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    Explore " sagemaker" with insightful episodes like "Emmanuel Turlay, Founder and CEO of Sematic and machine learning pioneer, discusses what's required to turn every software engineer into an ML engineer", "MLOps at a Reasonable Scale: Approaches & Challenges with Jacopo Tagliabue", "52 Weeks of AWS: Episode 2: Reinvent 2021 and Getting Started with AWS" and "Reviewing AWS re:Invent 2019" from podcasts like ""AI and the Future of Work", "ML Platform Podcast", "52 Weeks of Cloud" and "The Cloudcast"" and more!

    Episodes (4)

    Emmanuel Turlay, Founder and CEO of Sematic and machine learning pioneer, discusses what's required to turn every software engineer into an ML engineer

    Emmanuel Turlay, Founder and CEO of Sematic and machine learning pioneer, discusses what's required to turn every software engineer into an ML engineer

    Emmanuel Turlay spent more than a decade in engineering roles at tech-first companies like Instacart and Cruise before realizing machine learning engineers need a better solution. Emmanuel started Sematic earlier this year and was part of the YC summer 2022 batch. He recently raised a $3M seed round from investors including Race Capital and Soma Capital. Thanks to friend of the podcast and former guest Hina Dixit from Samsung NEXT for the intro to Emmanuel.

    I’ve been involved with the AutoML space for five years and, for full disclosure, I’m on the board of Auger which is in a related space. I’ve seen the space evolve and know how much room there is for innovation. This one's a great education about what’s broken and what’s ahead from a true machine learning pioneer.

    Listen and learn...

    1. How to turn every software engineer into a machine learning engineer
    2. How AutoML platforms are automating tasks performed in traditional ML tools
    3. How Emmanuel translated learning from Cruise, the self-driving car company, into an open source platform available to all data engineering teams
    4. How to move from building an ML model locally to deploying it to the cloud and creating a data pipeline... in hours
    5. What you should know about self-driving cars... from one of the experts who developed the brains that power them
    6. Why 80% of AI and ML projects fail

    References in this episode:

    MLOps at a Reasonable Scale: Approaches & Challenges with Jacopo Tagliabue

    MLOps at a Reasonable Scale: Approaches & Challenges with Jacopo Tagliabue
    Today, we’re joined by Jacopo Tagliabue, Director of A.I. at Coveo. He currently combines product thinking and research-like curiosity to build better data-driven systems at scale. They examine how immature data pipelines are impeding a substantial part of industry practitioners from profiting from the latest ML research. People from super-advanced, hyperscale companies come up with the majority of ideas for machine learning best practices and tools, examples are Big Tech companies like Google, Uber, and Airbnb, with sophisticated ML infrastructure to handle their petabytes of data. However, 98% of businesses aren't using machine learning in production at hyperscale but rather on a smaller (reasonable) scale. Jacopo discusses how businesses may get started with machine learning at a modest size. Most of these organizations are early adopters of machine learning, and with their good sized proprietary datasets they can also reap the benefits of ML without requiring all of the super-advanced hyper-real-time infrastructure.

    52 Weeks of AWS: Episode 2: Reinvent 2021 and Getting Started with AWS

    52 Weeks of AWS: Episode 2: Reinvent 2021 and Getting Started with AWS

    If you enjoyed this video, here are additional resources to look at:

    Coursera + Duke Specialization: Building Cloud Computing Solutions at Scale Specialization: https://www.coursera.org/specializations/building-cloud-computing-solutions-at-scale

    Python, Bash, and SQL Essentials for Data Engineering Specialization: https://www.coursera.org/specializations/python-bash-sql-data-engineering-duke

    O'Reilly Book: Practical MLOps: https://www.amazon.com/Practical-MLOps-Operationalizing-Machine-Learning/dp/1098103017

    O'Reilly Book: Python for DevOps: https://www.amazon.com/gp/product/B082P97LDW/

    Pragmatic AI: An Introduction to Cloud-based Machine Learning: https://www.amazon.com/gp/product/B07FB8F8QP/

    Pragmatic AI Labs Book: Python Command-Line Tools: https://www.amazon.com/gp/product/B0855FSFYZ

    Pragmatic AI Labs Book: Cloud Computing for Data Analysis: https://www.amazon.com/gp/product/B0992BN7W8

    Pragmatic AI Book: Minimal Python: https://www.amazon.com/gp/product/B0855NSRR7

    Pragmatic AI Book: Testing in Python: https://www.amazon.com/gp/product/B0855NSRR7

    Subscribe to Pragmatic AI Labs YouTube Channel: https://www.youtube.com/channel/UCNDfiL0D1LUeKWAkRE1xO5Q

    View content on noahgift.com: https://noahgift.com/

    View content on Pragmatic AI Labs Website: https://paiml.com/

    Check out all a Master's degree worth of courses on Coursera on topics ranging from Cloud Computing to Rust to LLMs and Generative AI: https://www.coursera.org/instructor/noahgift.

    Reviewing AWS re:Invent 2019

    Reviewing AWS re:Invent 2019

    SHOW: 427

    DESCRIPTION: Brian reviews the big themes from AWS re:Invent 2019, as well as “most” of the new announcements.

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    The Cloudcast
    en-usDecember 07, 2019
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