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E-book: AWS Certified Machine Learning Study Guide: Specialty (MLS-C01) Exam

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  • Format: PDF+DRM
  • Pub. Date: 16-Nov-2021
  • Publisher: Sybex Inc.,U.S.
  • Language: eng
  • ISBN-13: 9781119821021
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AWS Certified Machine Learning Study Guide: Specialty (MLS-C01) ExamLarger Image
  • Format: PDF+DRM
  • Pub. Date: 16-Nov-2021
  • Publisher: Sybex Inc.,U.S.
  • Language: eng
  • ISBN-13: 9781119821021
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Succeed on the AWS Machine Learning exam or in your next job as a machine learning specialist on the AWS Cloud platform with this hands-on guide 

As the most popular cloud service in the world today, Amazon Web Services offers a wide range of opportunities for those interested in the development and deployment of artificial intelligence and machine learning business solutions. 

The AWS Certified Machine Learning Study Guide: Specialty (MLS-CO1) Exam delivers hyper-focused, authoritative instruction for anyone considering the pursuit of the prestigious Amazon Web Services Machine Learning certification or a new career as a machine learning specialist working within the AWS architecture. 

From exam to interview to your first day on the job, this study guide provides the domain-by-domain specific knowledge you need to build, train, tune, and deploy machine learning models with the AWS Cloud. And with the practice exams and assessments, electronic flashcards, and supplementary online resources that accompany this Study Guide, you’ll be prepared for success in every subject area covered by the exam. 

You’ll also find: 

  • An intuitive and organized layout perfect for anyone taking the exam for the first time or seasoned professionals seeking a refresher on machine learning on the AWS Cloud 
  • Authoritative instruction on a widely recognized certification that unlocks countless career opportunities in machine learning and data science 
  • Access to the Sybex online learning resources and test bank, with chapter review questions, a full-length practice exam, hundreds of electronic flashcards, and a glossary of key terms 

AWS Certified Machine Learning Study Guide: Specialty (MLS-CO1) Exam is an indispensable guide for anyone seeking to prepare themselves for success on the AWS Certified Machine Learning Specialty exam or for a job interview in the field of machine learning, or who wishes to improve their skills in the field as they pursue a career in AWS machine learning. 

