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title: '科技英文:Managing Machine Learning in Production with Kubeflow and DevOps'

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title: 科技英文:Managing Machine Learning in Production with Kubeflow and DevOps

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#Managing Machine Learning in Production with Kubeflow and DevOps - David Aronchick, Microsoft
1.Managing Machine Learning in Production with Kubeflow and DevOps - David Aronchick, Microsoft
Dev:development /Ops:operations=keep going
1
00:00:00,030 --> 00:00:01,990
hi I apologize I was not working on my
嗨，我很抱歉，我沒有為我工作
Apologize(V):sorry
2
00:00:02,189 --> 00:00:03,428
slides one minute I'm not one of those
滑了一分鐘，我不是其中之一

3
00:00:03,629 --> 00:00:05,049
presenters but I was very late so I
主持人，但我來晚了，所以我

4
00:00:05,250 --> 00:00:08,560
apologize thank you so much I am David
很抱歉，我是大衛

5
00:00:08,759 --> 00:00:10,390
Ron chick I lead open source machine
羅恩·小妞我領導開源機器
open source:free
6
00:00:10,589 --> 00:00:13,330
learning strategy and ml at Microsoft
微軟的學習策略和毫升
Ml:maching Learning
7
00:00:13,529 --> 00:00:16,780
and Azure and I was previously the lead
和Azure，而我以前是負責人
Pentagon:top secret
8
00:00:16,980 --> 00:00:18,940
p.m. for kubernetes and I helped start
下午為kubernetes，我幫助開始
p.m.:project manager 
9
00:00:19,140 --> 00:00:20,890
the Q flow project and I'm here to talk
 Q flow項目，我在這裡談

10
00:00:21,089 --> 00:00:23,470
to you about how to bring your machine
向您介紹如何攜帶您的機器

11
00:00:23,670 --> 00:00:26,260
learning to production using Q flow and
使用Q流學習生產

12
00:00:26,460 --> 00:00:33,219
mo ops so at Microsoft because the
在Microsoft運作的原因是

13
00:00:33,420 --> 00:00:35,320
widget is not working all right I will
小部件無法正常工作，我會

14
00:00:35,520 --> 00:00:38,049
be operating from my laptop and
通過我的筆記型電腦操作
Operating:ops
15
00:00:38,250 --> 00:00:39,969
Microsoft's we do have a lot of
微軟的我們確實有很多

16
00:00:40,170 --> 00:00:43,178
experience bringing ml to production we
將ml投入生產的經驗

17
00:00:43,378 --> 00:00:44,739
bring together your data we bring
彙集您帶來的資料

18
00:00:44,939 --> 00:00:48,759
together cloud your models and our
一起遮蓋您的模型，以及
Cloud:雲端
19
00:00:48,960 --> 00:00:51,009
job is really to help customers large
真的是説明大客戶的工作

20
00:00:51,210 --> 00:00:52,869
customers small customers whatever it is
客戶小客戶，無論是什麼

21
00:00:53,070 --> 00:00:55,869
help you move through and this is
幫助您通過，這是

22
00:00:56,070 --> 00:00:57,579
something that we have a lot of
我們有很多東西

23
00:00:57,780 --> 00:01:00,038
experience doing we've have a lot of
經歷過，我們有很多

24
00:01:00,238 --> 00:01:02,979
internal experience around Microsoft
 mic Microsoft的內部經驗

25
00:01:03,179 --> 00:01:06,189
Research and an ml generally with many
研究和毫升一般與許多

26
00:01:06,390 --> 00:01:08,168
of the most recent benchmarks and
最新的基準
Benchmarks:test for your computer speed
27
00:01:08,368 --> 00:01:09,730
achievements from coming out of
的成就來自

28
00:01:09,930 --> 00:01:11,439
Microsoft Research we're really 
微軟研究院，我們真的非常

29
00:01:11,640 --> 00:01:13,509
proud of that and of course we do give
為此感到自豪，我們當然會給予

30
00:01:13,709 --> 00:01:15,009
all those back to the research community
所有那些回到研究界的人

31
00:01:15,209 --> 00:01:17,528
in the form of open papers and notebooks
以打開的紙和筆記本的形式
Form:way
32
00:01:17,728 --> 00:01:21,909
and data and the reality is that ml does
和數據，而現實是ml確實

33
00:01:22,109 --> 00:01:24,849
touch every aspect of Microsoft today
觸及Microsoft的各個方面
Aspect:a part
34
00:01:25,049 --> 00:01:26,679
literally every one of these logos and
實際上，這些徽標中的每一個和
Literally:actually
35
00:01:26,879 --> 00:01:30,450
many more from your customers clients
客戶的更多客戶

36
00:01:30,650 --> 00:01:33,878
rich clients dead clients are you know
有錢人客戶死了你知道嗎
:everybody use ml
37
00:01:34,078 --> 00:01:35,230
thin clients whatever they may be
瘦客戶無論他們是什麼

38
00:01:35,430 --> 00:01:39,039
Xbox phone you name it we're using ml in
您命名的Xbox手機，我們正在使用ml in 
Xbox:like switch
39
00:01:39,239 --> 00:01:41,349
all of these various places and we're
所有這些不同的地方，我們

40
00:01:41,549 --> 00:01:45,219
using it at enormous scale you know a
大規模使用它，您知道
Enormous:so big
41
00:01:45,420 --> 00:01:47,259
hundred and eighty million office users
一億八千萬辦公室用戶

42
00:01:47,459 --> 00:01:52,209
today every day use often features
今天每天經常使用我的功能

43
00:01:52,409 --> 00:01:55,659
in office we have 18 billion queries
在辦公室，我們有180億個查詢
Queries:question and other question to….
44
00:01:55,859 --> 00:01:58,058
asked of Cortana which is obviously rich
問到Cortana，這顯然很豐富

45
00:01:58,259 --> 00:02:00,730
NLP and other things and six point five
 NLP和其他東西和六點五

46
00:02:00,930 --> 00:02:03,549
trillion security events evaluated every
每萬億安全事件評估一次
trillion :1,000,000,000,000
evaluated:how good something is
47
00:02:03,750 --> 00:02:06,698
day there's simply no way that we could
一天根本沒有辦法

48
00:02:06,899 --> 00:02:09,130
operate and process this data
操作和處理此資料

49
00:02:09,330 --> 00:02:10,980
without something like machine learning
沒有像機器學習這樣的東西

50
00:02:11,180 --> 00:02:14,020
so this is the point in the slide
這就是幻燈片中的重點

51
00:02:14,219 --> 00:02:14,920
where everyone's like wow that does
每個人都喜歡哇

52
00:02:15,120 --> 00:02:17,590
sound really great except they also say
聽起來真的很棒，除了他們也說

53
00:02:17,789 --> 00:02:21,370
this right ml is hard and it's really on
正確的毫升很難，而且確實在

54
00:02:21,569 --> 00:02:23,560
us that those that build these platforms
我們那些建立這些平臺的人

55
00:02:23,759 --> 00:02:26,530
in the ml community to help folks get
在ml社區中幫助人們獲得
Folks:people
56
00:02:26,729 --> 00:02:29,590
there better because we know something
那裡更好，因為我們知道一些

57
00:02:29,789 --> 00:02:32,200
that a lot of new people to ml don't
有很多新朋友不願意

58
00:02:32,400 --> 00:02:35,650
know and that's the following today a
知道，那就是今天的下一個

59
00:02:35,849 --> 00:02:39,340
lot of people new to ml think that it's
很多剛接觸毫升的人都認為這是

60
00:02:39,539 --> 00:02:42,070
all about the model and I understand
關於模型的一切，我瞭解

61
00:02:42,270 --> 00:02:44,350
why every new article out there talks
為什麼每一篇新文章都在談論

62
00:02:44,550 --> 00:02:46,300
about well you know Google just released
關於您知道Google剛剛發佈的

63
00:02:46,500 --> 00:02:48,460
Bert or Microsoft to release this and
伯特或微軟發佈此

64
00:02:48,659 --> 00:02:50,650
you know alphago did this and it's
而且您知道alphago做到了，這是
Alpha:the leader and best
65
00:02:50,849 --> 00:02:52,180
all about this amazing model that they
他們關於這個驚人的模型的一切

66
00:02:52,379 --> 00:02:54,189
built but it's not it's about the data
內置的，但這不是關於資料的

67
00:02:54,389 --> 00:02:56,920
processing and cleaning and all the
處理和清潔以及所有

68
00:02:57,120 --> 00:02:58,509
various things that are involved and
涉及的各種事物

69
00:02:58,709 --> 00:03:01,150
actually bringing something to
實際上帶來了一些東西

70
00:03:01,349 --> 00:03:04,689
production because that's the nature of
生產，因為這就是

