Difference between revisions of "Building Your Environment"
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|description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools | |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools | ||
}} | }} | ||
| − | [ | + | [https://www.youtube.com/results?search_query=AI+Environment+machine+learning+frameworks+compared Youtube search...] |
| − | [ | + | [https://www.google.com/search?q=AI+Environment+frameworks+compared+deep+machine+learning+ML ...Google search] |
| − | * [ | + | * [https://www.altexsoft.com/blog/datascience/comparing-machine-learning-as-a-service-amazon-microsoft-azure-google-cloud-ai-ibm-watson/ Comparing Machine Learning as a Service: Amazon, Microsoft Azure, Google Cloud AI, IBM Watson | Bradford Cross] |
| − | * [ | + | * [https://www.dataversity.net/ai-platforms-next-step-artificial-intelligence/# AI Platforms: The Next Step in Artificial Intelligence | Keith D. Foote] |
| − | * [ | + | * [https://www.ipi-singapore.org/technology-offers/artificial-intelligence-platform-machine-learning-modelling-and-cloud Artificial Intelligence Platform, Machine Learning Modelling and Cloud | IPI] |
* [[Algorithm Administration#AIOps/MLOps|AIOps/MLOps]] | * [[Algorithm Administration#AIOps/MLOps|AIOps/MLOps]] | ||
* [[Containers; Docker, Kubernetes & Microservices]] | * [[Containers; Docker, Kubernetes & Microservices]] | ||
| Line 19: | Line 19: | ||
* [[Platforms: AI/Machine Learning as a Service (AIaaS/MLaaS)]] | * [[Platforms: AI/Machine Learning as a Service (AIaaS/MLaaS)]] | ||
* [[Serverless]] | * [[Serverless]] | ||
| − | * [ | + | * [https://blog.pocketcluster.io/ Pocketcluster - Weekly Machine Learning Opensource Roundup] |
* Environments: | * Environments: | ||
** [[Local]] Machine, [[Anaconda]], [[TensorFlow]], Graphics card GPU | ** [[Local]] Machine, [[Anaconda]], [[TensorFlow]], Graphics card GPU | ||
** [[Laptop]]s for Machine Learning | ** [[Laptop]]s for Machine Learning | ||
** [[Google AIY Projects Program]] | ** [[Google AIY Projects Program]] | ||
| − | * [ | + | * [https://www.forbes.com/sites/cognitiveworld/2019/09/11/the-full-stack-data-scientist-myth-unicorn-or-new-normal/#60cf23612c60 The Full Stack Data Scientist: Myth, Unicorn, or New Normal? | Nisha Talagala - Forbes] |
The capability to transform data into actionable insight is the key to a competitive advantage for any | The capability to transform data into actionable insight is the key to a competitive advantage for any | ||
organization. ... While many ML algorithms have been around for years, the ability to apply complex mathematical calculations to | organization. ... While many ML algorithms have been around for years, the ability to apply complex mathematical calculations to | ||
| − | data, and process them more quickly than ever before, is a recent [[development]].[ | + | data, and process them more quickly than ever before, is a recent [[development]].[https://www.gartner.com/binaries/content/assets/events/keywords/catalyst/catus8/preparing_and_architecting_for_machine_learning.pdf Preparing and Architecting for Machine Learning | Carlton E. Sapp] |
| − | + | https://3.bp.blogspot.com/-Rrurwfwi9_A/WzNuu3w93sI/AAAAAAAAATs/bOsADMwyQREEgrZTsmtv_xfrSCE5GCRQQCLcBGAs/s400/EEAML.jpg | |
<youtube>WQt4H1Bo0jM</youtube> | <youtube>WQt4H1Bo0jM</youtube> | ||
| Line 38: | Line 38: | ||
<youtube>1t7EEq_Htgc</youtube> | <youtube>1t7EEq_Htgc</youtube> | ||
| − | * [ | + | * [https://www.kdnuggets.com/2019/02/gartner-2019-mq-data-science-machine-learning-changes.html Gainers, Losers, and Trends in Gartner 2019 Magic Quadrant for Data Science and Machine Learning Platforms | Gregory Piatetsky - KDnuggets] |
