What is MLOps
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A brief introduction to MLOps (machine learning operations). For more, check out our other MLOps materials on valohai.com/mlops
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MLOps by Sasha Rosenbaum
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From Data Science to MLOpsCarl W. Handlin The document discusses moving from data science to MLOps. It defines MLOps as extending DevOps methodology to include machine learning, data science, and data engineering assets. Key concepts of MLOps include iterative development, automation, continuous integration and delivery, versioning, testing, reproducibility, monitoring, source control, and model/feature stores. MLOps helps address challenges of moving models to production like the deployment gap by establishing best practices and tools for testing, deploying, managing, and monitoring models.
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https://dataphoenix.info/the-a-z-of-data/
https://dataphoenix.info/the-a-z-of-data-introduction-to-mlops/
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To watch the full presentation click here: https://info.cnvrg.io/mlopsformachinelearning
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Using MLOps to Bring ML to Production/The Promise of MLOps
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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
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MLflow: A Platform for Production Machine Learning
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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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- Introductions
- Case Study: GoCheck Kids
- 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
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 [email protected] 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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2) It offers a collaborative data science workspace for data engineers, data scientists, and ML engineers to work together on projects using notebooks.
3) The platform provides end-to-end governance for machine learning including experiment tracking, reproducibility, and model governance.
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Konstantinos Balafas, Head of AI Data, and Carlo Dal Mutto, Director of Engineering, both of Airbus, present the “MLOps: Managing Data and Workflows for Efficient Model Development and Deployment” tutorial at the May 2022 Embedded Vision Summit.
Machine learning operations (MLOps) is the engineering field focused on techniques for developing and deploying machine learning solutions at scale. As the name suggests, MLOps is a combination of machine learning development (“ML”) and software/IT operations (“Ops”). Blending these two words is particularly complex, given their diverse nature. ML development is characterized by research and experimental components, dealing with large amounts of data and complex operations, while software and IT operations aim at streamlining software deployment in products.
Typical problems addressed by MLOps include data management (labeling, organization, storage), ML model and pipeline training repeatability, error analysis, model integration and deployment and model monitoring. In this talk, Dal Mutto and Balafas present practical MLOps techniques useful for tackling a variety of MLOps needs. They illustrate these techniques with real-world examples from their work developing autonomous flying capabilities as part of the Wayfinder team at Acubed, the Silicon Valley innovation center of Airbus.
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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).
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- Data Quality and why it matters
- Challenges and solutions of Data Testing
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Managing the Complete Machine Learning Lifecycle with MLflow
Managing the Complete Machine Learning Lifecycle with MLflowDatabricks 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 solve for these challenges, Databricks unveiled last year MLflow, an open source project that aims at simplifying the entire ML lifecycle. 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 the past year, the MLflow community has grown quickly: over 120 contributors from over 40 companies have contributed code to the project, and over 200 companies are using MLflow.
In this tutorial, we will show you how using MLflow can help you:
Keep track of experiments runs and results across frameworks.
Execute projects remotely on to a Databricks cluster, and quickly reproduce your runs.
Quickly productionize models using Databricks production jobs, Docker containers, Azure ML, or Amazon SageMaker.
We will demo the building blocks of MLflow as well as the most recent additions since the 1.0 release.
What you will learn:
Understand the three main components of open source MLflow (MLflow Tracking, MLflow Projects, MLflow Models) and how each help address challenges of the ML lifecycle.
How to use MLflow Tracking to record and query experiments: code, data, config, and results.
How to use MLflow Projects packaging format to reproduce runs on any platform.
How to use MLflow Models general format to send models to diverse deployment tools.
Prerequisites:
A fully-charged laptop (8-16GB memory) with Chrome or Firefox
Python 3 and pip pre-installed
Pre-Register for a Databricks Standard Trial
Basic knowledge of Python programming language
Basic understanding of Machine Learning Concepts
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Introduction to MLflowDatabricks 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, I 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.
Introducing MlFlow: An Open Source Platform for the Machine Learning Lifecycl...
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.
MLFlow: Platform for Complete Machine Learning Lifecycle 
MLFlow: Platform for Complete Machine Learning Lifecycle Databricks Description
Data Science and ML development bring 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.
MLflow addresses some of these challenges during an ML model development cycle.
Abstract
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.