71
00:03:04,889 --> 00:03:05,860
machine learning
機器學習

72
00:03:06,060 --> 00:03:08,860
it is these many many micro services
這是許多許多微服務

73
00:03:09,060 --> 00:03:10,360
each of which have a very specific
每個都有一個非常具體的

74
00:03:10,560 --> 00:03:12,310
functionality often very specific
功能通常非常具體

75
00:03:12,509 --> 00:03:14,710
tooling that do very specific things
做非常具體的事情的工具

76
00:03:14,909 --> 00:03:16,960
well but then need to be coupled
好，但是需要結合

77
00:03:17,159 --> 00:03:19,330
together in an intelligent way and if
以一種聰明的方式在一起，如果
Intelligent:smart
78
00:03:19,530 --> 00:03:22,180
you just focus on the model then you're
您只關注模型，然後

79
00:03:22,379 --> 00:03:25,450
gonna be in trouble and I know what
會遇到麻煩，我知道

80
00:03:25,650 --> 00:03:27,580
you're saying you're saying your data
你是說你是在說你的數據

81
00:03:27,780 --> 00:03:29,620
scientists and you don't care and I
科學家，你不在乎，我

82
00:03:29,819 --> 00:03:33,280
believe you a little bit but I'm here to
相信你一點，但我在這裡

83
00:03:33,479 --> 00:03:36,069
tell you you actually do and the reason
告訴你你實際做的事以及原因

84
00:03:36,269 --> 00:03:40,500
is tweets like this right models are
是這樣的推文，正確的模型是
IDGAF:I don’t give a fuck= I don’t care
85
00:03:40,699 --> 00:03:43,750
relatively easy to build but they are
相對容易構建，但它們
Relatively:more or less
86
00:03:43,949 --> 00:03:47,380
very hard to roll out because more often
很難推出，因為更多時候
hard to roll out:make people use
87
00:03:47,580 --> 00:03:49,689
than not data scientists operate in a
而不是資料科學家在

88
00:03:49,889 --> 00:03:51,460
way that they are familiar with they
他們熟悉的方式

89
00:03:51,659 --> 00:03:53,319
understand their tools and they build
瞭解他們的工具，他們建立

90
00:03:53,519 --> 00:03:55,660
using their tooling and local laptops
使用他們的工具和本地筆記型電腦

91
00:03:55,860 --> 00:03:58,210
and local clusters but they don't know
和本地集群，但他們不知道

92
00:03:58,409 --> 00:03:59,770
how to reach out and roll it to
如何伸出手並推向

93
00:03:59,969 --> 00:04:01,480
production and the reason is  because
生產的原因是因為

94
00:04:01,680 --> 00:04:04,719
you have this separation right the data
你有這種分離權的資料

95
00:04:04,919 --> 00:04:06,219
scientists over here they're trying to
他們正在嘗試的科學家

96
00:04:06,419 --> 00:04:08,500
iterate as quickly as she can she wants
盡可能快地反覆運算
Iterate:explain
97
00:04:08,699 --> 00:04:10,120
to use frameworks and tooling she
使用框架和工具

98
00:04:10,319 --> 00:04:12,520
understands she wants to mix and match
知道她想混搭

99
00:04:12,719 --> 00:04:15,069
tools the absolute latest build of
工具的絕對最新版本

100
00:04:15,269 --> 00:04:17,170
tensorflow or PI Torture or
 tensorflow或PI Torture y瑪瑙或

101
00:04:17,370 --> 00:04:19,689
whatever it may be you know somebody
不管你認識誰

102
00:04:19,889 --> 00:04:21,430
from Carnegie Mellon just launched a
來自卡耐基梅隆大學的
Carnegie Mellon:有名的大學
103
00:04:21,629 --> 00:04:24,699
brand new tool around reproducibility
圍繞重現性的全新工具
Reproducibility:how easy to make copy
104
00:04:24,899 --> 00:04:26,540
and it's respectability and that's
這是受人尊敬的

105
00:04:26,740 --> 00:04:28,309
she should have the capabilities to do
她應該有能力去做

106
00:04:28,509 --> 00:04:30,829
that she also wants to not worry about
她也不想擔心

107
00:04:31,029 --> 00:04:32,210
management because it's just her laptop
管理因為這只是她的筆記型電腦

108
00:04:32,410 --> 00:04:33,800
you know something goes wrong she
刮鬍子你知道出事了她

109
00:04:34,000 --> 00:04:36,170
can flatten it and restart and on the
可以將其展平並重新啟動，然後在

110
00:04:36,370 --> 00:04:37,579
other hand she wants unlimited scale
另一方面，她想要無限的規模

111
00:04:37,779 --> 00:04:39,319
she's got a paper due you know on
她有一份論文，因為你知道

112
00:04:39,519 --> 00:04:41,718
Thursday at 5:00 p.m. she wants all the
星期四下午5:00，她想要所有

113
00:04:41,918 --> 00:04:43,100
GPUs in the world in order to achieve
為了實現世界上的GPU 
GPUs:graphics(圖形) processing unit
114
00:04:43,300 --> 00:04:46,369
the results she needs on the other hand
另一方面，她需要的結果

115
00:04:46,569 --> 00:04:49,100
you have the right and she needs
你有權利，她需要

116
00:04:49,300 --> 00:04:51,800
consistency she needs observability she
一致性，她需要可觀察性，她

117
00:04:52,000 --> 00:04:53,420
needs to reuse tooling that's already
需要重用已經存在的工具

118
00:04:53,620 --> 00:04:55,490
been approved by her organization
被她的組織批准

119
00:04:55,689 --> 00:04:57,350
because they have support and things
因為他們有支持和東西

120
00:04:57,550 --> 00:05:00,410
built in and she needs uptime if the if
內置，如果需要，她需要正常執行時間
Uptime:time in the computer
121
00:05:00,610 --> 00:05:02,240
things are constantly changing under the
在不斷變化的情況下

122
00:05:02,439 --> 00:05:05,778
hood with no records of what's going on
引擎蓋，沒有發生任何事情的記錄
under the hood:parts inside
123
00:05:05,978 --> 00:05:09,649
then that's not gonna work now I am here
那那不行了我現在在這裡

124
00:05:09,848 --> 00:05:12,139
to propose that we can bring them
提議我們可以帶他們去

125
00:05:12,339 --> 00:05:14,210
together and we're gonna do it
在一起，我們要做

126
00:05:14,410 --> 00:05:19,610
through ml ops now before I begin I want
在我開始之前，現在要通過ml ops 

127
00:05:19,810 --> 00:05:22,100
to talk about what the e2e machine
說說什麼e2e機
e2e: end to end=from here to there
128
00:05:22,300 --> 00:05:23,389
learning lifecycle looks like now you
學習生命週期看起來像現在

129
00:05:23,589 --> 00:05:24,860
saw a bunch of like boxes connected
看到一堆像盒子一樣的盒子

130
00:05:25,060 --> 00:05:26,749
together with arrows there but the main
與那裡的箭頭，但主要
Arrows->
131
00:05:26,949 --> 00:05:28,759
things that I think we need to identify
我認為我們需要確定的事情

132
00:05:28,959 --> 00:05:31,459
and solve for in an lifecycle is
並在生命週期中解決

133
00:05:31,658 --> 00:05:34,249
what you see here first you do have the
首先您在這裡看到的是

134
00:05:34,449 --> 00:05:36,050
development and deployment of the model
模型的開發和部署

135
00:05:36,250 --> 00:05:37,939
then you're gonna have to package it in
那麼你將不得不打包

136
00:05:38,139 --> 00:05:40,490
a way that can be used and migrated to
migrated:move to new place
一種可以使用和遷移到的方式

137
00:05:40,689 --> 00:05:42,499
in production you want to validate that
在生產中，您要驗證

138
00:05:42,699 --> 00:05:44,499
model behavior before you roll it out
在推出之前模擬行為

139
00:05:44,699 --> 00:05:46,699
then you want to deploy the model and
然後您要部署模型並
Deploy:調派
140
00:05:46,899 --> 00:05:48,170
then you want to monitor it and in
然後您要監視它並在

141
00:05:48,370 --> 00:05:49,819
monitoring it  is not just about
監控它不僅僅是

142
00:05:50,019 --> 00:05:51,410
having it out there and making sure it's
把它放在那裡並確保它

