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E-raamat: Machine Learning for Oracle Database Professionals: Deploying Model-Driven Applications and Automation Pipelines

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  • Formaat: EPUB+DRM
  • Ilmumisaeg: 11-Jun-2021
  • Kirjastus: APress
  • Keel: eng
  • ISBN-13: 9781484270325
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Machine Learning for Oracle Database Professionals: Deploying Model-Driven Applications and Automation PipelinesSuurem pilt
  • Formaat: EPUB+DRM
  • Ilmumisaeg: 11-Jun-2021
  • Kirjastus: APress
  • Keel: eng
  • ISBN-13: 9781484270325
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Intermediate-Advanced user level

Database developers and administrators will use this book to learn how to deploy machine learning models in Oracle Database and in Oracle’s Autonomous Database cloud offering. The book covers the technologies that make up the Oracle Machine Learning (OML) platform, including OML4SQL, OML Notebooks, OML4R, and OML4Py. The book focuses on Oracle Machine Learning as part of the Oracle Autonomous Database collaborative environment. Also covered are advanced topics such as delivery and automation pipelines.

Throughout the book you will find practical details and hand-on examples showing you how to implement machine learning and automate deployment of machine learning. Discussion around the examples helps you gain a conceptual understanding of machine learning. Important concepts discussed include the methods involved, the algorithms to choose from, and mechanisms for process and deployment. Seasoned database professionals looking to make the leap into machine learning as a growth path will find much to like in this book as it helps you step up and use your current knowledge of Oracle Database to transition into providing machine learning solutions.


What You Will Learn

  • Use the Oracle Machine Learning (OML) Notebooks for data visualization and machine learning model building and evaluation
  • Understand Oracle offerings for machine learning
  • Develop machine learning with Oracle database using the built-in machine learning packages
  • Develop and deploy machine learning models using OML4SQL and OML4R
  • Leverage the Oracle Autonomous Database and its collaborative environment for Oracle Machine Learning
  • Develop and deploy machine learning projects in Oracle Autonomous Database
  • Build an automated pipeline that can detect and handle changes in data/model performance


