Steering a Large-Scale Data Migration // Michelle Marie Conway // MLOps Podcast Coffee #174 clip
MLOps Coffee Sessions #174 with Michelle Marie Conway, Harnessing MLOps in Finance: Bringing Statistical Models to Life for Positive Impact, co-hosted by Stephen Batifol.
Michelle dives into the challenges they faced in migrating their data from on-premises to Google Cloud. She discusses how they approach the process in small, controlled releases to ensure security and sustainability. With an ambitious budget of 3 billion, Michelle and her team tackle the task of steering a massive ship, with 70,000 colleagues spread across the organization.
// Abstract
Michelle Marie Conway joins hosts Stephen Batifol and Demetrios to share their insights and experiences in the tech industry. Michelle emphasizes the importance of constant learning and adaptation in the rapidly changing tech industry. They discuss the need to stay up to date with the latest documentation, understand code logic, and be mindful when writing code. Michelle also reflects on their experiences as one of the few women in their university math class and often being the only woman on their team in the workplace. They discuss the need for more girls to pursue STEM subjects in schools and the importance of allies in the workplace. Additionally, Michelle explores the benefits and challenges of AI tools, sharing their experiences with tools like Gen AI and Chat GPT. While AI tools enhance productivity, Michelle also acknowledges the limitations of these tools in more technical tasks and the continued reliance on developer resources. This episode offers valuable insights into the importance of continuous learning, gender diversity in STEM, and the potential of AI tools in the field of MLOps.
// Bio
As an Irish woman who relocated to London after completing her university studies in Dublin, Michelle spent the past 12 years carving out a career in the data and tech industry. With a keen eye for detail and a passion for innovation, She has consistently leveraged my expertise to drive growth and deliver results fo
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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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Message buses, Async and sync architecture
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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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Deeper thinking from data scientists around platform blackholes
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Adjacent usecases and multistep feature engineering
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Standardization of Machine Learning tools like in Software Engineering with Venkata Pingali
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Reproducability flaws in end to end Machine Learning debugging
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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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Are Kubeflow and Airflow complementary?
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Why Kubeflow gained so much traction=open community
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Who decides the dirrection of Kubeflow
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What do Kubeflow and Arrikto do and how do they work together?
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Versioning your ML steps with Kubeflow
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Machine Learning Lifecycles//Perception vs Reality
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Kubeflow vs SageMaker in Machine Learning
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