Index
A
- ab (Apache Benchmark), Benchmarking HTTP with Apache Benchmark (ab)-Benchmarking HTTP with Apache Benchmark (ab)
- ACM (AWS Certificate Manager), Provisioning an SSL Certificate with AWS ACM, Provisioning an ACM SSL Certificate
- adhoc predict, adhoc_predict, adhoc_predict from Pickle
- Algolia index, creating/updating, Creating an Algolia Index and Updating It
- Amazon EC2, Infrastructure as Code
- Amazon Identity and Access Management (IAM) service, Reading and Writing Files
- Amazon Resource Name (ARN), Provisioning an Amazon CloudFront Distribution
- Amazon Web Services (see AWS)
- anonymous functions, Anonymous Functions
- Ansible, Reading and Writing Files
- antipatterns, DevOps, DevOps Antipatterns-Principled leadership
- lack of automated build server, No Automated Build Server Antipattern
- lack of clear, elevating goal, A clear, elevating goal
- lack of collaborative climate, A collaborative climate
- lack of competent team members, Competent team members
- lack of coordination, Difficulties in Coordination as an Ongoing Accomplishment
- lack of external support and recognition, External support and recognition
- lack of logging, Flying Blind
- lack of principled leadership, Principled leadership
- lack of results-driven structure, A results-driven structure
- lack of standards of excellence, Standards of excellence
- lack of teamwork, No Teamwork
- lack of unified commitment, Unified commitment
- Apache Benchmark (ab), Benchmarking HTTP with Apache Benchmark (ab)-Benchmarking HTTP with Apache Benchmark (ab)
- Apache HTTP server, Using Regular Expressions to Search Text
- API Gateway methods, Provisioning DynamoDB Table, Lambda Functions, and API Gateway Methods Using the AWS CDK-Provisioning DynamoDB Table, Lambda Functions, and API Gateway Methods Using the AWS CDK
- argparse, Using argparse-Using argparse
- ARN (Amazon Resource Name), Provisioning an Amazon CloudFront Distribution
- assert statement, The Amazing assert
- asymmetric key encryption, Encryption with Cryptography
- automating files and the filesystem, Automating Files and the Filesystem-Paths as Objects with Pathlib
- automation tools, IaC with, A Classification of Infrastructure Automation Tools
- AutoML, Level 5: Continuous Delivery of Traditional ML and AutoML
- AWS (Amazon Web Services)
- about, Infrastructure as Code
- ACM, Provisioning an SSL Certificate with AWS ACM
- CDK, Provisioning DynamoDB Table, Lambda Functions, and API Gateway Methods Using the AWS CDK-Provisioning DynamoDB Table, Lambda Functions, and API Gateway Methods Using the AWS CDK
- CloudFront distribution, Provisioning an Amazon CloudFront Distribution-Provisioning an Amazon CloudFront Distribution
- CloudWatch logging, Using Amazon CloudWatch Logging with AWS Lambda
- Code Pipeline, Deploying with AWS CodePipeline
- creating new Pulumi project for, Creating a New Pulumi Python Project for AWS-Creating a New Pulumi Python Project for AWS
- Sagemaker, Cloud Machine learning Platforms
- AWS Certificate Manager (ACM), Provisioning an SSL Certificate with AWS ACM, Provisioning an ACM SSL Certificate
- AWS Lambda, Deploying Python Function to AWS Lambda-Deploying Python Function to AWS Lambda
- AWS SQS (Simple Queuing Service)
- Azure, Deploying Python Function to Azure-Deploying Python Function to Azure
B
- basename() method, Managing Files and Directories Using os.path
- Bash (see shells, Bash/ZSH)
- basicconfig, The basicconfig
- benchmarking, Benchmarking HTTP with Apache Benchmark (ab)-Benchmarking HTTP with Apache Benchmark (ab)
- big data, Big Data-Data Storage
- data storage, Data Storage
- defining, Big Data-Big Data
- file systems, Filesystems
- sources, Data Sources
- storage, Data Storage
- tools, components, and platforms, Big Data Tools, Components, and Platforms-Data Storage
- big endian, Talking to the Interpreter with the sys Module
- binaries
- blkid, Retrieving Specific Device Information
- brew, Automated Infrastructure Provisioning with Terraform
C
- capstone project, Capstone Project
- capsys, Built-in Fixtures
- CD (see continuous integration/continuous deployment (CI/CD))
- centralized logging, Centralized Logging
- changelog
- character classes, in regex searches, Character Classes
- character sets, in regex searches, Character Sets
- chown tool, strace
- CI (see continuous integration)
- CI/CD (see continuous integration/continuous deployment)
- circus, Management with systemd
- class definition, Using click
- click, Using click-Using click
- click.command, Using click
- click.group, Using click
- click.option, Using click
