#GetReady4AI - CrowdChat

The Future of AI is Now

Go from possible to Ready with AI, simplified by Dell EMC. Hear from Tom Burns, head of Dell EMC Networking and Solutions, who will explain how Dell EMC is helping organizations win with AI. Then, join AI experts as they discuss the implications of AI for a data-driven world.

#getready4ai @dvellante @ralphfinos @jameskobielus @mikeleone_it @tburnssocal @plburris @stu @jon_siegal @broomio @furrier

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Robin Holden Wolf17

As the leader in @NVIDIAAI GPU technology, NVIDIA is looking forward to strengthening our partnership with Dell #GetReady4AI with our joint Ready Solution for Deep Learning. Check out this website for more details: http://custom.crn.co....

https://www.crowdcha...

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Jonathan Siegal

Thanks for the great partnership

@jamonascone Machine Learning Knowledge Center featuring the latest developments in artificial intelligence, machine learning and deep learning

Thanks for the partnership

Where do you stand in your adoption of AI?

Where do you stand in your adoption of AI?

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Keith Drummond14

If Your Data Is Bad, Your Machine Learning Tools Are Useless - see video series on this from @TomRedman...https://www.youtube....

If Your Data Is Bad, Your Machine Learning Tools Are Useless
The first video in the series on the importance of Data Quality and Machine Learning and what you need to do!

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Michael Pomatto

This key point seems to get overlooked WAY too many times! It was a point that we tried to stress on my last few international consulting assignments. Especially with the GDPR rules in place, the data has to be accurate.

Yes Keith... #AI doesn't solve GIGO problem #DataQualityMatters

Yes. here's my discussion of the quality conundrum surrounding this data in an AI/ML development context, from a few years ago in another life. Data quality for social media analytics: sentiment or sediment? http://www.ibmbigdat...

Data Preparation can be 2/3rds or more of the total effort/time in an AI/Deep Learning project! Need to account for that!

Amol Choukekar1

Isilon is #1 ScaleOut. I would assume there soon would be a magic quadrant for AI Solutions viz Completeness of Vision (value ) and ability to execute (speed)?

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https://www.crowdcha... Where do you stand in your adoption of AI?

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.@plburris we use AI daily in our data ingestion system - curating video content and identifying the best "gems"

Michael Pomatto

Most of my current clients are still working on understanding what it means in their organizations. There are a lot of things being labeled AI that are not.

Many enterprises are at the start of their AI adoption. What that means is that they've adopted machine learning as an evolution of their advanced analytics practices, and are using it for focused apps: chatbots, intelligent search, security incident ad event mgt, etc

I'd put it in the category of NLP - but humans are still critical to our workflow - so I'd say we're still in the very early days

nearly 20% of organizations are depending on AI/ML to deliver significant measurable business outcomes immediately

Jonathan Siegal

While AI is a strategic priority for most CIOs, the trick is to offload data scientists of any mundane non-data science tasks

@jameskobielus we're seeing the same. There's also a lot of interest in image and video recognition capabilities in multiple verticals.

@jon_siegal The strategic priority should be to build data science teams of specialists for the various functions--statistical modeling, data engineering, programming, etc.---necessary to drive the entire AI DevOps workflow.

@broomio Yes. We're seeing plenty of activity in AI-driven computer vision. Check out my recent #SiliconANGLE piece re this trend: https://siliconangle...

#IWork4Dell #getreadyforAI Check out Dell EMC's announcement on Ready Solutions for AI - http://www.emc.com/a...

Dell EMC Accelerates Artificial Intelligence Adoption for Digital Transformation
Dell EMC announces the availability of new Ready Solutions for AI, with specialized designs for Machine Learning with Hadoop and Deep Learning with NVIDIA. The Dell EMC Ready Solutions simplify AI environments, deliver faster, deeper insights than th...

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Robin Holden Wolf

Here are the @NVIDIAAI & Dell Ready Solution details.. Ready Solutions for AI: Deep Learning with NVIDIA:

Dell EMC and NVIDIA engineered this deep learning design to be built around Dell EMC PowerEdge servers with NVIDIA® Tesla® V100 Tensor Core GPUs.

Grant Gustafson5

Build your own is still big with seasoned data scientists on large companies - how does Dell convince them that 'easy' is better?

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Rodrigo Gazzaneo

it's a conversation about productivity and outcomes. Focus on the activities that generate value, automate and consume other steps on the process.

Great question, I understand data scientist are taking the DIY approach, yet that doesn't equal fastest time value. We offer speed, agility and tools that enable the data scientist to build an environment within 5 clicks of a mouse.

Lucas A. Wilson

I think build your own will always be an option for some (just like in #HPC). In the end it all comes down to where your company wants to spend - a baseline configuration that can be extended later or bare metal.

@Armando75 That's not the case. Data scientists are very much adopting the new generation of prebuilt DevOps platforms to drive team productivity through the AI pipeline. See my #Wikibon study: https://wikibon.com/...

Rodrigo Gazzaneo

@Armando75 exactly. TTV. Time to value. That's the KPI.

agreed. There is no one size fits all approach. However, it's important to understand the tradeoffs of either approach.

Rodrigo Gazzaneo

@jameskobielus I have a very interesting experience on that. I worked with a Data Science team that adopted #agile #devops #PaaS early on 2014.

Grant Gustafson

@broomio these data scientists are like master guitarists and when we say 'easy', it's like showing them how to play folk songs. will this ready solution be flexible enough to meet all of their needs or will they have to tweak? I'm thinking large orgs, not start ups in AI

That's overstating the average data scientist's jack-of-all-trades "unicorn" bonafides. Most are statistical modeling specialists in larger teams with data engineers, programmers, governance specialists, and others in increasingly DevOps workflows.

Curcuru discussing the pressure that AI places on enterprise storage. Need flash storage that supports nanosecond AI performance: create a seamless instantaneous decision/response re customers and other stakeholders.

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https://www.crowdcha... What do you think is the most important aspect of this "Get Ready for AI" announcement?

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I think the notion of scaling value from AI in operational data was a great high order bit

Definitely the data at scale with the right tools set for outcomes.

the ability to scale infrastructure as AI requirements scale over time is super important. We are seeing the tip of the iceberg in terms of the performance requirements of AI workloads.

enabling organizations to quickly and easily deploy an AI technology stack that can scale to meet the demands of their organization

Michael Pomatto

To be honest, I'm from the data scientist / engineering side of the house. It was a bit too much marketing for me; however, I'm getting some contacts for follow-up that I am sure will be useful. Thanks for the invite.

.@plburris key to me is making infrastructure invisible. To data wonks infrastructure is practically irrelevant (Until it breaks)...

Dell Ready Solutions for AI: customers now have expertly-designed, easy-to-deploy, easy-to-use AI solutions that leverage on-prem cost advantages and data locations. Dell services can further accelerate the time-to-insight and maximize results & ROI.

#Wikibon Using AI and Advanced Analytics in real-time is key to improving and automating high-value decisions - nice example from MasterCard.

The Data Science Provisioning Portal. Optimizing the entire software and hardware stack for your particular use case is the make-or-break consideration in full-stack AI readiness to deploy.

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Thanks Nick for advancing AI #GetReady4AI

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Speed / simplicity of deployment.