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Cloud Computing - Analytics, AI, ML, Big Data
Manage episode 283552345 series 2844744
SHOW: Season 1, Show 9
OVERVIEW: From the creators of the Internet's #1 Cloud Computing podcast, The Cloudcast, Aaron Delp (@aarondelp) and Brian Gracely (@bgracely) introduce this new podcast, Cloudcast Basics.
- What does AI/ML/Big Data mean in the cloud? The on-demand nature of public cloud really made sense, these capabilities require lots of data, lots of computing (various levels of capacity, variable amounts of time), and their ability to impact the business could be connected to how quickly new experiments were tried.
- How are AI/ML/Big Data services allocated? They are very much like “SaaS” offerings, run by the public cloud, with some level of “nerd knobs” to tweak usage patterns. It usually involves the ability to ingest data, ETL/clean data, store data, run algorithms, use predefined models, build customized models.
- How were AI/ML/Big Data services allocated before cloud computing? Massive capital outlays for equipment that was difficult to setup and maintain, and oftentimes didn’t have a model for sharing across groups within a company.
- What does the cloud computing provider do with a AI/ML/Big Data offering (responsibilities vs. customer responsibilities)? Cloud providers typically run the underlying services, and then there is variability on what the user can control or needs to do.
- Why are there so many variations of AI/ML/Big Data? This is sort of like databases, in that we’ve learned different ways to build models, test models, store data (real-time vs. historical).
- Does it matter where the AI/ML/Big Data is located? How do clouds organize AI/ML/Big Data?
- How much do AI/ML/Big Data services cost in the cloud? What are the various ways you can buy AI/ML/Big Data?
Examples:
- AWS AI/ML - https://aws.amazon.com/machine-learning/
- AWS Analytics - https://aws.amazon.com/big-data/datalakes-and-analytics/
- Azure Analytics - https://azure.microsoft.com/en-us/product-categories/analytics/
- Azure AI/ML - https://azure.microsoft.com/en-us/overview/ai-platform/
- GCP AI/ML - https://cloud.google.com/products/ai
SUBSCRIBE: Please subscribe anywhere you get podcasts (Apple Podcasts, Google Podcasts, Spotify, Stitcher, Amazon Music, Pandora, etc.).
CLOUD NEWS OF THE WEEK - http://bit.ly/cloudcast-cnotw
LEARNING CLOUD COMPUTING:
Here are some great places to begin your cloud journey, if you're interested in getting hands-on experience with the technology, or you'd like to build your skills towards a certification.
- CBT Nuggets - Training and Certifications
- A Cloud Guru - Training and Certifications
- Cloud Academy - Training and Certifications
- Katakoda - Self-Paced, Interactive Learning
- GitHub - Code Samples and Collaboration
FEEDBACK?
- Web: Cloudcast Basics
- Email: show at cloudcastbasics dot net
- Twitter: @cloudcastbasics
31集单集
Manage episode 283552345 series 2844744
SHOW: Season 1, Show 9
OVERVIEW: From the creators of the Internet's #1 Cloud Computing podcast, The Cloudcast, Aaron Delp (@aarondelp) and Brian Gracely (@bgracely) introduce this new podcast, Cloudcast Basics.
- What does AI/ML/Big Data mean in the cloud? The on-demand nature of public cloud really made sense, these capabilities require lots of data, lots of computing (various levels of capacity, variable amounts of time), and their ability to impact the business could be connected to how quickly new experiments were tried.
- How are AI/ML/Big Data services allocated? They are very much like “SaaS” offerings, run by the public cloud, with some level of “nerd knobs” to tweak usage patterns. It usually involves the ability to ingest data, ETL/clean data, store data, run algorithms, use predefined models, build customized models.
- How were AI/ML/Big Data services allocated before cloud computing? Massive capital outlays for equipment that was difficult to setup and maintain, and oftentimes didn’t have a model for sharing across groups within a company.
- What does the cloud computing provider do with a AI/ML/Big Data offering (responsibilities vs. customer responsibilities)? Cloud providers typically run the underlying services, and then there is variability on what the user can control or needs to do.
- Why are there so many variations of AI/ML/Big Data? This is sort of like databases, in that we’ve learned different ways to build models, test models, store data (real-time vs. historical).
- Does it matter where the AI/ML/Big Data is located? How do clouds organize AI/ML/Big Data?
- How much do AI/ML/Big Data services cost in the cloud? What are the various ways you can buy AI/ML/Big Data?
Examples:
- AWS AI/ML - https://aws.amazon.com/machine-learning/
- AWS Analytics - https://aws.amazon.com/big-data/datalakes-and-analytics/
- Azure Analytics - https://azure.microsoft.com/en-us/product-categories/analytics/
- Azure AI/ML - https://azure.microsoft.com/en-us/overview/ai-platform/
- GCP AI/ML - https://cloud.google.com/products/ai
SUBSCRIBE: Please subscribe anywhere you get podcasts (Apple Podcasts, Google Podcasts, Spotify, Stitcher, Amazon Music, Pandora, etc.).
CLOUD NEWS OF THE WEEK - http://bit.ly/cloudcast-cnotw
LEARNING CLOUD COMPUTING:
Here are some great places to begin your cloud journey, if you're interested in getting hands-on experience with the technology, or you'd like to build your skills towards a certification.
- CBT Nuggets - Training and Certifications
- A Cloud Guru - Training and Certifications
- Cloud Academy - Training and Certifications
- Katakoda - Self-Paced, Interactive Learning
- GitHub - Code Samples and Collaboration
FEEDBACK?
- Web: Cloudcast Basics
- Email: show at cloudcastbasics dot net
- Twitter: @cloudcastbasics
31集单集
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