|
|
|
|
LEADER |
00000cgm a2200000 i 4500 |
001 |
OR_on1177144842 |
003 |
OCoLC |
005 |
20231017213018.0 |
006 |
m o c |
007 |
cr cna|||||||| |
007 |
vz czazuu |
008 |
200724s2019 xx 040 o vleng d |
040 |
|
|
|a UMI
|b eng
|e rda
|e pn
|c UMI
|d OCLCF
|d OCLCO
|d OCLCQ
|
029 |
1 |
|
|a AU@
|b 000071521902
|
035 |
|
|
|a (OCoLC)1177144842
|
037 |
|
|
|a CL0501000125
|b Safari Books Online
|
050 |
|
4 |
|a TS171.4
|
049 |
|
|
|a UAMI
|
100 |
1 |
|
|a Bhaowal, Mayukh,
|e on-screen presenter.
|
245 |
1 |
0 |
|a Executive briefing :
|b managing AI products /
|c Mayukh Bhaowal.
|
246 |
3 |
0 |
|a Managing AI products
|
246 |
3 |
|
|a Managing Artificial Intelligence products
|
264 |
|
1 |
|a [Place of publication not identified] :
|b O'Reilly Media,
|c 2019.
|
300 |
|
|
|a 1 online resource (1 streaming video file (39 min., 13 sec.))
|
336 |
|
|
|a two-dimensional moving image
|b tdi
|2 rdacontent
|
337 |
|
|
|a computer
|b c
|2 rdamedia
|
337 |
|
|
|a video
|b v
|2 rdamedia
|
338 |
|
|
|a online resource
|b cr
|2 rdacarrier
|
511 |
0 |
|
|a Presenter, Mayukh Bhaowal.
|
500 |
|
|
|a Title from title screen (viewed July 23, 2020).
|
520 |
|
|
|a "There's an evolution in product management correlated with the shift from the digital revolution to the AI revolution. AI product managers are rising. As AI and ML eat software, more and more PMs and execs need to level up their skills to manage these products and provide requirements and specifications that add value to the data engineering and data science teams. This leads to actually solving customer pain points and not just building a cool technology solution. Imagine a new recommendation feature. Traditionally, a PM would work with a designer and come up with the UI/UX specification around the layout, when and where to show the recommendations, the behavior on interacting with the recommendations, etc. However, product specifications around UX, layout, or interactions are of no value to a machine learning engineer or a data scientist who would need to operationalize a machine learning system to power such recommendations. Mayukh Bhaowal (Salesforce) details the top areas an AI product manager needs to focus on above and beyond a traditional PM, including mapping of business problems to machine learning problems, understanding data and labelled data nuances, defining crisp model evaluation criteria, model explainability, ethics and bias, and the distinction between research and production when it comes to AI-powered products and features. This session is from the 2019 O'Reilly Artificial Intelligence Conference in San Jose, CA."--Resource description page
|
590 |
|
|
|a O'Reilly
|b O'Reilly Online Learning: Academic/Public Library Edition
|
611 |
2 |
0 |
|a O'Reilly Artificial Intelligence Conference
|d (2019 :
|c San Jose, Calif.)
|
650 |
|
0 |
|a Product management.
|
650 |
|
0 |
|a Project management
|x Evaluation.
|
650 |
|
0 |
|a Artificial intelligence.
|
650 |
|
0 |
|a New products
|x Management.
|
650 |
|
2 |
|a Artificial Intelligence
|
650 |
|
6 |
|a Produits commerciaux
|x Gestion.
|
650 |
|
6 |
|a Intelligence artificielle.
|
650 |
|
7 |
|a artificial intelligence.
|2 aat
|
650 |
|
7 |
|a Artificial intelligence
|2 fast
|0 (OCoLC)fst00817247
|
650 |
|
7 |
|a New products
|x Management
|2 fast
|0 (OCoLC)fst01036904
|
650 |
|
7 |
|a Product management
|2 fast
|0 (OCoLC)fst01078225
|
650 |
|
7 |
|a Project management
|x Evaluation
|2 fast
|0 (OCoLC)fst01078802
|
856 |
4 |
0 |
|u https://learning.oreilly.com/videos/~/0636920371298/?ar
|z Texto completo (Requiere registro previo con correo institucional)
|
994 |
|
|
|a 92
|b IZTAP
|