MLOps for production-level machine learning
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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
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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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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.
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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
Pythonsevilla2019 - Introduction to MLFlow
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AI & BigData Online Day 2023 Spring
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Rsqrd AI: From R&D to ROI of AI
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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.
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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.
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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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2. How artificial intelligence has been adopted across different business domains to automate tasks, gain insights from data, and improve customer experiences. Some challenges to AI adoption are also outlined.
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Abstract:
It’s already challenging to transition a machine learning model or AI system from the research space to production, and maintaining that system alongside ever-changing data is an even greater challenge. In software engineering, continuous delivery practices have been developed to ensure that developers can adapt, maintain, and update software and systems cheaply and quickly, enabling release cycles on the scale of hours or days instead of weeks or months.
Nevertheless, in the data science world, continuous delivery is rarely applied holistically—due in part to different workflows: data scientists regularly work on whole sets of hypotheses, whereas software engineers work more linearly even when evaluating multiple implementation alternatives. Therefore, existing software engineering practices cannot be applied as is to machine learning projects.
Arif Wider and Emily Gorcenski explore continuous delivery (CD) for AI/ML along with case studies for applying CD principles to data science workflows. Join in to learn how they drew on their expertise to adapt practices and tools to allow for continuous intelligence—the practice of delivering AI applications continuously.
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Configure and execute continuous training and continuous deployment for ML
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Watch the full presentation with video and audio here: https://info.cnvrg.io/monitor-machine-learning-model-workshop
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This presentation will offer solutions to the common challenges data scientists and data engineers face when building a machine learning pipeline.
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Data scientists want to do data science – and are slowed down by MLOps and DevOps tasks.
They lack user friendly tools needed to track experiments, attach resources, manage datasets and launch multiple ML pipelines.
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- How the NVIDIA DGX ecosystem with cnvrg.io makes GPU assets consumed easily, with one-click, bypassing complexity of MLOps
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piped drinking water supply from surface water sources
Day 1 - Lab 1 Reconnaissance Scanning with NMAP, Vulnerability Assessment wit...
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MLOps for production-level machine learning
- 2. whoami ● Solutions Architect @ cnvrg.io ● = built by data scientists, for data scientists to help teams: ○ Get from data to models to production in the most efficient and fast way ○ Bridge science and engineering ○ Automate MLOps
- 3. def agenda(30 mins): ● Introduction to MLOps ● What can MLOps do? ● A world without MLOps ● Elements of an MLOps solution ● MLOps with cnvrg.io
- 4. MLOps.intro() ● Is it a practice? A job? A platform? ● Collaboration & communication between data science & engineering professionals ● Stabilizes pipeline for entire ML lifecycle ● Create a real Machine Learning Cycle ● Ease friction at every point of the pipeline Research Data ExplorationDeployment Training
- 5. if not MLops: + Not all data scientists come from a DevOps background and are not always following best practices + Enterprise ML development is slow & hard to standardize/scale + So many Tools & Frameworks = Many delays in process = Friction between teams = Wasted resources = Tough to iterate on models = No standardization in industry
- 6. if MLOps == True: ● Deploy faster, easier and more often ● Centralize model tracking, versioning and monitoring ● Integrate different technologies together ● Reduce friction between science and engineering ○ Enhance collaboration ● Move towards standardization in the field ● Support continual learning with MLOps (CI/CD) ○ Webinar on CI/CD for Machine Learning
- 7. MLOps.elements() 1. Data management 2. Collaboration & communication 3. Model tracking, version and management 4. One-click experiment execution and model deployment 5. Unifies team no matter what language, framework, or provider 6. Features to assist every role in the team a. Data Scientist b. Engineer c. Business Managers 7. Continual learning - the automatic deployment and retraining of models IN production
- 8. MLOps.now() ● Many half-baked solutions ● Very early open-source technologies ● Requires deep know how to even implement
- 9. cnvrg.demo()
- 10. webinar.summary() ● Data Science is a quickly advancing field ● The lifecycle is inefficient within most firms ● MLOps is a still developing field that should be quickly adopted ● As a community we need to invest in standardizing MLOps ● You can incorporate MLOps with open source tools or full platform solutions
- 12. Next webinar!