143
00:05:51,610 --> 00:05:53,930
up but it's in fact taking all that data
上升，但實際上是在吸收所有資料

144
00:05:54,129 --> 00:05:56,809
and feeding it back into the original so
並把它送回原來的

145
00:05:57,009 --> 00:05:58,430
that you can now train it again and be
現在您可以再次訓練它並成為

146
00:05:58,629 --> 00:05:59,350
smarter about it
對此更聰明

147
00:05:59,550 --> 00:06:01,639
so you may say you might have heard this
所以你可能會說你可能聽說過

148
00:06:01,839 --> 00:06:04,819
before and the reality is you have you
以前，現實是你擁有你

149
00:06:05,019 --> 00:06:06,680
heard this several years ago when things
幾年前聽說過

150
00:06:06,879 --> 00:06:08,240
started getting kicked off around get
開始在周圍開始
kicked off:
kickstart:there has many new project in WEBSITE
151
00:06:08,439 --> 00:06:10,968
ops and that was the idea that you could
操作，那是您可以

152
00:06:11,168 --> 00:06:14,749
start with get and record everything
從獲取開始並記錄一切

153
00:06:14,949 --> 00:06:16,129
that you were doing relative to your
你相對於你所做的

154
00:06:16,329 --> 00:06:19,218
overall pipeline iterate very quickly on
整個管道非常快速地反覆運算

155
00:06:19,418 --> 00:06:20,629
that pipeline and then once that
該管道，然後一旦

156
00:06:20,829 --> 00:06:22,730
pipeline had passed all its tests and
管道通過了所有測試，並且

157
00:06:22,930 --> 00:06:24,319
humans had looked at it and said yes
人們看著它說是

158
00:06:24,519 --> 00:06:26,809
this is ready to go you trigger a second
準備好了，您觸發第二個
Trigger:to start something
159
00:06:27,009 --> 00:06:29,149
cycle and that second cycle is rolling
週期，第二個週期正在滾動

160
00:06:29,348 --> 00:06:31,338
it out to production but again it's all
投入生產，但這又是全部

161
00:06:31,538 --> 00:06:33,259
driven off get you don't have someone
被趕走，你沒有人

162
00:06:33,459 --> 00:06:35,209
in the middle there introducing any new
在中間介紹任何新的

163
00:06:35,408 --> 00:06:37,160
changes because those wouldn't have been
變化，因為那些不會

164
00:06:37,360 --> 00:06:39,050
tested and relied or
經過測試並依賴

165
00:06:39,250 --> 00:06:41,090
tested and observed and made sure that
測試並觀察並確保

166
00:06:41,290 --> 00:06:43,340
they pass all your bars and what that
他們通過了你所有的酒吧，那

167
00:06:43,540 --> 00:06:45,170
really gets to you at is those lines
真正讓您著迷的是那些臺詞

168
00:06:45,370 --> 00:06:47,000
at the bottom you get velocity plus
在底部，您獲得速度加
Velocity:speed for something
169
00:06:47,199 --> 00:06:51,590
security so what we need is ml ops right
安全性，所以我們需要的是ml ops 

170
00:06:51,790 --> 00:06:53,629
and ml ops is going to look like this
和ml ops看起來像這樣

171
00:06:53,829 --> 00:06:56,840
right you start with the data scientist
從資料科學家開始

172
00:06:57,040 --> 00:06:58,250
the data scientist is able to iterate
資料科學家能夠反覆運算
Iterate:explain

173
00:06:58,449 --> 00:07:00,319
exactly as quickly as she could and
盡其所能

174
00:07:00,519 --> 00:07:02,030
potentially even faster because you're
可能甚至更快，因為您
Potentially:something could but not sure
175
00:07:02,230 --> 00:07:03,470
giving her the Best of Breed tools and
給她最好的工具

176
00:07:03,670 --> 00:07:05,270
you're giving in to her in a system
你在系統中屈服於她

177
00:07:05,470 --> 00:07:06,560
where she doesn't have to think about
她不必思考的地方

178
00:07:06,759 --> 00:07:08,870
well this Python dependency didn't work
很好，這個Python依賴項不起作用

179
00:07:09,069 --> 00:07:10,370
with that other package that I had over
與我已經結束的其他包裹

180
00:07:10,569 --> 00:07:12,889
there you give her a path to install and
在那裡，您給她安裝的路徑， 

181
00:07:13,089 --> 00:07:14,600
manage these things in a very regular
定期管理這些東西

182
00:07:14,800 --> 00:07:17,660
way and then from that you start the
方式，然後從那開始

183
00:07:17,860 --> 00:07:18,350
second half
下半場

184
00:07:18,550 --> 00:07:20,600
right you have her finish you have
對，你有她的成就，你有

185
00:07:20,800 --> 00:07:23,000
her check in and move forward through
她簽入並繼續前進

186
00:07:23,199 --> 00:07:25,040
dev that's integrating it into the
開發人員將其集成到
ML+DEV+OPS
Integrating:整合
187
00:07:25,240 --> 00:07:27,259
application itself and then integrate
應用程式本身，然後集成

188
00:07:27,459 --> 00:07:29,569
again when it comes to rolling out to
再次涉及到

189
00:07:29,769 --> 00:07:32,629
production and when you do that you get
生產，當您這樣做時，您會得到

190
00:07:32,829 --> 00:07:34,430
these benefits right you get great
這些好處使您受益匪淺

191
00:07:34,629 --> 00:07:37,129
observability that means you exactly
可觀察性意味著您完全

192
00:07:37,329 --> 00:07:39,020
what's being rolled out so you're able
正在推出什麼，以便您能夠

193
00:07:39,220 --> 00:07:41,240
to observe it using standard toolings
使用標準工具進行觀察

194
00:07:41,439 --> 00:07:43,879
and reproducibility you get validation
和重現性得到驗證

195
00:07:44,079 --> 00:07:45,560
again because the stuff is checked in
再次因為東西被簽入

196
00:07:45,759 --> 00:07:48,560
and good you can do static validation
很好，您可以進行靜態驗證
Static:not moving 
validation:驗證
197
00:07:48,759 --> 00:07:50,870
you could do runtime validation by
您可以通過執行運行時驗證

198
00:07:51,069 --> 00:07:52,520
rolling it out to Canaries in a very
很快地將它推廣到金絲雀

199
00:07:52,720 --> 00:07:54,860
regimented way and then of course you
規整的方式，然後你當然
Regimented:array
200
00:07:55,060 --> 00:07:56,840
get reproducibility and auditability you
獲得可重複性和可審核性
Auditability:audit
201
00:07:57,040 --> 00:07:59,240
know that whatever that query was that
知道那個查詢是什麼

202
00:07:59,439 --> 00:08:01,069
prediction that happened at the end of
發生在末尾的預測

203
00:08:01,269 --> 00:08:03,079
that very long cycle you're able to
如此長的週期，您能夠

204
00:08:03,279 --> 00:08:05,300
trace back exactly what code rolled it
追溯確切的代碼

205
00:08:05,500 --> 00:08:07,790
out at every step of the way and in this
在這個過程中的每一步

206
00:08:07,990 --> 00:08:10,790
byte you get velocity plus security for
位元組，您可以獲得速度和安全性

207
00:08:10,990 --> 00:08:13,610
ml so some of the specific components
毫升，所以一些特定成分

208
00:08:13,810 --> 00:08:15,050
here and I'll just walk through this
在這裡，我將逐步流覽

209
00:08:15,250 --> 00:08:17,090
really quickly like I said you have the
真的很快，就像我說過的

210
00:08:17,290 --> 00:08:19,100
data scientist she checks in to get at
她簽到的資料科學家

211
00:08:19,300 --> 00:08:21,350
that point you do code set version or
那點你做代碼設置版本或

212
00:08:21,550 --> 00:08:23,210
you do code dataset and environment
您編寫資料集和環境代碼

213
00:08:23,410 --> 00:08:24,949
versioning you snapshot all of those
對所有這些版本進行快照

214
00:08:25,149 --> 00:08:27,230
things from there it automatically
從那裡自動進行的事情

215
00:08:27,430 --> 00:08:30,280
builds both the app and trains the model
同時構建應用程式和訓練模型

216
00:08:30,480 --> 00:08:32,750
and in that you're able to do hyper
而且你可以做超
Hyper:super
217
00:08:32,950 --> 00:08:34,218
parameter sweeps and all those kind of
參數掃描以及所有此類
Parameter:
Sweeps:掃毒
218
00:08:34,418 --> 00:08:36,949
things in an automated way you validate
驗證的自動化方式