- cloud computing, Cloud Computing-Case Study Questions
- cloud services, Types of Cloud Services-Software as a Service
- containers, Containers
- (see also Docker; Kubernetes)
- continuous delivery, Continuous Delivery
- distributed computing challenges and opportunities, Challenges and Opportunities in Distributed Computing
- FaaS and serverless, Function as a Service and Serverless
- forking process with Pool(), Forking Processes with Pool()-Forking Processes with Pool()
- foundations, Cloud Computing Foundations-Cloud Computing Foundations
- hardware virtualization, Hardware Virtualization
- high-performance servers, Using High-Performance Servers
- Iaas, Infrastructure as a Service-Infrastructure as a Service
- IaC, Infrastructure as Code
- MaaS, Metal as a Service
- machine learning platforms, Cloud Machine learning Platforms
- managing processes with subprocess, Manage Processes with Subprocess-The problem with Python threads
- multiprocessing, Using Multiprocessing to Solve Problems
- Numba, High Performance Python with Numba
- Numba JIT, Using Numba Just in Time Compiler
- PaaS, Platform as a Service
- process management, Process Management-Using High-Performance Servers
- Python concurrency/performance/process management in the cloud era, Python Concurrency, Performance, and Process Management in the Cloud Era
- SaaS, Software as a Service
- SDNs, Software Defined Networks
- SDS, Software Defined Storage
- serverless computing, Serverless Computing-Serverless Computing
- types of cloud computing, Types of Cloud Computing
- virtualization, Virtualization and Containers-Containers
- Cloud9, Serverless Computing
- CloudFront distribution, provisioning, Provisioning a CloudFront Distribution
- CloudWatch event trigger, Wiring Up CloudWatch Event Trigger
- CloudWatch events, Using AWS Lambda with CloudWatch Events
- command line, Working with the Command Line-Exercises
- command-line tools, Creating Command-Line Tools-Implementing Plug-ins
- case study: turbocharging Python with, Case Study: Turbocharging Python with Command-Line Tools-KMeans Clustering
- creating using argparse, Using argparse-Using argparse
- creating using click, Using click-Using click
- creating using fire, fire-fire
- creating using sys.argv, Using sys.argv-Using sys.argv
- implementing plugins, Implementing Plug-ins
- KMeans clustering, KMeans Clustering
- Numba JIT compiler, Using the Numba Just-in-Time (JIT) Compiler-Using the Numba Just-in-Time (JIT) Compiler
- running code on GPU with CUDA Python, Using the GPU with CUDA Python
- running true multicore multithreaded Python using Numba, Running True Multicore Multithreaded Python Using Numba
- compiling, regex, Compiling
- configuration values, staging stack with, Creating Configuration Values for the Staging Stack
- confirmation prompts, searching and replacing, Searching and Replacing with Confirmation Prompts
- conftest.py, conftest.py
- containers, Containers
- context, Kubernetes cluster and, Deploying Kubernetes Manifests to a Local Kubernetes Cluster Based on minikube
- continue statement, continue
- continuous delivery, Continuous Delivery
- continuous integration/continuous deployment (CI/CD), Continuous Integration and Continuous Deployment-Real-World Case Study: NFSOPS
- AWS Code Pipeline, Deploying with AWS CodePipeline
- case study: converting a poorly maintained WordPress site to Hugo, Real-World Case Study: Converting a Poorly Maintained WordPress Site to Hugo-Deploying with AWS CodePipeline
- case study: deploying a Python App Engine application with Google Cloud Build, Real-World Case Study: Deploying a Python App Engine Application with Google Cloud Build-Real-World Case Study: Deploying a Python App Engine Application with Google Cloud Build
- case study: NFSOPS, Real-World Case Study: NFSOPS
- cloud computing, Containers
- converting WordPress to Hugo posts, Converting WordPress to Hugo Posts
- creating/updating Algolia index, Creating an Algolia Index and Updating It
- deployment with AWS Code Pipeline, Deploying with AWS CodePipeline
- Hugo setup, Setting Up Hugo
- orchestrating with Makefile, Orchestrating with a Makefile
- control file, The control file
- CPU utilities, CPU Utilities-Viewing Processes with htop
- createrepo, RPM repositories
- cryptography library, Encryption with Cryptography-Encryption with Cryptography
- CSV, JSON converted from, Converting a CSV File to JSON
- CUDA Python, Using the GPU with CUDA Python
D
- data engineering, Data Engineering-Case Study Question
- big data, Big Data-Data Storage