With a short demo, you see 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 Using tracking Python APIs during model training Using MLflow UI to visually compare and contrast experimental runs with different tuning parameters and evaluate metrics
MLOps with Azure DevOps
MLOps with Azure DevOpsMarco Parenzan MLOps refers to applying DevOps practices and principles to machine learning. This allows for machine learning models and projects to be developed and deployed using automated pipelines for continuous integration and delivery. MLOps benefits include making machine learning work reproducible and auditable, enabling validation of models, and providing observability through monitoring of models after deployment. MLOps uses the same development practices as software engineering to ensure quality control for machine learning.
Vertex AI: Pipelines for your MLOps workflows
Vertex AI: Pipelines for your MLOps workflowsMárton Kodok 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.
Mohamed Sabri: Operationalize machine learning with Kubeflow
Mohamed Sabri: Operationalize machine learning with KubeflowLviv Startup Club This document summarizes a hands-on workshop on Kubeflow Pipeline. The workshop will cover requirements, an introduction to the presenter Mohamed Sabri, and their approach of strategizing, shaping, and spreading knowledge. It then discusses operationalizing machine learning (MLOps) and provides an analysis, design, coaching, and implementation framework. Deliverables include an implemented MLOps environment, training sessions, design documents, and a recommendations roadmap. The rest of the document discusses MLOps architectures, challenges, example technologies and tools, a use case, and deployment workflows from notebooks to production.
Mohamed Sabri: Operationalize machine learning with Kubeflow
Mohamed Sabri: Operationalize machine learning with KubeflowEdunomica Data Science Online Camp 2021
Website - https://dscamp.org/
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To watch the full presentation click here: https://info.cnvrg.io/mlopsformachinelearning
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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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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
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- Overview of AWS Infrastructure for Machine Learning
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- Experimentation
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- Feature Store
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Presenters
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- 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 [email protected] 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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MLFlow: Platform for Complete Machine Learning Lifecycle Databricks Description
Data Science and ML development bring 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.
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Abstract
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.
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With a short demo, you see 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 Using tracking Python APIs during model training Using MLflow UI to visually compare and contrast experimental runs with different tuning parameters and evaluate metrics
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- Connect the application to ScyllaDB (NoSQL data store)
- Negotiate tradeoffs related to data modeling and querying
- Manage and monitor the database for consistently low latencies
What is Model Context Protocol(MCP) - The new technology for communication bw...
What is Model Context Protocol(MCP) - The new technology for communication bw...Vishnu Singh Chundawat
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What is MLOps
- 2. What ML development should look like
- 3. Design 10% Model Development 90% Glue Coding Complex & unstructured handover Operations Time to Market What it actually looks like
- 4. Why? ML Code Needed to prove value
- 5. Why? Because most of your ML system is not about the ML code. Data Verification Serving Infrastructure Configuration Data Collection Feature Extraction Process Management Tools Analysis Tools Monitoring Machine Resource Management ML Code Hidden Technical Debt in Machine Learning Systems (2015): https://papers.nips.cc/paper/2015/file/86df7dcfd896fcaf2674f757a2463eba-Paper.pdf Needed to materialize value
- 6. The goal of MLOps is to reduce technical friction to get the model from an idea into production in the shortest possible time with as little risk as possible.
- 7. MLOps is not a single tool or platform. Tooling
- 8. MLOps is about agreeing to do ML the right way and then supporting it. Process Tooling
- 9. Process A few shared principles will take you a long way… ML should be collaborative. ML should be reproducible. ML should be continuous. ML should be tested & monitored.
- 10. Tooling And tooling will help implement your process. ML should be collaborative. ML should be reproducible. ML should be continuous. ML should be tested & monitored. Shared Infrastructure Versioning for Code, Data and Metadata Machine Learning Pipelines Model Deployment and Monitoring
- 11. Time Risk Short-term thinking Long-term thinking Do it fast Do it safe Time Do it right So it’ll be faster next time Risk Avoid the “It’s too early for MLOps” trap.
- 12. Data analysis ML pipeline Model serving Model monitoring Experimentation Feature store Metadata store Model registry Code repository The MLOps Stack provides one way to think about what tooling you might need. Download template: valohai.com/mlops
- 13. Data analysis ML pipeline Model serving Model monitoring Experimentation Feature store Metadata store Model registry Code repository Considerations: Self-Managed Tools vs Managed Tools Point Solutions vs End-to-End Platforms Download template: valohai.com/mlops
- 14. The MLOps tools are aplenty… but so are the requirements. Self-driving car vs recommendation engine Citizen data scientists vs seasoned engineers Healthcare & fintech vs mobile gaming Bootstrapping vs well-funded Use Case Team & skills Regulation Other resources
- 15. MLOps should fix the development loop. Process Tooling
- 16. Check out more reading materials at valohai.com/mlops