219
00:08:37,149 --> 00:08:38,629
the model at that testing point using
使用該測試點的模型

220
00:08:38,830 --> 00:08:41,299
things like model validation and
諸如模型驗證和

221
00:08:41,500 --> 00:08:43,309
certification and things like that you
認證和類似的東西

222
00:08:43,509 --> 00:08:45,139
release the app and then finally you
釋放應用程式，然後最終您

223
00:08:45,339 --> 00:08:47,029
roll it out and so where that's what
推出它，那在哪裡

224
00:08:47,230 --> 00:08:48,169
we're gonna try and get to here today
我們今天要去這裡

225
00:08:48,370 --> 00:08:49,879
and we're gonna do it right here on
我們將在這裡繼續

226
00:08:50,080 --> 00:08:52,578
stage for you well technically
從技術上為您做好舞臺
Technically:具體

227
00:08:52,778 --> 00:08:56,659
so the question is this sounds pretty
所以問題是聽起來很漂亮

228
00:08:56,860 --> 00:08:58,969
good I'd like to take it on what should
好，我想拿什麼做

229
00:08:59,169 --> 00:09:01,519
I do in order to achieve that well I
我為了做到這一點而努力

230
00:09:01,720 --> 00:09:03,378
have a suggestion for you go join one of
有一個建議給你去參加其中之一

231
00:09:03,578 --> 00:09:05,029
these big companies because they already
這些大公司，因為他們已經

232
00:09:05,230 --> 00:09:08,029
have and they have it it's you know at
有，他們有，這是你知道的

233
00:09:08,230 --> 00:09:10,428
Google it's tf-x and at Facebook it's FB
 Google是TF-X，Facebook是FB 

234
00:09:10,629 --> 00:09:13,488
Lerner flow at uber it's Michelangelo
尤納的勒納潮流是米開朗基羅

235
00:09:13,688 --> 00:09:15,318
Microsoft has our own which is called
微軟有我們自己的被稱為

236
00:09:15,519 --> 00:09:19,039
ether and what this is it's a very
乙太，這是一個非常
Ether:magic in the air
237
00:09:19,240 --> 00:09:21,738
standard data science platform where a
標準資料科學平臺

238
00:09:21,938 --> 00:09:25,698
data scientist will come in and you know
資料科學家會進來，你知道

239
00:09:25,899 --> 00:09:27,469
be able to interact at a very high level
能夠進行高水準的互動

240
00:09:27,669 --> 00:09:30,139
see other people's experiments see her
看別人的實驗見她

241
00:09:30,339 --> 00:09:32,868
own experiments iterate and version on
自己的實驗進行反覆運算並在其上進行版本控制

242
00:09:33,068 --> 00:09:35,448
her own experiments and then basically
她自己的實驗，然後基本上

243
00:09:35,649 --> 00:09:37,099
download and begin to use those
下載並開始使用那些

244
00:09:37,299 --> 00:09:39,019
experiments rather than having to start
實驗而不是必須開始

245
00:09:39,220 --> 00:09:40,578
from scratch every time she can start
每次她可以從頭開始
Start from scratch:star from nothing
246
00:09:40,778 --> 00:09:42,618
there and and move forward rather than
在那裡，前進而不是

247
00:09:42,818 --> 00:09:46,669
you know resetting everything so you
你知道重置一切，所以你

248
00:09:46,870 --> 00:09:48,799
have rejected my suggestions
拒絕了我的建議

249
00:09:49,000 --> 00:09:50,479
that's very harsh of you I don't want to
對你來說我很苛刻
Harsh:too difficult
250
00:09:50,679 --> 00:09:51,498
work at a company so I'm gonna help you
在公司工作，所以我會幫助你

251
00:09:51,698 --> 00:09:55,039
by building your own ml ops platform
通過建立自己的ml ops平臺

252
00:09:55,240 --> 00:09:57,128
using first we'll do it with C ICD
首先使用C ICD 

253
00:09:57,328 --> 00:10:01,099
second we'll add in git and third we'll
第二，我們添加git，第三，我們添加

254
00:10:01,299 --> 00:10:04,459
add a soon you start with Q plug that
添加一個很快您開始使用Q外掛程式

255
00:10:04,659 --> 00:10:07,608
was get in the middle or get lab or
在中間或實驗室

256
00:10:07,808 --> 00:10:09,019
bitbucket or whatever it might be and
比特桶或其他可能的東西

257
00:10:09,220 --> 00:10:11,659
then finally we'll do it with a C ICD
最後，我們將使用C ICD 

258
00:10:11,860 --> 00:10:14,868
platform by the way I hope I did my best
平臺，希望我能做到最好

259
00:10:15,068 --> 00:10:16,818
I'm trying to be very neutral here all
我試圖在這裡保持中立
Neutral: 中立
260
00:10:17,019 --> 00:10:20,058
logos are the same size and by the way
徽標的大小和方式相同

261
00:10:20,259 --> 00:10:23,628
if you are thinking of inventing a new C
如果您正在考慮發明新的C 

262
00:10:23,828 --> 00:10:27,289
ICD platform there are so many there are
 ICD平臺有很多

263
00:10:27,490 --> 00:10:31,849
so many C ICD platforms it's enough just
這麼多的C ICD平臺就足夠了

264
00:10:32,049 --> 00:10:34,128
help another one help one that already
幫助另一個已經幫助的人

265
00:10:34,328 --> 00:10:39,318
exists I promise you okay so joking
存在，我保證你還可以，所以開玩笑
Exists:egg+sister
266
00:10:39,519 --> 00:10:42,738
aside let's go ml ops in the real I'm
拋開現實，我走了

267
00:10:42,938 --> 00:10:45,828
gonna take on a challenge that many
將會面臨許多挑戰

268
00:10:46,028 --> 00:10:48,318
people I don't know if Corey's here
我不知道寇里在這裡的人

269
00:10:48,519 --> 00:10:49,669
Corey are you here I know that you're
寇里你在這裡我知道你在

270
00:10:49,870 --> 00:10:51,108
attending cube con but I'm not sure you
參加立方體騙局，但我不確定你
Con:conference 活動
271
00:10:51,308 --> 00:10:52,789
hear Corey doesn't believe that at
聽到寇里不相信

272
00:10:52,990 --> 00:10:55,248
Multi-cloud exists I'm gonna say that it
多面存在，我要說
Multi:many
273
00:10:55,448 --> 00:10:56,959
does and I'm gonna say it does in the
會，我會說它在

274
00:10:57,159 --> 00:10:58,969
following way multi-cloud really does
按照多雲真正的方法

275
00:10:59,169 --> 00:11:00,558
exist in the real world and I'm gonna
存在于現實世界中，我要

276
00:11:00,759 --> 00:11:03,529
lay out a very standard scenario and
提出一個非常標準的方案
Scenario:common story
277
00:11:03,730 --> 00:11:05,379
then solve it for you on stage
然後在舞臺上為你解決

278
00:11:05,580 --> 00:11:08,409
so what when I say multi-cloud this is
所以當我說多雲時

279
00:11:08,610 --> 00:11:10,929
what it looks like you have at the top
頂部看起來像什麼

280
00:11:11,129 --> 00:11:12,939
you have cloud whatever cloud it might
無論有沒有雲，您都有雲

281
00:11:13,139 --> 00:11:14,889
be and at the bottom you have on Prem
 be and在Prem的最底端
on Prem:on the local hard drive
282
00:11:15,089 --> 00:11:17,109
and again that could be that's a special
再次可能那是一個特殊的

283
00:11:17,309 --> 00:11:18,639
case of a second cloud it could be a
第二個雲的情況可能是

284
00:11:18,839 --> 00:11:19,448
second cloud
第二朵雲

285
00:11:19,649 --> 00:11:20,769
it could be on Prem could be your local
它可能在Prem上可能是您本地的

286
00:11:20,970 --> 00:11:22,539
laptop doesn't matter and in the middle
筆記型電腦沒關係，在中間

287
00:11:22,740 --> 00:11:26,529
you have get the reason you've chosen
你有選擇的理由

288
00:11:26,730 --> 00:11:28,750
that top cloud is because it's
那頂雲是因為

289
00:11:28,950 --> 00:11:30,008
distributed is something that your
分發是你的事
Distributed:to give to many people
290
00:11:30,208 --> 00:11:31,899
datacenter can't offer you today maybe
資料中心今天可能無法為您提供服務