- case study, Case Study: Building a Homegrown Data Pipeline
- creating event-driven lambdas, Creating Event-Driven Lambdas
- generator pipeline to read and process lines, Generator Pipeline to Read and Process Lines
- reading a file, Read a File
- reading AWS SQS events from AWS Lambda, Reading Amazon SQS Events from AWS Lambda-Conclusion
- real-time streaming ingestion, Real-Time Streaming Ingestion
- serverless, Serverless Data Engineering-Conclusion
- small data, Small Data
- using AWS CloudWatch logging with AWS Lambda, Using Amazon CloudWatch Logging with AWS Lambda
- using AWS Lambda to populate AWS SQS, Using AWS Lambda to Populate Amazon Simple Queue Service-Using AWS Lambda to Populate Amazon Simple Queue Service
- using AWS Lambda with CloudWatch events, Using AWS Lambda with CloudWatch Events
- wiring up CloudWatch event trigger, Wiring Up CloudWatch Event Trigger
- writing a file, Write a File
- YAML, Using YAML
- Data Lake, Data Storage
- Debian packaging, Debian Packaging-Debian repositories
- Debian repositories, Debian repositories
- debuild command-line tool, Producing the binary
- deep learning
- describe() method, Reading and Writing Files, Real-World Case Study: Deploying a Python App Engine Application with Google Cloud Build
- descriptive statistics, Descriptive Statistics
- descriptive versioning, Descriptive Versioning
- dicts, Dicts
- direnv, Reading and Writing Files
- dirname() method, Managing Files and Directories Using os.path, Managing Files and Directories Using os.path
- disk utilities, Disk Utilities-Retrieving Specific Device Information
- Docker, Container Technologies: Docker and Docker Compose-Exercises
- creating/building/running/removing images/containers, Creating, Building, Running, and Removing Docker Images and Containers-Creating, Building, Running, and Removing Docker Images and Containers
- defining, What Is a Docker Container?
- development, Infrastructure as Code
- porting docker-compose services to new host and operating system, Porting the docker-compose Services to a New Host and Operating System-Porting the docker-compose Services to a New Host and Operating System
- publishing images to Docker registry, Publishing Docker Images to a Docker Registry
- running container with same image on different host, Running a Docker Container with the Same Image on a Different Host-Running a Docker Container with the Same Image on a Different Host
- running multiple containers with Docker Compose, Running Multiple Docker Containers with Docker Compose-Running Multiple Docker Containers with Docker Compose
- sklearn flask, Sklearn Flask with Kubernetes and Docker-Scale Input
- sklearn-based machine learning model with (see sklearn-based machine learning model)
- docker build command, Creating, Building, Running, and Removing Docker Images and Containers
- Docker Compose
- docker compose logs command, Running Multiple Docker Containers with Docker Compose
- docker images command, Creating, Building, Running, and Removing Docker Images and Containers
- docker kill command, Creating, Building, Running, and Removing Docker Images and Containers
- docker ps, Running Multiple Docker Containers with Docker Compose
- Docker registry, publishing images to, Publishing Docker Images to a Docker Registry
- docker rmi command, Creating, Building, Running, and Removing Docker Images and Containers
- docker run command, Creating, Building, Running, and Removing Docker Images and Containers
- docker stop command, Creating, Building, Running, and Removing Docker Images and Containers
- docker-compose exec db, Running Multiple Docker Containers with Docker Compose
- docker-compose up -d db command, Running Multiple Docker Containers with Docker Compose
- Dockerfile format, Containers
- DynamoDB table, Provisioning DynamoDB Table, Lambda Functions, and API Gateway Methods Using the AWS CDK-Provisioning DynamoDB Table, Lambda Functions, and API Gateway Methods Using the AWS CDK
E
- EDA (Exploratory Data Analysis), EDA
- elasticsearch, The ELK Stack, Elasticsearch and Kibana-Elasticsearch and Kibana
- ELK (Elasticsearch, Logstash, Kibana) stack, The ELK Stack-Elasticsearch and Kibana
- encrypting text, Encrypting Text-Encryption with Cryptography
- EnvironFilter, Deeper Configuration
- event-driven lambda, Creating Event-Driven Lambdas
- exception handling, Handling Exceptions
- execution control, Execution Control-continue
- expanduser() function, Managing Files and Directories Using os.path
- expensive_operation(), Common Patterns
- Exploratory Data Analysis (see EDA)
F
- FaaS (see function as a service)