| − | + | https://www.kdnuggets.com/images/gartner-mq-data-science-platforms-2018-vs-2018-645.jpg | |
== <span id="My Own AI Server"></span>My Own AI Server == | == <span id="My Own AI Server"></span>My Own AI Server == | ||
| − | * [ | + | * [https://www.digit.in/features/machine-learning-and-ai/deep-learning-with-tensorflow-and-intel-a-hardware-and-software-guide-for-beginners-47216.html Deep Learning with Tensorflow and INTEL - A Hardware and Software Guide for Beginners | digit] |
| − | * [ | + | * [https://medium.com/the-mission/why-building-your-own-deep-learning-computer-is-10x-cheaper-than-aws-b1c91b55ce8c Why building your own Deep Learning Computer is 10x cheaper than AWS | Jeff Chen] |
| − | * [ | + | * [https://towardsdatascience.com/build-and-setup-your-own-deep-learning-server-from-scratch-e771dacaa252 Build and Setup Your Own Deep Learning Server From Scratch | Kitty Shum] |
| − | * [ | + | * [https://www.cio.com/article/3209706/artificial-intelligence/machine-intelligence-build-your-own-vs-as-a-service.html Machine intelligence: Build your own vs. as-a-service] |
<youtube>xsnVlMWQj8o</youtube> | <youtube>xsnVlMWQj8o</youtube> | ||
| Line 55: | Line 55: | ||
== [[Meta|Facebook]] == | == [[Meta|Facebook]] == | ||
| − | [ | + | [https://www.youtube.com/results?search_query=Meta+Facebook+big+basin+sur+tioga+Server+machine+learning Youtube search...] |
| − | [ | + | [https://www.google.com/search?q=Meta+Facebook+big+basin+sur+tioga+AI+Server+deep+machine+learning+ML ...Google search] |
| − | * [ | + | * [https://code.fb.com/data-center-engineering/introducing-big-basin-our-next-generation-ai-hardware/ Introducing Big Basin: Our next-generation AI hardware | Kevin Lee -] [[Meta|Facebook]] |
| − | * [ | + | * [https://www.slideshare.net/KarthikMurugesan2/facebook-machine-learning-infrastructure-2018-slides] [[Meta|Facebook] |
<youtube>hwGxhz5zPvk</youtube> | <youtube>hwGxhz5zPvk</youtube> | ||
<youtube>pvtUrkIR3rY</youtube> | <youtube>pvtUrkIR3rY</youtube> | ||
Revision as of 05:04, 28 March 2023
Youtube search... ...Google search
- Comparing Machine Learning as a Service: Amazon, Microsoft Azure, Google Cloud AI, IBM Watson | Bradford Cross
- AI Platforms: The Next Step in Artificial Intelligence | Keith D. Foote
- Artificial Intelligence Platform, Machine Learning Modelling and Cloud | IPI
- AIOps/MLOps
- Containers; Docker, Kubernetes & Microservices
- Kubeflow Pipelines
- Service Capabilities
- Automated Learning
- AI Marketplace & Toolkit/Model Interoperability
- Platforms: AI/Machine Learning as a Service (AIaaS/MLaaS)
- Serverless
- Pocketcluster - Weekly Machine Learning Opensource Roundup
- Environments:
- Local Machine, Anaconda, TensorFlow, Graphics card GPU
- Laptops for Machine Learning
- Google AIY Projects Program
- The Full Stack Data Scientist: Myth, Unicorn, or New Normal? | Nisha Talagala - Forbes
The capability to transform data into actionable insight is the key to a competitive advantage for any organization. ... While many ML algorithms have been around for years, the ability to apply complex mathematical calculations to data, and process them more quickly than ever before, is a recent development.Preparing and Architecting for Machine Learning | Carlton E. Sapp
My Own AI Server
- Deep Learning with Tensorflow and INTEL - A Hardware and Software Guide for Beginners | digit
- Why building your own Deep Learning Computer is 10x cheaper than AWS | Jeff Chen
- Build and Setup Your Own Deep Learning Server From Scratch | Kitty Shum
- Machine intelligence: Build your own vs. as-a-service
Youtube search... ...Google search