291
00:11:32,100 --> 00:11:32,828
it's up time
時間到了
up time:how much time it takes on the computer
292
00:11:33,028 --> 00:11:34,959
maybe it's locality because of
也許是因為
Locality(adv)
293
00:11:35,159 --> 00:11:37,059
regulatory reasons maybe it's just
監管方面的原因可能僅僅是
Regulatory(V):規制
294
00:11:37,259 --> 00:11:39,189
closer to your IOT deployments right you
更接近您的物聯網部署
deploy =Distributed:to give to many people

295
00:11:39,389 --> 00:11:41,139
don't want to have that far big Latancy
不想擁有那麼大的萊頓
Latency:ping = ping test
296
00:11:41,339 --> 00:11:43,419
sees and so on but the reason used to
看到等等，但以前的原因

297
00:11:43,620 --> 00:11:44,679
have stuff on prem is because you
在prem上有東西是因為你

298
00:11:44,879 --> 00:11:45,909
actually have a lot of data that you've
實際上有很多資料

299
00:11:46,110 --> 00:11:48,128
been collecting for many years and it's
已經收集了很多年了

300
00:11:48,328 --> 00:11:49,899
just a lot a big pain in the buttom to
屁股上的痛苦很大
a big pain:a big problem again

301
00:11:50,100 --> 00:11:52,599
move it to the cloud we see this
將其移動到雲中，我們看到了

302
00:11:52,799 --> 00:11:54,399
scenario all the time and it's whether
情況一直存在，這是否
Scenario:common situation
303
00:11:54,600 --> 00:11:56,198
or not you know maybe that's a poor
還是你不知道那是一個窮人

304
00:11:56,399 --> 00:11:57,698
latency connection or maybe you just
延遲連接，也許你只是
Latency:
305
00:11:57,899 --> 00:12:00,159
don't want to spend you know many many
不想花你知道很多很多

306
00:12:00,360 --> 00:12:01,689
millions of dollars on petabytes of data
 PB級數據的數百萬美元
小到大Megabyte->Gigabyte->terabyte(1000GB)->Petabytes(1000000GB)
307
00:12:01,889 --> 00:12:02,769
being held in the cloud because you
被困在雲端，因為你

308
00:12:02,970 --> 00:12:03,849
already have a bunch of disks and it's
已經有一堆光碟了

309
00:12:04,049 --> 00:12:06,149
doing its job you don't want to do that
做它的工作，你不想那樣做

310
00:12:06,350 --> 00:12:09,008
so and then we have our to cast of
所以然後我們要

311
00:12:09,208 --> 00:12:10,659
characters we have our data scientist
我們擁有資料科學家的角色

312
00:12:10,860 --> 00:12:13,448
she wants to iterate very quickly on the
她想非常快速地反覆運算
Iterate:explain
313
00:12:13,649 --> 00:12:15,819
the model and make sure that it works
模型並確保其有效

314
00:12:16,019 --> 00:12:17,799
and she's gonna want to train next to
她要在旁邊訓練

315
00:12:18,000 --> 00:12:20,198
the model but then the sres
模型，然後是sres 

316
00:12:20,399 --> 00:12:21,698
and the ml engineers are going to want
和毫升工程師會想要

317
00:12:21,899 --> 00:12:25,899
to host in the cloud so let's do this so
託管在雲中，所以我們這樣做

318
00:12:26,100 --> 00:12:28,508
what she does is first she's gonna check
她要做的是首先要檢查

319
00:12:28,708 --> 00:12:32,500
in her code and to get will be
在她的代碼，並獲得將是

320
00:12:32,700 --> 00:12:34,809
watched by the CI CD pipeline for a new
 CI CD管道正在監視新的

321
00:12:35,009 --> 00:12:37,179
check-in and that will automatically
簽入，這將自動

322
00:12:37,379 --> 00:12:41,198
trigger a cube flow pipeline it that cue
觸發一個提示的多維資料集流管道

323
00:12:41,399 --> 00:12:43,029
flow pipeline will process the data
流管道將處理資料

324
00:12:43,230 --> 00:12:44,769
first in whatever way it needs
首先以任何需要的方式

325
00:12:44,970 --> 00:12:46,959
processing or if it's already been
正在處理或已經處理過

326
00:12:47,159 --> 00:12:48,819
processed and check pointed then it can
處理並檢查指出，然後可以

327
00:12:49,019 --> 00:12:50,319
skip to the next step and cue flows
跳到下一步並提示流程

328
00:12:50,519 --> 00:12:51,429
smart enough to do that
足夠聰明地做到這一點

329
00:12:51,629 --> 00:12:54,479
it then will run a training run
然後它將進行一次訓練

330
00:12:54,679 --> 00:12:58,120
automatically on that process data again
再次自動處理該過程資料

331
00:12:58,320 --> 00:12:59,859
using whatever parameters you've passed
使用您傳遞的任何參數

332
00:13:00,059 --> 00:13:02,409
in via get right again this isn't
通過再次獲得權利這不是

333
00:13:02,610 --> 00:13:03,609
something where the data scientist is
資料科學家所在的地方

334
00:13:03,809 --> 00:13:05,318
picking it on the fly you want to be
隨時隨地挑選它

335
00:13:05,519 --> 00:13:08,258
extremely prescriptive about this once
關於這一次非常規範
Prescriptive:don’t give wrong code
336
00:13:08,458 --> 00:13:10,059
that's done it's gonna register the
這樣就可以註冊
Register:報名
337
00:13:10,259 --> 00:13:12,459
model at a central point which can be
可以在一個中心點建模

338
00:13:12,659 --> 00:13:15,669
picked up and then rolled out to the
拿起然後展開到

339
00:13:15,870 --> 00:13:17,139
cloud and in this case you would roll it
雲，在這種情況下，您將其滾動

340
00:13:17,339 --> 00:13:19,120
out to something like a canary end point
到金絲雀終點

341
00:13:19,320 --> 00:13:21,009
or a staging endpoint to make sure that
或暫存端點以確保

342
00:13:21,210 --> 00:13:24,069
it runs a human being often will after
它使一個人經常會

343
00:13:24,269 --> 00:13:26,500
the tests of paws come by make sure
通過檢查爪子來確保

344
00:13:26,700 --> 00:13:29,469
everything is operating properly that
一切運行正常

345
00:13:29,669 --> 00:13:31,809
the human being will then say okay it's
然後，人類會說，好的

346
00:13:32,009 --> 00:13:33,669
time for me to trigger the next step and
是我觸發下一步的時間了， 

347
00:13:33,870 --> 00:13:35,799
that's at that point it gets rolled out
到那時它被推出

348
00:13:36,000 --> 00:13:38,259
to a public service endpoint and use for
到公共服務端點並用於

349
00:13:38,460 --> 00:13:42,639
the model for production okay so that's
生產模型還可以，所以

350
00:13:42,840 --> 00:13:44,319
the high level let's see what this
高層次，讓我們看看這是什麼

351
00:13:44,519 --> 00:13:47,689
actually looks like when it's running
實際上看起來像是在運行時

352
00:13:47,779 --> 00:13:53,889
and yes you can okay well this is gonna
是的，你可以很好，這將會

353
00:13:54,090 --> 00:13:55,359
be very hard because I shoulder the
很難，因為我肩負著

354
00:13:55,559 --> 00:13:57,819
whole time alright so this is um you
整個時間都很好，所以這是你

355
00:13:58,019 --> 00:13:59,919
know the one downside of doing we're
知道做我們的一個缺點
Downside:壞處
356
00:14:00,120 --> 00:14:02,079
moving into ml is you have to record all
進入毫升是你必須記錄所有

357
00:14:02,279 --> 00:14:04,049
your videos because everything takes forever
您的視頻會永遠解決一切

358
00:14:04,250 --> 00:14:07,089
so I promise you can look at the
所以我保證你可以看看

359
00:14:07,289 --> 00:14:08,379
date I don't know where the data is on
日期，我不知道資料在哪裡

360
00:14:08,580 --> 00:14:10,000
anyone see a date on there this is
有人在那裡看到約會

361
00:14:10,200 --> 00:14:12,189
literally run last night so I promise
昨天晚上確實跑了，所以我保證

362
00:14:12,389 --> 00:14:14,109
you can go to this repo which is
你可以去這個倉庫

363
00:14:14,309 --> 00:14:15,879
public and download all the code and
公開並下載所有代碼， 

364
00:14:16,080 --> 00:14:18,939
have a good time okay where are you
玩得開心吧，你在哪裡

365
00:14:19,139 --> 00:14:23,199
okay so first is this running it is okay
好的，所以首先運行它是可以的

366
00:14:23,399 --> 00:14:25,539
so first what you see here this is a
首先，您在這裡看到的是