- faas-cli build command, Deploying Python Function to OpenFaaS
- faas-cli login command, Deploying Python Function to OpenFaaS
- faas-cli push command, Deploying Python Function to OpenFaaS
- fault tolerance, Fault Tolerance
- fdisk, Retrieving Specific Device Information
- feature, defined, Machine Learning Key Terminology
- files, automating (see automating files and the filesystem)
- filtering processes, Listing and Filtering Processes
- findall() command, Find All
- finditer() method, Find Iterator, Using Regular Expressions to Search Text
- find_packages() function, Package files
- fio, Accurate tests with fio
- fire, fire-fire
- fitting, of machine learning model, Fit the model, Fit Model
- Foord, Michael, Michael Foord-Michael Foord
- for loops, for Loops
- forking process with pool(), Forking Processes with Pool()-Forking Processes with Pool()
- function as a service (FaaS), Function as a Service and Serverless
- Function as a Service (FaaS), Serverless Technologies
- function decorators, Using click
- functions (generally), Functions-Anonymous Functions
G
- garbage collection, Dealing with Large Files
- GCP (Google Cloud Platform), launching GKE Kubernetes cluster in, Launching a GKE Kubernetes Cluster in GCP with Pulumi-Launching a GKE Kubernetes Cluster in GCP with Pulumi
- generate_latest(), Prometheus
- generator comprehensions, Generator Comprehensions
- generator pipeline, read and process lines with, Generator Pipeline to Read and Process Lines
- generators, Generators
- getLogger(), Deeper Configuration
- get_prefix(), Instrumentation
- Git
- GKE Kubernetes cluster
- Google Cloud Build, Real-World Case Study: Deploying a Python App Engine Application with Google Cloud Build-Real-World Case Study: Deploying a Python App Engine Application with Google Cloud Build
- Google Cloud Functions, Deploying Python Function to Google Cloud Functions-Deploying Python Function to Google Cloud Functions
- Google Cloud Platform (GCP), launching GKE Kubernetes cluster in, Launching a GKE Kubernetes Cluster in GCP with Pulumi-Launching a GKE Kubernetes Cluster in GCP with Pulumi
- Google Container Engine (GKE) (see GKE Kubernetes cluster)
- GPU (Graphics Processing Unit), Using the GPU with CUDA Python
- Grafana Helm charts, Installing Prometheus and Grafana Helm Charts-Installing Prometheus and Grafana Helm Charts
- Graphite, Graphite
- groups, defining in regex searches, Groups
H
- hardware virtualization, Hardware Virtualization
- Harrison, Matt, Matt Harrison-Matt Harrison
- hash functions, Hashing with Hashlib
- hashing, Hashing with Hashlib
- hashlib, Hashing with Hashlib
- head() method, Reading and Writing Files
- hello python function, Deploying Python Function to OpenFaaS
- helpers, Customizing the Python Shell
- helpers.uuid4(), Customizing the Python Shell
- Holzer, Teijo, Teijo Holzer
- host.addr, Features and Special Fixtures
- host.ansible, Features and Special Fixtures
- host.check_output, Features and Special Fixtures
- host.docker, Features and Special Fixtures
- host.interface, Features and Special Fixtures
- host.iptables, Features and Special Fixtures
- host.mount_point, Features and Special Fixtures
- host.package, Features and Special Fixtures
- host.process, Features and Special Fixtures
- host.run, Features and Special Fixtures
- host.run_expect, Features and Special Fixtures
- host.sudo, Features and Special Fixtures
- host.system_info, Features and Special Fixtures
- htop, Viewing Processes with htop
- HTTP
- Hugo
- hybrid cloud, Types of Cloud Computing
I
- Iaas (Infrastructure as a Service), Infrastructure as a Service-Infrastructure as a Service
- IaC (see Infrastructure as Code)
- IAM (Identity and Access Management) service, Reading and Writing Files
- IDEs (Integrated Development Environments), Getting Started with pytest
- if/elif/else statements, if/elif/else
- Infrastructure as a Service (Iaas), Infrastructure as a Service-Infrastructure as a Service
- Infrastructure as Code (IaC)
- infrastructure testing, Infrastructure Testing-Features and Special Fixtures
- init method, Using click
- init system (see systemd)
- __init__() method, Using click
- Integrated Development Environments (IDEs), Getting Started with pytest
- internal package index, Hosting an internal package index-Hosting an internal package index
- interpreter, sys module and, Talking to the Interpreter with the sys Module
- interviews, Glenn Solomon-Michael Foord
- Andrew Nguyen, Andrew Nguyen-Andrew Nguyen
- Gabriella Roman, Gabriella Roman
- Glenn Solomon, Glenn Solomon
- Jonathan LaCour, Jonathan LaCour
