially for Azure. The demonstrations focus on Python with Azure ML Service and Azure Databricks.
Unleashing Apache Kafka and TensorFlow in Hybrid Cloud Architectures
Talk at Strate Conference in London: Unleashing Apache Kafka and TensorFlow in Hybrid Cloud Architectures with Confluent:
How do you leverage the flexibility and extreme scale of the public cloud and the Apache Kafka ecosystem to build scalable, mission-critical machine learning infrastructures that span multiple public clouds—or bridge your on-premises data centre to the cloud?
Join Kai Wähner to learn how to use technologies such as TensorFlow with Kafka’s open source ecosystem for machine learning infrastructures. You’ll learn how to build a scalable, mission-critical machine learning infrastructure for data ingestion and processing, model training, deployment, and monitoring.
The discussed architecture includes capabilities like scalable data preprocessing for training and predictions, a combination of different deep learning frameworks, data replication between data centers, intelligent real-time microservices running on Kubernetes, and local deployment of analytic models for offline predictions.
Learn how the public cloud allows extreme scale for building analytic models and how the Apache Kafka open source ecosystem enables building a cloud-independent infrastructure for preprocessing and ingestion of data and inference and monitoring of analytic models in real time
Understand why hybrid architectures and local model deployment are key for success in many scenarios and why you need a flexible machine learning architecture that supports different technologies and frameworks
Similar to Ml ops deployment choices (20)
Advanced Model Inferencing leveraging Kubeflow Serving, KNative and Istio
Advanced Model Inferencing leveraging Kubeflow Serving, KNative and Istio
End to end Machine Learning using Kubeflow - Build, Train, Deploy and Manage
End to end Machine Learning using Kubeflow - Build, Train, Deploy and Manage
Kubecon 2023 EU - KServe - The State and Future of Cloud-Native Model Serving
Kubecon 2023 EU - KServe - The State and Future of Cloud-Native Model Serving
Serverless Functions and Machine Learning: Putting the AI in APIs
Serverless Functions and Machine Learning: Putting the AI in APIs
Introducing MlFlow: An Open Source Platform for the Machine Learning Lifecycl...
Introducing MlFlow: An Open Source Platform for the Machine Learning Lifecycl...
Deploying and Monitoring Heterogeneous Machine Learning Applications with Cli...
Deploying and Monitoring Heterogeneous Machine Learning Applications with Cli...
OS for AI: Elastic Microservices & the Next Gen of ML
OS for AI: Elastic Microservices & the Next Gen of ML
Clipper: A Low-Latency Online Prediction Serving System
Clipper: A Low-Latency Online Prediction Serving System
2018 11 14 Artificial Intelligence and Machine Learning in Azure
2018 11 14 Artificial Intelligence and Machine Learning in Azure
Unleashing Apache Kafka and TensorFlow in Hybrid Cloud Architectures
Unleashing Apache Kafka and TensorFlow in Hybrid Cloud Architectures
Recently uploaded
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
Removing Uninteresting Bytes in Software Fuzzing
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
UiPath Test Automation using UiPath Test Suite series, part 6
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
FIDO Alliance Osaka Seminar: The WebAuthn API and Discoverable Credentials.pdf
FIDO Alliance Osaka Seminar
National Security Agency - NSA mobile device best practices
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
zkStudyClub - Reef: Fast Succinct Non-Interactive Zero-Knowledge Regex Proofs
This paper presents Reef, a system for generating publicly verifiable succinct non-interactive zero-knowledge proofs that a committed document matches or does not match a regular expression. We describe applications such as proving the strength of passwords, the provenance of email despite redactions, the validity of oblivious DNS queries, and the existence of mutations in DNA. Reef supports the Perl Compatible Regular Expression syntax, including wildcards, alternation, ranges, capture groups, Kleene star, negations, and lookarounds. Reef introduces a new type of automata, Skipping Alternating Finite Automata (SAFA), that skips irrelevant parts of a document when producing proofs without undermining soundness, and instantiates SAFA with a lookup argument. Our experimental evaluation confirms that Reef can generate proofs for documents with 32M characters; the proofs are small and cheap to verify (under a second).