367
00:14:25,740 --> 00:14:28,719
very standard repo out there and it's
非常標準的倉庫在那裡

368
00:14:28,919 --> 00:14:30,309
got all the code in it and I let me show
裡面有所有代碼，我讓我展示

369
00:14:30,509 --> 00:14:31,870
you actually exactly what this code
您實際上正是這段代碼

370
00:14:32,070 --> 00:14:36,920
looks like here see this you cannot oh
看起來像這裡看到你不能哦

371
00:14:41,389 --> 00:14:46,059
okay so they always say never show code
好吧，所以他們總是說從不顯示代碼

372
00:14:46,259 --> 00:14:48,159
when you're doing a demo because
在進行演示時，因為

373
00:14:48,360 --> 00:14:49,779
everyone's eyes glaze over but these are
每個人的眼睛都凝視著，但這是
eyes glaze over:they don’t understand
374
00:14:49,980 --> 00:14:51,639
very smart room so I'm gonna trust you
非常聰明的房間，所以我會相信你

375
00:14:51,840 --> 00:14:53,949
your eyes are not going to leave over so
你的眼睛不會離開所以

376
00:14:54,149 --> 00:14:55,449
what you have here is this is a very
你在這裡是一個非常

377
00:14:55,649 --> 00:14:57,939
very standard CI CD pipeline this is
非常標準的CI CD管道，這是

378
00:14:58,139 --> 00:14:59,949
using Azure dev ops which is something
使用Azure開發人員操作

379
00:15:00,149 --> 00:15:02,409
that we use hosted dev ops CI CD on
我們使用託管的dev ops CI CD 
Host: the leader
380
00:15:02,610 --> 00:15:04,659
Azure but the Yambol is gonna look super
蔚藍但Yambol看起來超級好

381
00:15:04,860 --> 00:15:06,250
familiar because it follows very
熟悉，因為它遵循

382
00:15:06,450 --> 00:15:08,769
standard cid CD practices looks just
標準CID CD的做法看起來只是

383
00:15:08,970 --> 00:15:11,589
like jenkins and what you see here is at
就像詹金斯，你在這裡看到的是

384
00:15:11,789 --> 00:15:13,089
the top you see a trigger that means
在頂部，您會看到一個觸發器，這意味著

385
00:15:13,289 --> 00:15:14,620
it's triggering off the master once you
一旦你觸發了主人

386
00:15:14,820 --> 00:15:18,279
check in and here you have individual
簽入，這裡有個人

387
00:15:18,480 --> 00:15:20,199
steps this in fact does a build of
步驟這實際上是建立

388
00:15:20,399 --> 00:15:23,829
container and we do three builds here
容器，我們在這裡進行三個構建

389
00:15:24,029 --> 00:15:26,039
because that's what cube flow requires
因為那是多維資料集流所需要的

390
00:15:26,240 --> 00:15:28,809
cube flow needs every one of your steps
多維資料集流需要您執行的每一步

391
00:15:29,009 --> 00:15:31,089
to be built into a container and then
放在一個容器裡然後

392
00:15:31,289 --> 00:15:32,939
you roll that for
你為

393
00:15:33,139 --> 00:15:34,829
and into your cute flow pipeline and
並進入您可愛的流程管道， 

394
00:15:35,029 --> 00:15:37,469
what you're rolling in what the pipeline
您在管道中滾動什麼

395
00:15:37,669 --> 00:15:42,449
looks like is this and actually why
看起來是這樣，實際上是為什麼

396
00:15:42,649 --> 00:15:43,879
don't I get to this in just a second so
我不是一秒鐘就能做到這一點嗎

397
00:15:44,080 --> 00:15:48,329
we'll go back to our video here and like
我們將在這裡回到我們的視頻

398
00:15:48,529 --> 00:15:52,859
I said what you see is a standard repo
我說你看到的是標準回購

399
00:15:53,059 --> 00:15:56,969
or is my thing here and that was just
還是我在這裡的東西，那僅僅是

400
00:15:57,169 --> 00:15:58,709
the code that you saw me pointing at
您看到我指向的代碼

401
00:15:58,909 --> 00:16:01,289
earlier with standard pipelines and this
早期使用標準管道，這

402
00:16:01,490 --> 00:16:03,779
is a very important one it does burritos
是一個非常重要的墨西哥卷餅

403
00:16:03,980 --> 00:16:05,909
versus tacos you upload and it'll tell
與您上傳的炸玉米餅，它將告訴您

404
00:16:06,110 --> 00:16:06,870
you whether or not something Sabrina
你是否有薩布麗娜的東西

405
00:16:07,070 --> 00:16:08,519
virgin taco we're just a hardware heart
原始的炸玉米餅，我們只是硬體的心臟

406
00:16:08,720 --> 00:16:10,289
problem that you would think you know a
你以為自己知道的問題

407
00:16:10,490 --> 00:16:13,229
half-open burrito what you get the idea
半開卷餅你有什麼主意

408
00:16:13,429 --> 00:16:15,659
all right so we moving on here and you
好吧，我們繼續在這裡和你

409
00:16:15,860 --> 00:16:17,309
can see what it's doing is this code
可以看到這段代碼在做什麼

410
00:16:17,509 --> 00:16:19,289
that's in the repo right now is doing
現在在回購中

411
00:16:19,490 --> 00:16:21,569
transfer learning using very standard
使用非常標準的轉移學習

412
00:16:21,769 --> 00:16:23,459
transfer learning tool called mobile net
轉移學習工具稱為移動網

413
00:16:23,659 --> 00:16:28,579
and so we layer on top of that so
所以我們在此之上

414
00:16:28,779 --> 00:16:31,169
like I said this is just incredibly
就像我說的那樣
Incredibly:so much can’t believe
415
00:16:31,370 --> 00:16:34,289
pipeline code and these are the pipeline
管道代碼，這些是管道

416
00:16:34,490 --> 00:16:35,849
steps that you'll see here and I've into
您將在這裡看到的步驟，我已經進入

417
00:16:36,049 --> 00:16:38,099
those in just a second but the idea is
那些只是一秒鐘，但想法是

418
00:16:38,299 --> 00:16:41,849
that we want to show how when you check
我們想告訴您如何檢查

419
00:16:42,049 --> 00:16:43,139
in it's going to kick all these things
在其中踢所有這些東西
Kick:throw
420
00:16:43,340 --> 00:16:45,750
off so we come back here and we're gonna
離開，所以我們回到這裡，我們要

421
00:16:45,950 --> 00:16:52,259
hit merge on our pull request and it's
在我們的拉取請求上點擊合併， 
Merge:put somethings together
Request:要求
pull request:ask something from server
422
00:16:52,460 --> 00:16:53,279
gonna kick off there we go
要去那裡開始
kick off:to start
423
00:16:53,480 --> 00:16:57,120
so we're hitting merge and off we go
所以我們要合併然後離開

424
00:16:57,320 --> 00:16:59,759
and there you go so this is the UI for a
然後去，所以這是

425
00:16:59,960 --> 00:17:02,490
DevOps again all we did was hit merge
 DevOps我們所做的一切再次被合併

426
00:17:02,690 --> 00:17:05,309
and it all this kicked off so at this
這一切開始了

427
00:17:05,509 --> 00:17:07,200
point we're gonna go forward you can see
點，我們要前進，你可以看到

428
00:17:07,400 --> 00:17:09,059
there right there build number nine has
那邊有九號樓

429
00:17:09,259 --> 00:17:11,549
just kicked off and that is you know May
剛剛開始，那就是你知道的梅

430
00:17:11,750 --> 00:17:13,649
21st so you know I'm being honest here
 21號所以你知道我在這裡很誠實
21st:20+first
Honest: onest
431
00:17:13,849 --> 00:17:15,059
and you can see how the things are
你會看到事情如何

432
00:17:15,259 --> 00:17:17,339
aspiring here so in this case it's going
嚮往這裡，所以在這種情況下
Aspiring:to dream for something
433
00:17:17,539 --> 00:17:18,960
to go through those three build steps
完成這三個構建步驟

434
00:17:19,160 --> 00:17:20,220
where it builds each one of the
它在其中構建每個

435
00:17:20,420 --> 00:17:22,740
containers using standard as your build
使用標準容器的容器

436
00:17:22,940 --> 00:17:25,499
tools and again this can be docker this
工具，這又可以是docker this 

437
00:17:25,699 --> 00:17:26,759
could be builder this could be anything
可能是建設者，這可能是任何事情