Who This Book Is For

Database developers and administrators who want to learn about machine learning, developers who want to build models and applications using Oracle Database’s built-in machine learning feature set, and administrators tasked with supporting applications on Oracle Database that make use of the Oracle Machine Learning feature set
About the Authors ix
About the Technical Reviewer xi
Acknowledgments xiii
Introduction xv
Chapter 1 Introduction To Machine Learning
1(22)
Why Machine Learning?
1(1)
What Is Machine Learning?
2(14)
Supervised Learning
3(6)
Unsupervised Learning
9(4)
Semi-Supervised Learning
13(3)
The Machine Learning Process
16(6)
Summary
22(1)
Chapter 2 Oracle And Machine Learning
23(16)
Oracle Machine Learning for SQL (0ML4SQL)
23(1)
Oracle and Other Programming Languages for Machine Learning
24(3)
R
24(1)
Python
25(2)
Java
27(1)
OCI Data Science
27(5)
Oracle Analytics Cloud
32(1)
AutoML
32(5)
Summary
37(2)
Chapter 3 Oracle Machine Learning For Sql
39(58)
PL/SQL Packages for 0ML4SQL
40(3)
Privileges
40(1)
Data Dictionary Views
41(2)
Predictive Analytics
43(5)
Data Preparation and Transformations
48(15)
Understanding the Data
48(3)
Preparing the Data
51(12)
PL/SQL API for 0ML4SQL
63(26)
The Settings Table
64(2)
Model Management
66(3)
Model Evaluation
69(9)
Model Scoring and Deployment
78(8)
Partitioned Model
86(3)
Extensions to 0ML4SQL
89(6)
Oracle Data Miner and Oracle SQL Developer
89(6)
OML Notebooks
95(1)
Summary
95(2)
Chapter 4 Oracle Autonomous Database For Machine Learning
97(38)
Oracle Cloud Infrastructure and Autonomous Database
98(4)
Oracle Cloud Infrastructure Services
99(1)
Sign-up and Access Oracle Cloud Infrastructure
99(3)
Oracle Autonomous Database Architecture and Components
102(3)
Oracle Autonomous Database Attributes
104(1)
Autonomous Database in Free Trier and Always Free
105(1)
Working with Oracle Autonomous Data Warehouse
105(23)
Provisioning Oracle Autonomous Data Warehouse
106(3)
Connect to Oracle Autonomous Data Warehouse
109(6)
Loading Data to Oracle Autonomous Data Warehouse
115(8)
Import Tables/Schema to Oracle Autonomous Database
123(5)
Oracle Machine Learning with ADW
128(5)
Accessing Oracle Machine Learning Through Oracle Autonomous Database
129(4)
Summary
133(2)
Chapter 5 Running Oracle Machine Learning With Autonomous Database
135(20)
Oracle Machine Learning Collaborative Environment
136(11)
Starting with Oracle Machine Learning
136(4)
Sharing Workspaces with Other Users
140(2)
Creating a Machine Learning Notebook
142(1)
Specifying Interpreter Bindings and Connection Groups
143(4)
Running SQL Scripts and Statements
147(3)
Create and Execute SQL Scripts in a Notebook
147(1)
Run SQL Statements in a Notebook
148(2)
Work with Notebooks to Analyze and Visualize Data
150(4)
Summary
154(1)
Chapter 6 Building Machine Learning Models With Oml Notebooks
155(32)
Oracle Machine Learning Overview
156(5)
Supervised Learning and Unsupervised Learning
157(3)
Machine Learning Process Flow
160(1)
Oracle Machine Learning for SQL
161(12)
0ML4SQL PL/SQL API and SQL Functions
161(1)
Data Preparation and Data Transformation
162(3)
Model Creation
165(2)
Model Evaluation
167(4)
Model Scoring and Model Deployment
171(2)
An Example of Machine Learning Project
173(1)
Classification Prediction Example
174(12)
Data Preparation and Data Transformation
175(3)
Predicting Attribute Importance
178(1)
Model Creation
179(2)
Model Testing and Evaluation
181(2)
Model Application
183(3)
Summary
186(1)
Chapter 7 Oracle Analytics Cloud
187(18)
Data Preparation
189(5)
Data Visualization and Narrate
194(6)
Machine Learning in Oracle Analytics Cloud
200(3)
Summary
203(2)
Chapter 8 Delivery And Automation Pipeline In Machine Learning
205(24)
ML Development Challenges
206(1)
Classical Software Engineering vs. Machine Learning
206(1)
Model Drift
206(1)
ML Deployment Challenges
207(3)
ML Life Cycle
208(1)
Scaling Challenges
209(1)
Key Requirements
210(1)
Design Considerations and Solutions
210(11)
Automating Data Science Steps
211(1)
Automated ML Pipeline: MLOps
212(2)
Model Registry for Tracking
214(3)
Data Validation
217(1)
Pipeline Abstraction
218(1)
Automatic Machine Learning (AutoML)
219(1)
Model Monitoring
219(1)
Model Monitoring Implementation
220(1)
Scaling Solutions
221(3)
ML Accelerators for Large Scale Model Training and Inference
221(1)
Distributed Machine Learning for Model Training
221(1)
Model Inference Options
222(1)
Input Data Pipeline
222(2)
ML Tooling Ecosystem
224(3)
ML Platforms
225(1)
ML Development Tools
226(1)
ML Deployment Tools
227(1)
Summary
227(2)
Chapter 9 Ml Deployment Pipeline Using Oracle Machine Learning
229(20)
Mainstream ML Platforms
229(3)
Oracle Machine Learning Environment
232(1)