- Joseph Reis, Joseph Reis-Joseph Reis
- Matt Harrison, Matt Harrison-Matt Harrison
- Michael Foord, Michael Foord-Michael Foord
- Rigoberto Roche, Rigoberto Roche-Rigoberto Roche
- Teijo Holzer, Teijo Holzer
- Ville Tuulos, Ville Tuulos-Ville Tuulos
- iostat, Measuring Performance
- IPython, IPython
K
- keys() method, Dicts
- kibana, The ELK Stack, Elasticsearch and Kibana-Elasticsearch and Kibana
- KMeans clustering, KMeans Clustering
- kubectl create -f command, Deploying Kubernetes Manifests to a Local Kubernetes Cluster Based on minikube
- kubectl create command, Deploying Kubernetes Manifests to a Local Kubernetes Cluster Based on minikube, Deploying Kubernetes Manifests to a Local Kubernetes Cluster Based on minikube
- kubectl get pvc command, Deploying Kubernetes Manifests to a Local Kubernetes Cluster Based on minikube
- kubectl logs command, Deploying Kubernetes Manifests to a Local Kubernetes Cluster Based on minikube
- kubectl port-forward command, Installing Prometheus and Grafana Helm Charts
- Kubernetes, Container Orchestration: Kubernetes-Exercises
- Kubernetes Management Service, Containers
L
- LaCour, Jonathan, Jonathan LaCour
- Lambda functions, Provisioning DynamoDB Table, Lambda Functions, and API Gateway Methods Using the AWS CDK-Provisioning DynamoDB Table, Lambda Functions, and API Gateway Methods Using the AWS CDK
- large files, automating, Dealing with Large Files
- lazy evaluation, Lazy Evaluation
- linear regression model, print accuracy of, Print accuracy of linear regression model
- Linux utilities, Useful Linux Utilities-Case Study Question
- listing processes, Listing and Filtering Processes
- lists, Lists-Lists
- little endian, Talking to the Interpreter with the sys Module
- LLVM compiler, Using Numba Just in Time Compiler
- load testing, Load Testing with molotov-Load Testing with molotov
- log handling, Log Handling
- logging, Logging-Common Patterns
- Logstash, The ELK Stack, Logstash-Logstash
- lsblk, Retrieving Specific Device Information
- lsof, Viewing Processes with htop
M
- Maas (Metal as a Service), Metal as a Service
- machine learning
- cloud machine learning platforms, Cloud Machine learning Platforms
- deep learning with PyTorch, Deep Learning with PyTorch-Print RMSE
- defining, What Is Machine Learning?-Plot predicted height versus actual height, Machine Learning Key Terminology
- key terminology, Machine Learning Key Terminology
- maturity model, Machine learning Maturity Model-Level 6: ML Operational Feedback Loop
- modeling, Modeling-Plot predicted height versus actual height
- Python machine learning ecosystem, Python Machine learning Ecosystem-Print RMSE
- sklearn flask with Kubernetes and Docker, MLOps and Machine learning Engineering-Learning Assessments
- sklearn regression model, Sklearn Regression Model
- supervised machine learning, Supervised Machine Learning-Kernel Density Distribution
- major.minor, Descriptive Versioning
- major.minor.micro, Descriptive Versioning
- Makefile, orchestrating with, Orchestrating with a Makefile
- manual provisioning
- maturity model, for machine learning, Machine learning Maturity Model-Level 6: ML Operational Feedback Loop
- key terminology, Machine Learning Key Terminology
- level 1: framing, scope identification, and problem definition, Level 1: Framing, Scope Identification, and Problem Definition
- level 2: continuous delivery of data, Level 2: Continuous Delivery of Data
- level 3: continuous delivery of clean data, Level 3: Continuous Delivery of Clean Data-Level 3: Continuous Delivery of Clean Data
- level 4: continuous delivery of EDA, Level 4: Continuous Delivery of Exploratory Data Analysis
- level 5: continuous delivery of traditional ML and AutoML, Level 5: Continuous Delivery of Traditional ML and AutoML
- level 6: ML operational feedback loop, Level 6: ML Operational Feedback Loop
- Metal as a Service (Maas), Metal as a Service
- minikube, Deploying Kubernetes Manifests to a Local Kubernetes Cluster Based on minikube-Deploying Kubernetes Manifests to a Local Kubernetes Cluster Based on minikube
- MLOps (see machine learning)
- modeling, for machine learning, Modeling-Plot predicted height versus actual height
- molotov, load testing with, Load Testing with molotov-Load Testing with molotov
- monitoring and logging, Monitoring-Prometheus, Logging-Common Patterns
- basicconfig, The basicconfig
- build versus buy decision, Did You Build It or Buy It?
- case study, Case Study: Production Database Kills Hard Drives
- centralized logging, Centralized Logging
- challenges, Why Is It Hard?