Introduction xvii
Assessment Test xxix
Answers to Assessment Test xxxv
Part I Introduction
1(66)
Chapter 1 AWS Al ML Stack
3(46)
Amazon Rekognition
4(6)
Image and Video Operations
6(4)
Amazon Textract
10(3)
Sync and Async APIs
11(2)
Amazon Transcribe
13(2)
Transcribe Features
13(1)
Transcribe Medical
14(1)
Amazon Translate
15(2)
Amazon Translate Features
16(1)
Amazon Polly
17(2)
Amazon Lex
19(2)
Lex Concepts
19(2)
Amazon Kendra
21(2)
How Kendra Works
22(1)
Amazon Personalize
23(4)
Amazon Forecast
27(5)
Forecasting Metrics
30(2)
Amazon Comprehend
32(1)
Amazon CodeGuru
33(1)
Amazon Augmented AI
34(1)
Amazon SageMaker
35(7)
Analyzing and Preprocessing Data
36(3)
Training
39(1)
Model Inference
40(2)
AWS Machine Learning Devices
42(1)
Summary
43(1)
Exam Essentials
43(1)
Review Questions
44(5)
Chapter 2 Supporting Services from the AWS Stack
49(18)
Storage
50(4)
Amazon S3
50(2)
Amazon EFS
52(1)
Amazon FSx for Lustre
52(1)
Data Versioning
53(1)
Amazon VPC
54(2)
AWS Lambda
56(3)
AWS Step Functions
59(1)
AWS RoboMaker
60(2)
Summary
62(1)
Exam Essentials
62(1)
Review Questions
63(4)
Part II Phases of Machine Learning Workloads
67(138)
Chapter 3 Business Understanding
69(8)
Phases of ML Workloads
70(1)
Business Problem Identification
71(1)
Summary
72(1)
Exam Essentials
73(1)
Review Questions
74(3)
Chapter 4 Framing a Machine Learning Problem
77(8)
ML Problem Framing
78(2)
Recommended Practices
80(1)
Summary
81(1)
Exam Essentials
81(1)
Review Questions
82(3)
Chapter 5 Data Collection
85(16)
Basic Data Concepts
86(2)
Data Repositories
88(1)
Data Migration to AWS
89(7)
Batch Data Collection
89(3)
Streaming Data Collection
92(4)
Summary
96(1)
Exam Essentials
96(2)
Review Questions
98(3)
Chapter 6 Data Preparation
101(12)
Data Preparation Tools
102(5)
SageMaker Ground Truth
102(2)
Amazon EMR
104(1)
Amazon SageMaker Processing
105(1)
AWS Glue
105(2)
Amazon Athena
107(1)
Redshift Spectrum
107(1)
Summary
107(1)
Exam Essentials
107(2)
Review Questions
109(4)
Chapter 7 Feature Engineering
113(14)
Feature Engineering Concepts
114(6)
Feature Engineering for Tabular Data
114(5)
Feature Engineering for Unstructured and Time Series Data
119(1)
Feature Engineering Tools on AWS
120(1)
Summary
121(1)
Exam Essentials
121(2)
Review Questions
123(4)
Chapter 8 Model Training
127(40)
Common ML Algorithms
128(19)
Supervised Machine Learning
129(9)
Textual Data
138(3)
Image Analysis
141(1)
Unsupervised Machine Learning
142(4)
Reinforcement Learning
146(1)
Local Training and Testing
147(2)
Remote Training
149(1)
Distributed Training
150(4)
Monitoring Training Jobs
154(4)
Amazon Cloud Watch
155(1)
AWS CloudTrail
155(3)
Amazon EventBridge
158(1)
Debugging Training Jobs
158(1)
Hyperparameter Optimization
159(3)
Summary
162(1)
Exam Essentials
162(2)
Review Questions
164(3)
Chapter 9 Model Evaluation
167(14)
Experiment Management
168(1)
Metrics and Visualization
169(5)
Metrics in AWS AI/ML Services
173(1)
Summary
174(1)
Exam Essentials
175(1)
Review Questions
176(5)
Chapter 10 Model Deployment and Inference
181(14)
Deployment for AI Services
182(2)
Deployment for Amazon SageMaker
184(3)
SageMaker Hosting: Under the Hood
184(3)
Advanced Deployment Topics
187(4)
Autoscaling Endpoints
187(1)
Deployment Strategies
188(2)
Testing Strategies
190(1)
Summary
191(1)
Exam Essentials
191(1)
Review Questions
192(3)
Chapter 11 Application Integration
195(10)
Integration with On-Premises Systems
196(2)
Integration with Cloud Systems
198(2)
Integration with Front-End Systems
200(1)
Summary
200(1)
Exam Essentials
201(1)
Review Questions
202(3)
Part III Machine Learning We 11-Architected Lens
205(82)
Chapter 12 Operational Excellence Pillar for ML
207(14)
Operational Excellence on AWS
208(7)
Everything as Code
209(1)
Continuous Integration and Continuous Delivery
210(3)
Continuous Monitoring
213(1)
Continuous Improvement
214(1)
Summary
215(1)
Exam Essentials
215(2)
Review Questions
217(4)
Chapter 13 Security Pillar
221(20)
Security and AWS
222(6)
Data Protection
223(1)
Isolation of Compute
224(1)
Fine-Grained Access Controls
225(1)
Audit and Logging
226(1)
Compliance Scope
227(1)
Secure SageMaker Environments
228(7)
Authentication and Authorization
228(3)
Data Protection
231(1)
Network Isolation
232(1)
Logging and Monitoring
233(2)
Compliance Scope
235(1)
AI Services Security
235(1)
Summary
236(1)
Exam Essentials
236(2)
Review Questions
238(3)
Chapter 14 Reliability Pillar
241(10)
Reliability on AWS
242(1)
Change Management for ML
242(3)
Failure Management for ML
245(1)
Summary
246(1)
Exam Essentials
246(1)
Review Questions
247(4)
Chapter 15 Performance Efficiency Pillar for ML
251(10)
Performance Efficiency for ML on AWS
252(5)
Selection
253(1)
Review
254(1)
Monitoring
255(1)
Trade-offs
256(1)
Summary
257(1)
Exam Essentials
257(1)
Review Questions
258(3)
Chapter 16 Cost Optimization Pillar for ML
261(10)
Common Design Principles
262(1)
Cost Optimization for ML Workloads
263(3)
Design Principles
263(1)
Common Cost Optimization Strategies
264(2)
Summary
266(1)
Exam Essentials
266(1)
Review Questions
267(4)
Chapter 17 Recent Updates in the AWS AI/ML Stack
271(16)
New Services and Features Related to AI Services
272(7)
New Services
272(3)
New Features of Existing Services
275(4)
New Features Related to Amazon SageMaker
279(6)
Amazon SageMaker Studio
279(1)
Amazon SageMaker Data Wrangler
279(1)
Amazon SageMaker Feature Store
280(1)
Amazon SageMaker Clarify
281(1)
Amazon SageMaker Autopilot
282(1)
Amazon SageMaker JumpStart
283(1)
Amazon SageMaker Debugger
283(1)
Amazon SageMaker Distributed Training Libraries
284(1)
Amazon SageMaker Pipelines and Projects
284(1)
Amazon SageMaker Model Monitor
284(1)
Amazon SageMaker Edge Manager
285(1)
Amazon SageMaker Asynchronous Inference
285(1)
Summary
285(1)
Exam Essentials
285(2)
Appendix Answers to the Review Questions
287(16)
Chapter 1 AWS AI ML Stack
288(1)
Chapter 2 Supporting Services from the AWS Stack
289(1)
Chapter 3 Business Understanding
290(1)
Chapter 4 Framing a Machine Learning Problem
291(1)
Chapter 5 Data Collection
291(1)
Chapter 6 Data Preparation
292(1)
Chapter 7 Feature Engineering
293(1)
Chapter 8 Model Training
294(1)
Chapter 9 Model Evaluation
295(1)
Chapter 10 Model Deployment and Inference
295(1)
Chapter 11 Application Integration
296(1)
Chapter 12 Operational Excellence Pillar for ML
297(1)
Chapter 13 Security Pillar
298(1)
Chapter 14 Reliability Pillar
298(1)
Chapter 15 Performance Efficiency Pillar for ML
299(1)
Chapter 16 Cost Optimization Pillar for ML
300(3)
Index 303
ABOUT THE AUTHORs

Shreyas Subramanian, PhD, is Principal Machine Learning specialist at Amazon Web Services. He has worked with several enterprise companies on business-critical machine learning and optimization problems.

Stefan Natu is Principal Machine Learning Specialist at Alexa AI, prior to which he was a Principal Architect at Amazon Web Services. His professional focus is on financial services, and he helps customers architect ML use cases on AWS with an emphasis on security, enterprise model governance, and operationalizing machine learning models.
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