CI/CD & Continuous Training in ML // Part 4 // MLOps Coffee Sessions #17
MLOps level 2: CI/CD pipeline automation
For a rapid and reliable update of the pipelines in production, you need a robust automated CI/CD system. This automated CI/CD system lets your data scientists rapidly explore new ideas around feature engineering, model architecture, and hyperparameters. They can implement these ideas and automatically build, test, and deploy the new pipeline components to the target environment.
Figure 4. CI/CD and automated ML pipeline.
This MLOps setup includes the following components:
Source control
Test and build services
Deployment services
Model registry
Feature store
ML metadata store
ML pipeline orchestrator
Characteristics of stages discussion.
Figure 5. Stages of the CI/CD automated ML pipeline.
The pipeline consists of the following stages:
Development and experimentation: You iteratively try out new ML algorithms and new modelling 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.
Pipeline continuous integration: You build source code and run various tests. The outputs of this stage are pipeline components (packages, executables, and artefacts) to be deployed in a later stage.
Pipeline continuous delivery: You deploy the artefacts produced by the CI stage to the target environment. The output of this stage is a deployed pipeline with the new implementation of the model.
Automated triggering: The pipeline is automatically executed in production based on a schedule or in response to a trigger. The output of this stage is a trained model that is pushed to the model registry.
Model continuous delivery: You serve the trained model as a prediction service for the predictions. The output of this stage is a deployed model prediction service.
Monitoring: You collect statistics on the model performance based on live data. The output of this stage is a trigger to execute the pipeline or to execute a new experiment cycle.
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Our 1st MLOps Meetup // Luke Marsden // MLOps Meetup #1
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Remote Collaboration as a Data Scientist
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MLOps Manifesto with Luke Marsden from Dotscience
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MLOps lifecycle description
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What Does Best in Class AI/ML Governance Look Like in Fin Services? // Charles Radclyffe // MLOps #2
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Life purpose and too many spreadsheets
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Explainability, Black boxes and EU white paper on reproducibility
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Hierarchy of Machine Learning Needs // Phil Winder // MLOps Meetup #3
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Automatically Retrain Machine Learning Models? Are best practices worth it?
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Building an MLOps Team? Key ideas to keep in mind
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Hierarchy of MLOps Needs
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Bare necessities for getting an ML model into production
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MLOps and Monitoring
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How Phil Winder got into Data Science and Software Engineering
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Provenance and Reproducibility in Machine Learning; what is it and why you need it?
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Friction Between Data Scientists and Software Engineers
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ML tooling in large companies
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ML Platforms - The build vs buy question
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ML Services Gateway at SurveyMonkey
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MLOps #4: Shubhi Jain - Building an ML Platform @SurveyMonkey
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Hybrid Data Science Teams @SurveyMonkey
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How do you handle ML version control at SurveyMonkey
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Doing ML with Personal Information
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Evolution of the ML feature store @SurveyMonkey
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Developing a Machine Learning Feature Store
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Auto retrain ML models is not the question
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3 key parts to Machine Learning monitoring
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MLOps Meetup #6: Mid-Scale Production Feature Engineering with Dr. Venkata Pingali
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MLOps meetup #5 High Stakes ML: Active Failures, Latent Factors with Flavio Clesio
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MLOps: Airflow Pros and Cons
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Specific challenges in Machine Learning
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Current State Of Machine Learning
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Humans in the Loop are a defining factor in Machine Learning
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Learning from real life Machine Learning failures
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Survivorship Bias in machine learning tutorials
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Swiss Cheese model in Machine Learning
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Resume driven development in Machine learning & software engineering
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Who has the highest standards in ML?
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Venkata Pingali of Scribble Data Thoughts on the Current State of Machine Learning
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Dependable data and being able to Trust in your Data with Venkata Pengali of Scribble Data
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Speed, Trust, Evolution and Scale in MLOps
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More difficult transition for data scientists to become ML engineers
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How many models in prod til I need a dedicated ML platform?
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3rd wave of data scientists
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MLOps meetup #7 Alex Spanos // TrueLayer 's MLOps Pipeline
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MLOps Meetup #8 Optimizing Your ML Workflow with Kubeflow 1.0
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