- common patterns, Common Patterns
- deeper configuration, Deeper Configuration-Deeper Configuration
- ELK stack, The ELK Stack-Elasticsearch and Kibana
- fault tolerance, Fault Tolerance
- Graphite, Graphite
- immutable DevOps principles, Immutable DevOps Principles-Fault Tolerance
- instrumentation, Instrumentation-Naming Conventions
- key concepts in building reliable systems, Key Concepts in Building Reliable Systems
- namespaces, Naming Conventions
- Prometheus, Prometheus-Prometheus
- StatsD, StatsD
- monkeypatch, Built-in Fixtures
- multicloud, Types of Cloud Computing
- multiprocessing, Using Multiprocessing to Solve Problems
N
- named groups, in regex searches, Named Groups
- namespaces, Naming Conventions
- namespacing, Reading and Writing Files
- naming conventions, Naming Conventions
- native Python packaging, Native Python Packaging-Hosting an internal package index
- Netflix, Cloud Computing Foundations
- network utilities, Network Utilities-Load Testing with molotov
- new stack, creation and deployment of, Creating and Deploying a New Stack-Creating and Deploying a New Stack
- NFSOPS, Real-World Case Study: NFSOPS
- Nginx package, Examples
- Nguyen, Andrew, Andrew Nguyen-Andrew Nguyen
- Numba, Running True Multicore Multithreaded Python Using Numba, High Performance Python with Numba
- Numba Just-in-Time (JIT) Compiler, Using the Numba Just-in-Time (JIT) Compiler-Using the Numba Just-in-Time (JIT) Compiler, Using Numba Just in Time Compiler
- Numpy, Machine Learning Key Terminology
P
- PaaS (Platform as a Service), Platform as a Service
- package files
- package management, Package Management-Case Study Question
- about, Package Management
- changelog, The changelog
- Debian packaging, Debian Packaging-Debian repositories
- descriptive versioning, Descriptive Versioning
- guidelines, Packaging Guidelines-The changelog
- importance of, Why Is Packaging Important?
- log handling, Log Handling
- native Python packaging, Native Python Packaging-Hosting an internal package index
- RPM packaging, RPM Packaging-RPM repositories
- solutions, Packaging Solutions-RPM repositories
- strategies, Choosing a Strategy
- systemd for, Management with systemd-The systemd Unit File
- unit installation, Installing the Unit
- when packaging might not be needed, When Packaging Might Not Be Needed
- Pandas package, Reading and Writing Files
- parametrization, Parametrization
- parted, Partitions
- partitions, Partitions
- pathlib, Reading and Writing Files, Paths as Objects with Pathlib
- paths, as object with pathlib, Paths as Objects with Pathlib
- pecan command-line tool, Setting It Up
- Platform as a Service (PaaS), Platform as a Service
- plugins, Implementing Plug-ins
- pool(), Forking Processes with Pool()-Forking Processes with Pool()
- print accuracy of linear regression model, Print accuracy of linear regression model
- print(), Debuggers
- print() function, Print
- private cloud, Types of Cloud Computing
- process management, Process Management-Using High-Performance Servers
- avoiding shell=True, Avoid shell=True
- FaaS, Function as a Service and Serverless
- forking process with Pool(), Forking Processes with Pool()-Forking Processes with Pool()
- high-performance servers, Using High-Performance Servers
- multiprocessing, Using Multiprocessing to Solve Problems
- Numba, High Performance Python with Numba
- Numba JIT, Using Numba Just in Time Compiler
- Python thread problems, The problem with Python threads
- setting timeouts and handling when appropriate, Set timeouts and handle them when appropriate
- subprocess, Manage Processes with Subprocess-The problem with Python threads
- Prometheus, Prometheus-Prometheus, Installing Prometheus and Grafana Helm Charts-Installing Prometheus and Grafana Helm Charts
- provisioning, manual (see manual provisioning)
- ps command, CPU Utilities
- psql -U postgres command, Porting the docker-compose Services to a New Host and Operating System
- public cloud, Types of Cloud Computing
- public_read_policy_for_bucket, Creating a New Pulumi Python Project for AWS
- Pulumi, Infrastructure as Code-Creating and Deploying a New Stack
- pulumi destroy, Destroying the GKE Cluster
- pulumi login command, Automated Infrastructure Provisioning with Pulumi
- pulumi new command, Launching a GKE Kubernetes Cluster in GCP with Pulumi
- pulumi stack init, Creating and Deploying a New Stack
- pulumi up, Provisioning a Route 53 Zone and DNS Records, Provisioning a CloudFront Distribution
- pycache, Removing Temporary Python Files
- pyclean, Removing Temporary Python Files
- PyPI (Python Package Index), Package Management, The Python Package Index
- pytest, Pytest for DevOps-Case Study Question
- assert statement, The Amazing assert
- built-in fixtures, Built-in Fixtures-Built-in Fixtures
- conftest.py, conftest.py
- connecting to remote nodes, Connecting to Remote Nodes-Connecting to Remote Nodes
- examples, Examples-Examples
- features, pytest Features-Parametrization, Features and Special Fixtures
- fixtures, Fixtures-Built-in Fixtures, Features and Special Fixtures
- getting started, Getting Started with pytest-Differences with unittest
- infrastructure testing, Infrastructure Testing-Features and Special Fixtures
- layouts and conventions, Layouts and conventions
- parametrization, Parametrization
- system validation, What Is System Validation?
