-
Notifications
You must be signed in to change notification settings - Fork 3
/
index.html
149 lines (144 loc) · 8.54 KB
/
index.html
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
---
title: "ML Ops: Machine Learning Operations"
layout: default
---
<!--<section class="header">
<div class="container">
<div class="header__flex">
<img class="header__logo" src="/assets/mlops_header_logo.svg" alt="MLOps Logo">
<div class="header__content">
<div class="header__content__left">
<h1 class="header__title">Machine Learning Operations</h1>
<p class="header__lead">With Machine Learning Model Operationalization Management (MLOps), we want
to provide an end-to-end machine learning development process to design, build and manage
reproducible, testable, and evolvable ML-powered software.</p>
</div>
<div class="header__content__right">
<img class="header__content__keyvisual" src="/assets/mlops_visual.svg" alt="MLOps Logo">
</div>
</div>
<a href="#gettingstarted"><img class="icon" src="/assets/icons/Icon__ButtonDown.svg"
alt="Please scroll"></a>
</div>
</div>
</section>-->
<section id="gettingstarted" class="gettingstarted">
<div class="container">
<h2>Getting started</h2>
<div class="gettingstarted__content">
<div class="gettingstarted__content__left">
<p>Being an emerging field, MLOps is rapidly gaining momentum amongst Data Scientists, ML Engineers and
AI enthusiasts. Following this trend, the <a href="https://github.com/cdfoundation/sig-mlops">Continuous
Delivery Foundation SIG MLOps</a> differentiates the ML models management from traditional
software engineering and suggests the following MLOps capabilities:</p>
</div>
<div class="gettingstarted__content__right">
<ul>
<li>MLOps aims to unify the release cycle for machine learning and software application release.
</li>
<li>MLOps enables automated testing of machine learning artifacts (e.g. data validation, ML model
testing, and ML model integration testing)
</li>
<li>MLOps enables the application of agile principles to machine learning projects.</li>
<li>MLOps enables supporting machine learning models and datasets to build these models as
first-class citizens within CI/CD systems.
</li>
<li>MLOps reduces technical debt across machine learning models.</li>
<li>MLOps must be a language-, framework-, platform-, and infrastructure-agnostic practice.</li>
</ul>
</div>
</div>
</div>
</section>
<section class="teasersection">
<div class="container">
<div class="teasersection__teasergrid">
<div class="teasersection__teasergrid__teaser">
<div class="teasersection__teasergrid__teaser__content">
<img class="teaser__icon" src="/assets/icons/Icon__Motivation.svg" alt="">
<h3>Motivation for MLOps</h3>
<p>You will learn for what to use Machine Learning, about various scenarios of change that need to
be managed and the iterative nature of ML-based software development. Finally, we provide the
MLOps definition and show the evolution of MLOps.</p>
</div>
<p><a class="button" href="/content/motivation">Read more</a></p>
</div>
<div class="teasersection__teasergrid__teaser">
<div class="teasersection__teasergrid__teaser__content">
<img class="teaser__icon" src="/assets/icons/Icon__DesigningSoftware.svg" alt="">
<h3>Designing ML-powered Software</h3>
<p>This part is devoted to one of the most important phase in any software project — understanding
the business problem and requirements. As these equally apply to ML-based software you need to
make sure to have a good understanding before setting out designing things.</p>
</div>
<p><a class="button" href="/content/phase-zero">Read more</a></p>
</div>
<div class="teasersection__teasergrid__teaser">
<div class="teasersection__teasergrid__teaser__content">
<img class="teaser__icon" src="/assets/icons/Icon__Lifecycle.svg" alt="">
<h3>End-to-End ML Workflow Lifecycle</h3>
<p>In this section, we provide a high-level overview of a typical workflow for machine
learning-based software development.</p>
</div>
<p><a class="button" href="/content/end-to-end-ml-workflow">Read more</a></p>
</div>
<div class="teasersection__teasergrid__teaser">
<div class="teasersection__teasergrid__teaser__content">
<img class="teaser__icon" src="/assets/icons/Icon__ThreeLevels.svg" alt="">
<h3>Three Levels of ML-based Software</h3>
<p>You will learn about three core elements of ML-based software — Data, ML models, and Code. In
particular, we will talk about</p>
<ul>
<li>Data Engineering Pipelines</li>
<li>ML Pipelines and ML workflows.</li>
<li>Model Serving Patterns and Deployment Strategies</li>
</ul>
</div>
<p><a class="button" href="/content/three-levels-of-ml-software">Read more</a></p>
</div>
<div class="teasersection__teasergrid__teaser">
<div class="teasersection__teasergrid__teaser__content">
<img class="teaser__icon" src="/assets/icons/Icon__Principles.svg" alt="">
<h3>MLOps Principles</h3>
<p>In this part, we describe principles and established practices to test, deploy, manage, and
monitor ML models in production.</p>
</div>
<p><a class="button" href="/content/mlops-principles">Read more</a></p>
</div>
<div class="teasersection__teasergrid__teaser">
<div class="teasersection__teasergrid__teaser__content">
<img class="teaser__icon" src="/assets/icons/Icon__Crisp.svg" alt="">
<h3>CRISP-ML(Q)</h3>
<p>You will learn about the standard process model for machine learning development.</p>
</div>
<p><a class="button" href="/content/crisp-ml">Read more</a></p>
</div>
<div class="teasersection__teasergrid__teaser">
<div class="teasersection__teasergrid__teaser__content">
<img class="teaser__icon" src="/assets/icons/Icon__StackCanvas.svg" alt="">
<h3>MLOps Stack Canvas</h3>
<p>In this part, you will learn how to specify an architecture and infrastructure stack for MLOps by
applying a general MLOps Stack Canvas framework, which is designed to be application- and
industry-neutral.</p>
</div>
<p><a class="button" href="/content/mlops-stack-canvas">Read more</a></p>
</div>
<div class="teasersection__teasergrid__teaser">
<div class="teasersection__teasergrid__teaser__content">
<img class="teaser__icon" src="/assets/icons/Icon__ModelGovernance.svg" alt="">
<h3>ML Model Governance</h3>
<p>This part presents an overview of governance processes, which are an integral part of MLOps.</p>
</div>
<p><a class="button" href="/content/model-governance">Read more</a></p>
</div>
<div class="teasersection__teasergrid__teaser">
<div class="teasersection__teasergrid__teaser__content">
<img class="teaser__icon" src="/assets/icons/Icon__Help.svg" alt="">
<h3>Need Help?</h3>
<p>MLOps consulting services by INNOQ</p>
</div>
<p><a class="button" href="https://data-ai.innoq.com/en">Read more</a></p>
</div>
</div>
</div>
</section>