Data Extraction in Big Data Environment
232(1)
In-Cluster Parallel Data Processing
233(1)
Automated Data Preparation and Feature Engineering
234(2)
General Data Processing Automation
234(1)
Text Processing Automation
235(1)
AutoML
235(1)
Scalable In-Database Model Training and Scoring
236(5)
In-Database Parallel Execution via Embedded Algorithms
236(4)
In-Cluster Parallel Execution
240(1)
Model Management
241(1)
Saving Models Using R Datastores in Database
241(3)
Leveraging Open Source Packages
244(4)
TensorFlow Extended (TFX) for Data Validation
244(1)
scikit-multiflow for Model Monitoring
245(1)
Kubeflow: Cloud-Native ML Pipeline Deployment
245(3)
Summary
248(1)
Chapter 10 Building Reproducible Ml Pipelines Using Oracle Machine Learning
249(34)
The Environment
250(10)
Setting up Oracle Machine Learning for R
251(1)
Verifying the Oracle Machine Learning for R Installation
252(2)
Setting up Open Source Components
254(6)
The Data
260(1)
Data Validation and Model Monitoring Implementation
261(3)
TensorFlow Data Validation (TFDV)
261(1)
Data Validation
262(2)
Model Monitoring
264(1)
Tracking and Reproducing ML Pipeline
264(14)
Data Version Control (DVC)
265(1)
Versioning Code, Data, and Model Files
265(1)
Demo with Actual ML Pipeline
266(12)
0ML4R Troubleshooting Tips
278(4)
Error When Connecting to Oracle Database (as oml user)
278(1)
Error Due to Missing Packages When Building Models
279(2)
Error When Creating or Dropping R Scripts for Embedded R Execution
281(1)
Summary
282(1)
Index 283
Heli Helskyaho is CEO for Miracle Finland Oy. She holds a masters degree in computer science from the University of Helsinki and specializes in databases. At the moment she is working on her doctoral studies, researching and teaching at the University of Helsinki. Her research areas cover big data, multi-model databases, schema discovery, and methods and tools for utilizing semi-structured data for decision making.  Heli has been working in IT since 1990. She has held several positions, but every role has included databases and database designing. She believes that understanding your data makes using the data much easier. She is an Oracle ACE Director, an Oracle Groundbreaker Ambassador, and a frequent speaker at many conferences. She is the author of several books and has been listed as one of the TOP 100 influencers in the IT sector in Finland for each year from 2015 to 2020. Jean Yu is a Senior Staff MLOps Engineer at Habana Labs, an Intel company. Prior to that, she was a Senior Data Engineer on the IBM Hybrid Cloud Management Data Science Team. Her current interests include deep learning, model productization, and distributed training of massive transformer-based language models. She holds a master's degree in computer science from the University of Texas at San Antonio. She has more than 25 years of experience in developing, deploying, and managing software applications, as well as in leading development teams. Her recent awards include an Outstanding Technical Achievement Award for significant innovation in Cloud Brokerage Cost and Asset Management products in 2019 as well as an Outstanding Technical Achievement Award for Innovation in the Delivery of Remote Maintenance Upgrade for Tivoli Storage Manager in 2011. Jean is a master inventor with 14 patents granted. She has been a voting member of the IBM Invention Review Board from 2006 to 2020. She has been a speaker at conferences such as North Central Oracle Apps User Group Training Day 2019 and Collaborate 2020. Kai Yu is a Distinguished Engineer of the Dell Technical Leadership Community. He is responsible for providing technical leadership to Dell Oracle Solutions Engineering.  He has over 27 years of experience in architecting and implementing various IT solutions, specializing in Oracle database, IT infrastructure, and cloud as well as business analytics and machine learning. Kai has been a frequent speaker at various IT/Oracle conferences with over 200 presentations in more than 20 countries. He also authored 36 articles in technical journals such as IEEE Transactions on Big Data, and has co-authored the Apress book Expert Oracle RAC12c. He has been an Oracle ACE Director since 2010, and has served on the IOUG/Quest Conference committee and served as IOUG RAC SIG president and IOUG CLOUG SIG co-founder and vice president. He received the 2011 OAUG Innovator of Year award and the 2012 Oracle Excellence Award: Technologist of the Year: Cloud Architect by Oracle Magazine. He holds two masters degrees in computer science and engineering from the Huazhong University of Science and Technology and the University of Wyoming. 







 
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