438
00:17:26,959 --> 00:17:29,399
that makes sense for you and you've all
這對您有意義，而您所有人
makes sense:有道理
439
00:17:29,599 --> 00:17:31,139
seen docker build many many times so I'm
看到碼頭工人建造了很多次，所以我

440
00:17:31,339 --> 00:17:35,220
going to fast forward through those okay
快點走過去

441
00:17:35,420 --> 00:17:37,589
and then this last step is where you
然後這最後一步是您

442
00:17:37,789 --> 00:17:39,809
take all those containers and now you've
拿走所有這些容器，現在您已經

443
00:17:40,009 --> 00:17:42,899
uploaded them to in this case a CR but
在這種情況下將它們上傳到CR， 

444
00:17:43,099 --> 00:17:45,930
it could be any docker registry okay
可能是任何Docker註冊表都可以

445
00:17:46,130 --> 00:17:48,598
so now that is done and now we have the
所以現在完成了，現在我們有了

446
00:17:48,798 --> 00:17:50,129
interesting bit because that is the
有趣的是，因為那是
Bit:small thing
447
00:17:50,329 --> 00:17:52,559
build pipeline our steps here are first
建立管道我們的步驟是首先

448
00:17:52,759 --> 00:17:54,419
off a build pipeline where it takes
離開需要的構建管道

449
00:17:54,619 --> 00:17:57,509
those containers them and then we do
這些容器，然後我們做

450
00:17:57,710 --> 00:17:59,129
what's called a release pipeline and you
所謂的發佈管道，你
Release:to let something go
451
00:17:59,329 --> 00:18:01,049
want those to be separate right you want
想要那些分開的權利

452
00:18:01,250 --> 00:18:02,250
to make sure the build is complete that
確保構建完成

453
00:18:02,450 --> 00:18:04,169
all the artifacts pass and already go
所有的文物都過去了，已經過去了

454
00:18:04,369 --> 00:18:05,940
and now we're going to do this release
現在我們要發佈此版本

455
00:18:06,140 --> 00:18:07,829
pipeline and the release pipeline is
管道和發佈管道是

456
00:18:08,029 --> 00:18:11,279
where it reaches out to cube flow and
它伸向立方體流動的地方
reaches out:try to catch something from far away
457
00:18:11,480 --> 00:18:13,680
executes and you can see this here it's
執行，您可以在這裡看到它
Executes:.exe
458
00:18:13,880 --> 00:18:16,769
reaching out using a swagger client that
使用招搖的客戶

459
00:18:16,970 --> 00:18:19,139
we wrote to connect directly to the cue
我們寫了直接連接到提示

460
00:18:19,339 --> 00:18:20,789
flow API you can see there it's
流API，您可以看到它的

461
00:18:20,990 --> 00:18:24,479
forwarding the connection and here you
轉發連接，在這裡您

462
00:18:24,679 --> 00:18:26,098
can see the run these are the rough
可以看到運行這些是粗糙的

463
00:18:26,298 --> 00:18:28,769
he had previously we run 84 we had a lot
他以前有我們跑84我們有很多

464
00:18:28,970 --> 00:18:31,019
of debugging not cue flows fault our
調試不提示流故障我們
Fault:mistake
465
00:18:31,220 --> 00:18:33,429
fault
故障

466
00:18:34,390 --> 00:18:37,858
and presto we're at run eighty-five
前提是我們現在快八十五了
Presto(口語):immediately馬上
467
00:18:38,058 --> 00:18:41,430
right so again I want to call back
對，所以我想再次致電

468
00:18:41,630 --> 00:18:43,108
because this is a small but special
因為這很小但是很特別

469
00:18:43,308 --> 00:18:45,180
thing all I did was kick hit get all I
我所做的就是踢得到我所有

470
00:18:45,380 --> 00:18:46,919
did was check in and merge my final PR
做的是檢查併合並我的最終PR 

471
00:18:47,119 --> 00:18:48,799
and it kicked off all of this goodness
它開始了所有的美好

472
00:18:49,000 --> 00:18:51,389
so now we were sending through here and
所以現在我們通過這裡發送

473
00:18:51,589 --> 00:18:54,119
you can see a rerunning know okay so
你可以看到一個重新運行的知道，所以
Rerunning:run again
474
00:18:54,319 --> 00:18:56,579
this is a standard cue put pipeline I'd
這是我想要的標準提示放置管道

475
00:18:56,779 --> 00:18:58,019
already described it does pre
已經描述過了

476
00:18:58,220 --> 00:18:59,789
processing here it's mounting in Azure
在這裡處理它正在Azure中安裝

477
00:18:59,990 --> 00:19:02,190
blob and you can see it watching the
一滴，你可以看到它看著

478
00:19:02,390 --> 00:19:03,180
logs right there
記錄在那裡

479
00:19:03,380 --> 00:19:05,309
and you can see the artifacts once
你可以一次看到文物

480
00:19:05,509 --> 00:19:06,899
that's done it automatically moves
完成後，它會自動移動

481
00:19:07,099 --> 00:19:09,750
forward the containers been created and
轉發已創建的容器，並

482
00:19:09,950 --> 00:19:11,399
here it's actually doing a training run
這實際上是在進行訓練

483
00:19:11,599 --> 00:19:13,259
right now it's using the latest
現在正在使用最新的

484
00:19:13,460 --> 00:19:16,049
tensorflow 2.0 you can see and not just
你可以看到的不僅僅是tensorflow 2.0 
2.0:two point o
485
00:19:16,250 --> 00:19:17,700
tensorflow 2.0 but let me just highlight
 tensorflow 2.0，但讓我強調一下

486
00:19:17,900 --> 00:19:19,680
that right there it's using GPUs
就在那裡，它正在使用GPU 

487
00:19:19,880 --> 00:19:22,559
natively on kubernetes directly through
直接通過kubernetes在本地

488
00:19:22,759 --> 00:19:27,180
this check it ok and it's going to
這項檢查確定，它將

489
00:19:27,380 --> 00:19:29,190
operate and run quite fast much faster
快速運行和運行

490
00:19:29,390 --> 00:19:30,899
than it ran when it was running on my
比它在我的電腦上運行時所運行的

491
00:19:31,099 --> 00:19:36,180
local machine forward there and now it's
本地機器向前走，現在

492
00:19:36,380 --> 00:19:38,190
doing registration and this is where you
做註冊，這是你的地方

493
00:19:38,390 --> 00:19:40,588
start to mix and match between things
開始在事物之間融合和匹配

494
00:19:40,788 --> 00:19:42,000
running in queue flow and things that
在佇列流中運行以及

495
00:19:42,200 --> 00:19:44,608
you may want to run in the cloud so in
您可能想在雲中運行，所以在

496
00:19:44,808 --> 00:19:46,379
this case it's going to do this last
在這種情況下，它將持續到最後

497
00:19:46,579 --> 00:19:49,889
register step in Azure we have a service
在Azure中註冊步驟我們有一項服務

498
00:19:50,089 --> 00:19:51,809
called the model management service this
稱為模型管理服務

499
00:19:52,009 --> 00:19:54,419
is basically a sophisticated storage
基本上是一個複雜的存儲
Sophisticated:very developed
500
00:19:54,619 --> 00:19:56,399
location which understands machine
瞭解機器的位置

501
00:19:56,599 --> 00:19:59,159
learning concepts first-class way
學習概念的一流方法
Concepts:idea
502
00:19:59,359 --> 00:20:01,229
and by that what I mean is you're able
我的意思是說你有能力

503
00:20:01,429 --> 00:20:02,789
to understand whether or not something
瞭解是否有東西

504
00:20:02,990 --> 00:20:05,369
needs versioning how to compare versions
需要版本控制如何比較版本

505
00:20:05,569 --> 00:20:06,960
within each other what the performance
彼此之間的表現

506
00:20:07,160 --> 00:20:08,460
of something is and you're able to
的東西是，你能夠

507
00:20:08,660 --> 00:20:11,819
visualize it in a first-class way and so
以一流的方式視覺化
Visualize:to see something with your eye close
508
00:20:12,019 --> 00:20:13,049
what it's going to do here at this last
這最後要在這裡做什麼

509
00:20:13,250 --> 00:20:15,659
step here are three versions that we ran
這是我們運行的三個版本

510
00:20:15,859 --> 00:20:19,379
already and once it's done you can see
已經，一旦完成，您可以看到

511
00:20:19,579 --> 00:20:22,129
here this is the model registry service
這是模型註冊服務

512
00:20:22,329 --> 00:20:27,428
it's going to kick it off any minute
它會在任何時候開始
any minute:very soon
513
00:20:28,599 --> 00:20:32,069
yeah okay so you can see here it's using
是的，所以你可以看到這裡正在使用

