The Cloudcast Massive Studios

Aaron (@aarondelp) and Brian (@bgracely) talk about all the major news stories in Cloud and AI from April 2024

SHOW: 817

TRANSCRIPT: Cloudcast #817 - CNOTM - April 2024

CLOUD NEWS OF THE WEEK - http://bit.ly/cloudcast-cnotw

NEW TO CLOUD? CHECK OUT OUR OTHER PODCAST - "CLOUDCAST BASICS"

SHOW NOTES: 
LINKS TO ALL ARTICLES FOR APRIL 2024 CLOUD NEWSSegments Covered in the Show:
Good Old Fashioned Cloud NewsThe AI Innovation Continues - Speed RoundTrend 1 - 2024 is going to be a year of big announcementsTrend 2 - We’re starting to see some (early) reality set in with AITrend 3 - Lots of things are being lost between Broadcom/VMware and AI discussionsFEEDBACK?
Email: show at the cloudcast dot netTwitter: @cloudcastpodInstagram: @cloudcastpodTikTok: @cloudcastpod

Every few years we have to be reminded that open source isn’t a business model. Let’s talk about the business dynamics that everyone seems to keep forgetting.  
SHOW: 816
SHOW TRANSCRIPT:  The Cloudcast #816
SHOW VIDEO: https://youtube.com/@TheCloudcastNET 
CLOUD NEWS OF THE WEEK - http://bit.ly/cloudcast-cnotw
CHECK OUT OUR NEW PODCAST - "CLOUDCAST BASICS"


SHOW NOTES:
Adam Jacobs discusses open source and business (The Changelog)“figure out how to play nice with competition” (Twitter)The will never be another Red Hat - Economics of OSS (a16z)OPEN SOURCE IS A LICENSE, NOT A BUSINESS MODEL
There are rules around software licenses (e.g. Apache, GPL, etc.)There are no rules about how people feel about software, creators or maintainersFREE, FREE TIERS, EXTENSIONS, CLONES
Red Flags: Writes most of the code, took VC funding (multiple rounds)Green flags: Lots of diverse (companies) contributorsYellow flags:  Foundation owns copyright“There’s the business side and there’s the hippie side of OSS”“I have endless ambitions”“I didn’t build a forever entity”“When is the rug pull going to happen?”If a company takes VC funding, is open source anything more than a marketing vehicle?“Docker figured it out and now they are doing like $100M”. Did they? When is OSS personal, and when is it a company?Will there never be another Red Hat, or just not another Linux?How much is too much when determining if a company should give things away for free?

FEEDBACK?
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John Yue (CEO & Co-Founder @ inference.ai) discusses AI workload sizing, matching GPUs to workloads, availability of GPUs vs. costs, and more.

SHOW: 815

CLOUD NEWS OF THE WEEK - http://bit.ly/cloudcast-cnotw

NEW TO CLOUD? CHECK OUT OUR OTHER PODCAST - "CLOUDCAST BASICS"

SHOW NOTES:
Inference.ai (homepage)TechCrunch postSiliconAngle post on ChatGPUTopic 1 - Our topic for today is sizing and IaaS hosting for AI/ML. We’ve covered a lot of basics lately, today we’re going to dig deeper. There is a surprising amount of depth to AI sizing, and it isn’t just speeds and feeds of GPUs. We’d like to welcome John Yue (CEO & Co-Founder @ inference.ai) for this discussion. John, welcome to the show

Topic 2 - Let’s start with sizing, I’ve talked to a lot of customers recently with my day job, and it is amazing how deep AI/ML sizing can go. First, you have to size for training/fine-tuning differently than you would for the inference stage. Second, some just think, pick the biggest GPUs you can afford and go. How should your customers approach this? (GPU’s, software dependencies, etc.)

Topic 2a - Follow-up question what are the business side, what are the business parameters that need to be considered? (budget, cost efficiency, latency/response time, timeline, etc.)

Topic 3 - The whole process can be overwhelming and as we mentioned, some organizations may not think of everything. You recently announced a chatbot to help with this exact process, ChatGPU. Tell everyone a bit about that and how it came to be.

Topic 4 - This is almost like a match-making service, correct? Everyone wants an H100, but not everyone needs or can afford an H100.

Topic 5 - How does GPU availability play into all of this? NVIDIA is sold out for something like 2 years at this point; how is that sustainable? Does everything need to run on a “Ferrari class” NVIDIA GPU?

Topic 6 -  What’s next in the IaaS for AI/ML space? What does a next-generation data center for AI/ML look like? Will the Industry move away from GPUs to reduce dependence on NVIDIA?

