Scaling Data Reliably: A Journey in Growing Through Data Pain Points // Miriah Peterson // DE4AI

MLOps.community · Beginner ·📊 Data Analytics & Business Intelligence ·1y ago
//Abstract Software practitioners work to make their systems reliable. We hear teams boasting of having four or five 9s of uptime. Data Systems depend on data that can be out of date or late. Pipelines and automated jobs fail to run. Data sometimes arrives late changing the outcomes of processing jobs. All these situations are examples of Data Downtime and lead to misleading results and false reporting. As a DRE team (Data Reliability Engineering) we borrowed tools and practices from SRE to build a better data system. In this talk, we will explore real-world reliability situations for our data systems and address three major topics to strengthen any pipeline: Data Downtime: What Data Downtime is, how it affects your bottom line, and how to minimize it. Data Service Level Metrics: We will talk about metadata for your Data pipeline and how to report on pipeline transactions that can lead to preventative data engineering practices. Data monitoring: What to look out for and how to be aware of system failure versus data failures. //Bio Miriah Peterson, a seasoned engineer with 6 years of expertise in Go programming, excels as a Data Reliability Engineer. Her professional journey includes crafting videos, tutorials, and courses, showcasing her mastery in Go and Data Engineering. A dynamic speaker, Miriah has delivered talks on Go, machine learning, and data engineering. As a board member of Forge Foundation Inc. and an organizer of the GoWest Conference, Utah Data Engineering, and Machine Learning Utah meetups, she actively shapes the tech community. Miriah earned her bachelor's degree in physics from Brigham Young University in 2017, laying a strong foundation for her multifaceted contributions to the field. A big thank you to our Premium Sponsors @Databricks, @tecton8241, & @onehouseHQfor their generous support!
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Our 1st MLOps Meetup // Luke Marsden // MLOps Meetup #1
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 8 Hierarchy of Machine Learning Needs // Phil Winder // MLOps Meetup #3
Hierarchy of Machine Learning Needs // Phil Winder // MLOps Meetup #3
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 9 Automatically Retrain Machine Learning Models? Are best practices worth it?
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 10 Building an MLOps Team? Key ideas to keep in mind
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 11 Hierarchy of MLOps Needs
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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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MLOps Meetup #6: Mid-Scale Production Feature Engineering with Dr. Venkata Pingali
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Specific challenges in Machine Learning
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 34 Current State Of Machine Learning
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Humans in the Loop are a defining factor in Machine Learning
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 36 Learning from real life Machine Learning failures
Learning from real life Machine Learning failures
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 38 Swiss Cheese model in Machine Learning
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 39 Resume driven development in Machine learning & software engineering
Resume driven development in Machine learning & software engineering
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 42 Dependable data and being able to Trust in your Data with Venkata Pengali of Scribble Data
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 48 Adjacent usecases and multistep feature engineering
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 49 Standardization of Machine Learning tools like in Software Engineering with Venkata Pingali
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 50 Reproducability flaws in end to end Machine Learning debugging
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 53 MLOps Meetup #8 Optimizing Your ML Workflow with Kubeflow 1.0
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