514
00:20:32,269 --> 00:20:35,039
in this case the azure SDK natively
在這種情況下，本機azure SDK 

515
00:20:35,240 --> 00:20:37,079
inside of cube flow it's able to reach
在多維資料集流內部，它可以達到

516
00:20:37,279 --> 00:20:38,879
out and you can see they're using our
出去，你可以看到他們正在使用我們的

517
00:20:39,079 --> 00:20:40,829
service principles service passwords the
服務原則服務密碼
Principles:rules
518
00:20:41,029 --> 00:20:42,960
subscription information it's able to
能夠訂閱的資訊
Subscription:訂閱
519
00:20:43,160 --> 00:20:45,539
take that hc5 file and simply push it up
提取該hc5文件，然後將其向上推

520
00:20:45,740 --> 00:20:48,119
to the model registry service which then
到模型註冊服務，然後

521
00:20:48,319 --> 00:20:50,039
will kick off the second half of the
將在下半場開始

522
00:20:50,240 --> 00:20:55,559
solution and you can see there so that's
解決方案，您可以在那裡看到，所以

523
00:20:55,759 --> 00:20:57,690
roughly what it is you know rolling
大概是什麼，你知道滾動

524
00:20:57,890 --> 00:21:00,029
things out here's where you can see some
在這裡可以看到一些東西

525
00:21:00,230 --> 00:21:01,079
of the richness that we're able to
我們能夠做到的豐富

526
00:21:01,279 --> 00:21:02,598
upload and the metadata that we have
上傳以及我們擁有的中繼資料
Metadata:data about data
527
00:21:02,798 --> 00:21:06,990
relative to these things and no I don't
相對於這些東西，不，我不

528
00:21:07,190 --> 00:21:13,108
need you they were real so that's what
需要你，他們是真實的，那就是

529
00:21:13,308 --> 00:21:15,389
model that's what Ops looks like in
這就是ops的樣子

530
00:21:15,589 --> 00:21:16,950
reality right and from that point
現實的權利，從那時起

531
00:21:17,150 --> 00:21:18,089
forward you know I just rolled out a
轉發，你知道我剛剛推出了

532
00:21:18,289 --> 00:21:19,289
service endpoint everyone seen that
每個人都看到的服務端點

533
00:21:19,490 --> 00:21:23,069
right nothing special there but that's
沒什麼特別的，但是那是

534
00:21:23,269 --> 00:21:24,358
what we're talking about this is
我們正在談論的是

535
00:21:24,558 --> 00:21:26,159
something where you had a cube flow
立方體流動的地方

536
00:21:26,359 --> 00:21:29,190
cluster that was in no way connected to
完全沒有連接的集群

537
00:21:29,390 --> 00:21:31,289
you know any of the more sophisticated
你知道任何更複雜的

538
00:21:31,490 --> 00:21:33,598
azor or other components and with just
 azor或其他組件

539
00:21:33,798 --> 00:21:36,539
running standard kubernetes and standard
運行標準的kubernetes和標準

540
00:21:36,740 --> 00:21:38,819
cube flow and the reality is that
立方體流動，而現實是

541
00:21:39,019 --> 00:21:41,309
gives you the opportunity to do the
給你機會去做

542
00:21:41,509 --> 00:21:42,869
training in a way that makes sense for
進行有意義的培訓

543
00:21:43,069 --> 00:21:45,299
your organization you don't have to take
您的組織，您不必

544
00:21:45,500 --> 00:21:46,769
your data you don't have to change the
您的資料，您不必更改

545
00:21:46,970 --> 00:21:48,450
way your processes work you don't have
您沒有的流程工作方式

546
00:21:48,650 --> 00:21:51,598
to adopt some crazy new standard just
採取一些瘋狂的新標準
Adopt:to take something not yours and raise it
547
00:21:51,798 --> 00:21:53,848
because that looks slightly faster this
因為這看起來快一點
Slightly:a little
548
00:21:54,048 --> 00:21:55,979
is an open platform where you can pick
是一個開放的平臺，您可以在其中選擇

549
00:21:56,179 --> 00:21:57,839
and choose and plug in the components
然後選擇並插入元件

550
00:21:58,039 --> 00:21:59,789
that make sense to you and that's the
對你有意義，那就是

551
00:21:59,990 --> 00:22:01,319
power that things like open source
像開源這樣的東西

552
00:22:01,519 --> 00:22:03,750
and things like Q flow provide now I
現在像Q flow這樣的東西

553
00:22:03,950 --> 00:22:05,219
know what you're saying it sounds like a
知道你在說什麼聽起來像

554
00:22:05,419 --> 00:22:07,529
lot of work I would like to recall this
我想回顧很多工作
Recall:remember
555
00:22:07,730 --> 00:22:09,659
statement here you know what's a lot of
聲明在這裡，你知道很多

556
00:22:09,859 --> 00:22:10,829
work eleven months sitting around
工作十一個月

557
00:22:11,029 --> 00:22:11,940
without your model shipping out to
無需將模型運送到

558
00:22:12,140 --> 00:22:12,399
production
生產

559
00:22:12,599 --> 00:22:15,039
that's a lot of work this you know okay
這是很多工作，你知道嗎

560
00:22:15,240 --> 00:22:16,509
you have to spend a few minutes making
你必須花幾分鐘的時間

561
00:22:16,710 --> 00:22:18,129
sure that service principles are able to
確保服務主體能夠
Principles: 主體
562
00:22:18,329 --> 00:22:20,558
cross clouds but other than that you'll
跨雲，但除此之外， 

563
00:22:20,759 --> 00:22:24,698
be fine and you're not just fine
很好，你不只是很好

564
00:22:24,898 --> 00:22:27,549
right you're actually moving faster and
對，實際上您的移動速度更快， 

565
00:22:27,750 --> 00:22:29,438
and that's what we're really trying to
這就是我們真正想要的

566
00:22:29,638 --> 00:22:32,318
provide here data scientists understand
提供資料科學家瞭解
data scientists:algorithm
567
00:22:32,519 --> 00:22:36,519
their problem areas but too often they
他們的問題領域，但他們經常

568
00:22:36,720 --> 00:22:38,318
have thrown things over the wall and
把東西扔在牆上， 

569
00:22:38,519 --> 00:22:41,169
asked production to ultimately you know
要求生產，最終你知道
Ultimately:in the end
570
00:22:41,369 --> 00:22:43,568
deal with a lot of the hardness what
處理很多硬度什麼
Hardness:difficult thing
571
00:22:43,769 --> 00:22:45,188
we're saying is we want to bring
我們是說我們要帶

572
00:22:45,388 --> 00:22:47,799
through things like ml ops we want to
通過像ml ops這樣的事情，我們想要

573
00:22:48,000 --> 00:22:50,108
bring the same advances that get ops
帶來同樣的進步

574
00:22:50,308 --> 00:22:52,808
brought to app developers we want to
帶給我們想要的應用程式開發人員

575
00:22:53,009 --> 00:22:55,568
help data scientists be part of how to
説明資料科學家成為如何

576
00:22:55,769 --> 00:22:57,789
bring things to production and get into
把東西帶入生產並進入

577
00:22:57,990 --> 00:23:00,039
end ownership and even better than that
最終擁有權甚至比這更好

578
00:23:00,240 --> 00:23:02,469
help them learn real software
説明他們學習真正的軟體

579
00:23:02,669 --> 00:23:04,659
engineering best practices like for too
工程最佳實踐，例如

580
00:23:04,859 --> 00:23:06,308
long we've  said hey data
很久以來，我們已經說過嘿資料

581
00:23:06,509 --> 00:23:08,108
scientists you know don't worry about
你認識的科學家不用擔心

582
00:23:08,308 --> 00:23:11,259
you know sweet best practices you know
你知道甜蜜的最佳實踐

583
00:23:11,460 --> 00:23:13,959
come back when you're older no that is
當你長大回來時，那不是

584
00:23:14,159 --> 00:23:16,000
not okay these are these are software
不好，這些是軟體

585
00:23:16,200 --> 00:23:17,740
engineers they want to help get things
他們想要幫助的工程師

586
00:23:17,940 --> 00:23:19,240
into reduction they want their models to
減少他們想要他們的模型

587
00:23:19,440 --> 00:23:21,399
be used just as much as you do we need
盡可能多地使用，我們需要

588
00:23:21,599 --> 00:23:23,709
to help them bridge the gap but not by
幫助他們彌合差距，但不能
bridge the gap: 彌合差距