- Testinfra, Introduction to Testinfra
- testing Jupyter Notebooks, Testing Jupyter Notebooks with pytest
- testing superpowers with, Testing Superpowers with pytest
- testing with, Testing with pytest-Layouts and conventions
- unittest versus, Differences with unittest-Differences with unittest
- Python (basics), Python Essentials for DevOps-Exercises
- basic math, Basic Math
- built-in functions, Built-in Functions
- built-in objects, Built-in Objects-Dicts
- comments, Comments
- execution control, Execution Control-continue
- functions, Functions-Anonymous Functions
- handling exceptions, Handling Exceptions
- installing/running, Installing and Running Python
- IPython, IPython
- IPython advanced features, More IPython Features
- Jupyter Notebooks, Jupyter Notebooks
- lazy evaluation, Lazy Evaluation
- print function, Print
- procedural programming, Procedural Programming-Comments
- Python scripts, Python scripts
- Python shell, The Python Shell
- range function, Range
- regular expressions, Using Regular Expressions-Compiling
- variables, Variables
- while loops, while Loops
- Python App Engine, Real-World Case Study: Deploying a Python App Engine Application with Google Cloud Build-Real-World Case Study: Deploying a Python App Engine Application with Google Cloud Build
- Python one-liners, Python One-Liners-How Fast Is this Snippet?
- Python Package Index (PyPI), Package Management, The Python Package Index
- Python shell, The Python Shell
- PyTorch
R
- range() function, Range
- read and process lines, generator pipeline to, Generator Pipeline to Read and Process Lines
- read() function, Reading and Writing Files
- reading a file, Reading and Writing Files-Reading and Writing Files, Read a File
- readlines() method, Reading and Writing Files, Read a File
- real-time streaming ingestion, Real-Time Streaming Ingestion
- recursive globbing, Recursive Globbing
- regular expressions, Using Regular Expressions-Compiling
- character classes, Character Classes
- character sets, Character Sets
- compiling, Compiling
- find all, Find All
- find iterator, Find Iterator
- groups, Groups
- named groups, Named Groups
- pulling information from log with, Using Regular Expressions to Search Text
- searching, Searching
- substitution, Substitution
- Reis, Joseph, Joseph Reis-Joseph Reis
- removing temporary Python files, Removing Temporary Python Files
- Roche, Rigoberto, Rigoberto Roche-Rigoberto Roche
- Roman, Gabriella, Gabriella Roman
- route53 DNS record, provisioning, Provisioning a Route 53 DNS Record, Provisioning a Route 53 Zone and DNS Records-Provisioning a Route 53 Zone and DNS Records, Provisioning a Route 53 DNS Record for the Site URL
- RPM packaging, RPM Packaging-RPM repositories
- rpmbuild command-line tool, Producing the binary
S
- s3 bucket
- SaaS (Software as a service), Software as a Service
- scale input, sklearn flask and, Scale Input, Scale Input
- scatterplot, Scatterplot
- Scikit-Learn, Machine Learning Key Terminology
- SDNs (Software Defined Networks), Software Defined Networks
- SDS (Software Defined Storage), Software Defined Storage
- searches, regex, Searching
- searching, Using Regular Expressions to Search Text
- sequence operations, Sequence operations
- sequences, Sequences-Dicts
- serverless computing, Serverless Computing-Serverless Computing
- serverless data engineering, Serverless Data Engineering-Conclusion
- serverless deploy command, Deploying Python Function to AWS Lambda
- serverless invoke command, Deploying Python Function to AWS Lambda
- serverless technologies, Serverless Technologies-Exercises
- about, Serverless Technologies-Serverless Technologies
- deploying Python function to AWS Lambda, Deploying Python Function to AWS Lambda-Deploying Python Function to AWS Lambda
- deploying Python function to Azure, Deploying Python Function to Azure-Deploying Python Function to Azure
- deploying Python function to Google Cloud Functions, Deploying Python Function to Google Cloud Functions-Deploying Python Function to Google Cloud Functions
- deploying Python function to OpenFaas, Deploying Python Function to OpenFaaS-Deploying Python Function to OpenFaaS
- deploying Python function to self-hosted Faas platforms, Deploying a Python Function to Self-Hosted FaaS Platforms-Deploying Python Function to OpenFaaS
- deploying the same Python function to the Big Three cloud providers, Deploying the Same Python Function to the “Big Three” Cloud Providers-Deploying Python Function to Azure
- installing the Serverless framework, Installing Serverless Framework
- provisioning DynamoDB table/Lambda functions/API Gateway methods using the AWS CDK, Provisioning DynamoDB Table, Lambda Functions, and API Gateway Methods Using the AWS CDK-Provisioning DynamoDB Table, Lambda Functions, and API Gateway Methods Using the AWS CDK
- setattr, Built-in Fixtures
- setup() function, Package files
- setuptools, The spec file
- shells, Working with the Shell-Spawn Processes with the subprocess Module
- shells, Bash/ZSH, Working with Bash and ZSH-Converting a CSV File to JSON
- changing directories to module's path, Changing Directories to a Module’s Path
- converting CSV file to JSON, Converting a CSV File to JSON
- customizing the Python shell, Customizing the Python Shell
- determining module existence and finding path to module, Does My Module Exist?