FEEDBACK?
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For some strange reason, “maintenance” has been in the news quite a bit lately. Is there ever a time when maintenance is enjoyable, or appreciated? 
SHOW: 814
SHOW TRANSCRIPT: The Cloudcast #814
SHOW VIDEO: https://youtube.com/@TheCloudcastNET 
CLOUD NEWS OF THE WEEK - http://bit.ly/cloudcast-cnotw
CHECK OUT OUR NEW PODCAST - "CLOUDCAST BASICS"


SHOW NOTES:
AWS increased the price of longer-running EKS clusters by 6xBroadcom changes VMware licensing from perpetual to subscriptionBroadcom offers security patches to perpetual license customersIncreasing the Kubernetes support window to 1 yearDiscovering the XZ backdoor (Oxide and Friends, podcast)IS MAINTENANCE EVER APPRECIATED OR ENJOYABLE?
Spent the day surrounded by maintenance activities (oil, AC, power-wash)The costs of maintenance are real and opportunityMaintenance often goes unappreciated and unseenNaming: Release Notes, Technical Debt, Chaos EngineeringTECHNICAL DEBT VS. MAINTENANCE
Should we encourage a lack of maintenance vs. innovation as a priority?Should we encourage active maintenance with lower hard costs?Is there a way to put respect on maintenance? (e.g. OSS maintainers)Do we undervalue maintenance (e.g. Backup/Recovery, DisasterRecovery, etc.)?What maintenance best practices do you use? What are the good and bad of them?FEEDBACK?
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Kalyan Veeramachaneni (@kveeramac, CEO/Founder @DataCebo) discusses the generation and value proposition of synthetic data for GenAI.

SHOW: 813

CLOUD NEWS OF THE WEEK - http://bit.ly/cloudcast-cnotw

NEW TO CLOUD? CHECK OUT OUR OTHER PODCAST - "CLOUDCAST BASICS"

SHOW NOTES:
DataCebo (homepage)Synthetic Data Vault - SDVTechCrunch ArticleMIT News ArticleTopic 1 - Our topic for today is synthetic data. While the concept and need for synthetic data has been around for a long time, it isn’t a topic that typically comes to the forefront and something we haven’t talked about until today. Today is a bit of crossing the streams between developers and testing data and using GenAI to achieve this goal. For this, we’re joined by Kalyan, CEO and Co-Founder of DataCebo. Welcome to the show

Topic 2 - First, for those not familiar, what is synthetic data? What is the use case and need? What problem is it solving today?

Topic 2a - Hopefully, listeners out there are making the connection to the advantages of GenAI for synthetic data, but take us through your original concept at MIT and the history of Synthetic Data Vault (SDV).

Topic 3 - We recently did a show on the security and privacy of training LLMs where we covered the need to mask PII for the training of models for compliance. I can also see bias issues coming into play or maybe training data that doesn’t exist in the real world (weather models example). What are some of the use cases that you’ve seen require synthetic data sets. Are there certain industries (healthcare, financials, etc.) that benefit?

Topic 4 - You were designing this based on GenAI before GenAI was “cool”. How has the rise of LLMs impacted this space?

Topic 5 - If I understand this correctly, organizations would put generative AI on a problem to describe a need for a data set, the model would then evaluate the available data and create a quality synthetic or “fake” dataset. How would the organization verify the quality of the dataset? How would they validate that a synthetic data set is as good as the original data?

Topic 6 - Let’s talk about resources for a bit. When I think of GenAI and training, I think of large amounts of hardware and in particular GPU’s that might have limited availability. Is that true here? Also, is this on-prem or in the cloud, or both? 


FEEDBACK?


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With the AI Era upon us, the challenge of trying to learn and make sense of the technologies, the business opportunities and the pitfalls is both exciting and equally terrifying.  
SHOW: 812
SHOW TRANSCRIPT: The Cloudcast #812
SHOW VIDEO: https://youtube.com/@TheCloudcastNET 
CLOUD NEWS OF THE WEEK - http://bit.ly/cloudcast-cnotw
CHECK OUT OUR NEW PODCAST - "CLOUDCAST BASICS"


SHOW NOTES:


WHEN WAS THE LAST TIME YOU REALLY LEARNED SOMETHING NEW IN TECH?
Cloud was all new, but not completelyThe pace of cloud innovation seemed fast, but not in comparison to AIThe focus was around one cloud vs. a new source every weekPICK A TOPIC, READ, BE CONFUSED, LOOK FOR CONFIRMATION, RINSE, REPEAT
Where to start the learning process? Where to pick a source of learning?(Coursera, MIT online, blogs/videos/search, etc.)How to determine legitimacy of the sources? How far to learn before stopping to make sense? Trying to relate it to something you already know? How and when to ask questions? FEEDBACK?
Email: show at the cloudcast dot netTwitter: @cloudcastpodInstagram: @cloudcastpodTikTok: @cloudcastpod