Paper: https://eprint.iacr.org/2023/1886
Climate Impact of Software Testing at Nordic Testing Days
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Mind map of terminologies used in context of Generative AI
Mind map of common terms used in context of Generative AI.
The Art of the Pitch: WordPress Relationships and Sales
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024
Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
Generative AI Deep Dive: Advancing from Proof of Concept to Production
Join Maher Hanafi, VP of Engineering at Betterworks, in this new session where he'll share a practical framework to transform Gen AI prototypes into impactful products! He'll delve into the complexities of data collection and management, model selection and optimization, and ensuring security, scalability, and responsible use.
Pushing the limits of ePRTC: 100ns holdover for 100 days
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
ここ3000字までしか入らないけどタイトルの方がたくさん文字入ると思います。
Recently uploaded (20)
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf
UiPath Test Automation using UiPath Test Suite series, part 6
UiPath Test Automation using UiPath Test Suite series, part 6
GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...
GraphSummit Singapore | Graphing Success: Revolutionising Organisational Stru...
FIDO Alliance Osaka Seminar: The WebAuthn API and Discoverable Credentials.pdf
FIDO Alliance Osaka Seminar: The WebAuthn API and Discoverable Credentials.pdf
National Security Agency - NSA mobile device best practices
National Security Agency - NSA mobile device best practices
zkStudyClub - Reef: Fast Succinct Non-Interactive Zero-Knowledge Regex Proofs
zkStudyClub - Reef: Fast Succinct Non-Interactive Zero-Knowledge Regex Proofs
Climate Impact of Software Testing at Nordic Testing Days
Climate Impact of Software Testing at Nordic Testing Days
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...
Mind map of terminologies used in context of Generative AI
Mind map of terminologies used in context of Generative AI
The Art of the Pitch: WordPress Relationships and Sales
The Art of the Pitch: WordPress Relationships and Sales
GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024
GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024
GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...
GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...
Generative AI Deep Dive: Advancing from Proof of Concept to Production
Generative AI Deep Dive: Advancing from Proof of Concept to Production
Pushing the limits of ePRTC: 100ns holdover for 100 days
Pushing the limits of ePRTC: 100ns holdover for 100 days
Secstrike : Reverse Engineering & Pwnable tools for CTF.pptx
Secstrike : Reverse Engineering & Pwnable tools for CTF.pptx
みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
Ml ops deployment choices
- 1. ML Ops : To Deploy Manifesto toward ML Journey Prepared By : Avinash Patil
- 2. How to deploy Machine Learning Models Central Model Deployment? Do you need GPU ? Privacy concerns Distribute Predictions to clients How about when?? Choose various ML models Various ML scenarios ML Models
- 3. Model Server Batch Inferences, A/B testing Inferences on specific hardware
- 4. Model Server , API HTTP RequestsSimplest Architecture
- 5. Scalable Architecture Kubernetes:Container Orchestration A/B Testing Allows you to scale your APIs Useful for scalable deployments or multiple model versions
- 6. Model Server Flask Quick and dirty Cons: No scalability
- 7. TF Serving Provides separation between API Code and Models Easy Model Deployment Batching Consistent APIs (gRPC and REST) Support multiple model versions
- 8. Other Options Seldon : Scalable and framework agnostics Graphpipe : Deployment for mxnet, PyTorch models via ONNX MLFlow : Works with any ML library, language & existing code Simple TensorFlow Serving
- 9. Cloud Options Google AI Platform Azure ML AWS Sagemaker Cons :Becomes Cost prone in future.
- 10. Kubeflow Simple, Portable and Scalable Used Cases: 1. Deploying and managing a complex ML system at scale 2. Experimentation with training an ML model 3. End to end hybrid and multi-cloud ML workloads 4. Tuning the model hyperparameters during training 5. Continuous integration and deployment (CI/CD) for ML
- 11. Kubeflow : ML Toolkits
- 16. MLFlow Fully ML Lifecycle and Managed Solution
- 17. Tensorflow Lite ★ Deploy to clients browsers through Tensorflow.js ★ Deployable to your Edge devices, watches, mobile phones ★ ➔ Cons : Minimal TF ops, Models need to converted