- listing and filtering processes, Listing and Filtering Processes
- mixing Python, Mixing Python with Bash and ZSH-Converting a CSV File to JSON
- random password generator, Random Password Generator
- recursive globbing, Recursive Globbing
- removing temporary Python files, Removing Temporary Python Files
- searching and replacing with confirmation prompts, Searching and Replacing with Confirmation Prompts
- Unix timestamp, Unix Timestamp
- site URL, route53 DNS record provisioning for, Provisioning a Route 53 DNS Record for the Site URL
- sklearn-based machine learning model, Sklearn Flask with Kubernetes and Docker-Scale Input
- adhoc predict, adhoc_predict, adhoc_predict from Pickle
- EDA, EDA
- evaluating model, Evaluate
- fitting model, Fit Model
- JSON workflow, JSON Workflow
- Kubernetes and Docker, Sklearn Flask with Kubernetes and Docker-Scale Input
- modeling, Modeling
- scale input, Scale Input, Scale Input
- tune scaled GBM, Tune Scaled GBM
- sleep command, Set timeouts and handle them when appropriate
- small data, Small Data
- Software as a service (SaaS), Software as a Service
- Software Defined Networks (SDNs), Software Defined Networks
- Software Defined Storage (SDS), Software Defined Storage
- Solomon, Glenn, Glenn Solomon
- spawn processes, Spawn Processes with the subprocess Module
- spec file, The spec file
- split() method, Managing Files and Directories Using os.path
- splitting data, Split the data
- SQS (Simple Queuing Service)
- SSH tunneling, SSH Tunneling
- SSL certificate, provisioning with ACM, Provisioning an SSL Certificate with AWS ACM, Provisioning an ACM SSL Certificate
- staging stack, Creating Configuration Values for the Staging Stack
- static files, copying, Copying Static Files to S3
- statistics, descriptive, Descriptive Statistics
- StatsD, StatsD, Instrumentation
- storage, big data, Data Storage
- strace, Viewing Processes with htop, strace-strace
- strings, Strings-Strings
- string_to_bool() function, Parametrization
- subprocess, Manage Processes with Subprocess-The problem with Python threads
- subprocess.run() function, Spawn Processes with the subprocess Module
- substitution, in regex searches, Substitution
- Subversion (SVN), Useful Linux Utilities
- supervised machine learning, Supervised Machine Learning-Kernel Density Distribution
- supervisord, Management with systemd
- SVN (Subversion), Useful Linux Utilities
- symmetric key encryption, Encryption with Cryptography
- sys module, Talking to the Interpreter with the sys Module
- sys.argv, Using sys.argv-Using sys.argv
- sys.byteorder, Talking to the Interpreter with the sys Module
- sys.getsizeof, Talking to the Interpreter with the sys Module
- sys.version_info, Talking to the Interpreter with the sys Module
- system validation, What Is System Validation?
- systemd, Choosing a Strategy, Management with systemd-The systemd Unit File
T
- target, defined, Machine Learning Key Terminology
- temporary Python files, removing, Removing Temporary Python Files
- Terraform, Automated Infrastructure Provisioning with Terraform-Deleting All AWS Resources Provisioned with Terraform
- terraform apply, Provisioning an S3 Bucket, Provisioning an Amazon CloudFront Distribution
- terraform destroy, Deleting All AWS Resources Provisioned with Terraform
- terraform init command, Provisioning an S3 Bucket
- terraform plan command, Provisioning an S3 Bucket
- Testinfra, Introduction to Testinfra
- test_list_todos() function, Provisioning DynamoDB Table, Lambda Functions, and API Gateway Methods Using the AWS CDK
- timeit module, How Fast Is this Snippet?
- touch README command, Package files
- traditional machine learning, continuous delivery of, Level 5: Continuous Delivery of Traditional ML and AutoML
- Tuulos, Ville, Ville Tuulos-Ville Tuulos
Z
- ZSH (see shells, Bash/ZSH)
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