Noah Gift

Apply generative AI techniques like prompt engineering and few-shot learning to guide model outputs.
Utilize AI pair programming tools and MLOps best practices to enhance development workflows.
Deploy and integrate language models using cloud services such as AWS and Azure OpenAI Service.
Serve powerful language models as scalable web APIs for both cloud and local environments.
Leverage Small Language Models (SLMs) and Large Language Models (LLMs) for various NLP tasks.

Gain a solid understanding of generative AI models, their capabilities, and how to provide effective prompts for optimal outputs.
Master Azure AI services, learn to manage GPU quotas, deploy LLMs, leverage Azure Machine Learning, and utilize Azure OpenAI Service.
Deploy and manage LLMs on AWS, optimize cost and performance, monitor metrics, build secure pipelines, and comply with regulations.
Leverage open-source LLMs like LLaMA and Mistral, fine-tune models on custom datasets, and… Show more

Gain a solid understanding of generative AI models, their capabilities, and how to provide effective prompts for optimal outputs.
Master Azure AI services, learn to manage GPU quotas, deploy LLMs, leverage Azure Machine Learning, and utilize Azure OpenAI Service.
Deploy and manage LLMs on AWS, optimize cost and performance, monitor metrics, build secure pipelines, and comply with regulations.
Leverage open-source LLMs like LLaMA and Mistral, fine-tune models on custom datasets, and containerize for efficient deployment.
Scale data engineering systems using Celery, RabbitMQ, Airflow, and graph databases, optimizing performance for large, complex datasets. Show less

Master Python fundamentals, MLOps principles, and data management to build and deploy ML models in production environments.
Utilize Amazon Sagemaker / AWS, Azure, MLflow, and Hugging Face for end-to-end ML solutions, pipeline creation, and API development.
Fine-tune and deploy Large Language Models (LLMs) and containerized models using the ONNX format with Hugging Face.
Design a full MLOps pipeline with MLflow, managing projects, models, and tracking system features.

Implement DevOps workflows by building, deploying, and monitoring applications using Rust, leveraging containerization and observability.
Develop proficiency in Rust programming, mastering core concepts, error handling, and package management for efficient, reliable code.
Construct robust data processing systems by utilizing Rust's data structures, safety features, and interfacing with databases and cloud services.
Craft sophisticated command-line tools and utilities for automation… Show more

Implement DevOps workflows by building, deploying, and monitoring applications using Rust, leveraging containerization and observability.
Develop proficiency in Rust programming, mastering core concepts, error handling, and package management for efficient, reliable code.
Construct robust data processing systems by utilizing Rust's data structures, safety features, and interfacing with databases and cloud services.
Craft sophisticated command-line tools and utilities for automation using Python or Rust, enhancing productivity and efficiency.
Integrate Rust with LLM frameworks like HuggingFace Transformers, and deploy large models on cloud infrastructures while applying DevOps principles. Show less

LEVEL UP YOUR RUST SKILLS AND PUSH MLOPS FORWARD WITH GITHUB COPILOT.

Learn how to apply Machine Learning Operations (MLOps) to solve real-world problems. The course covers end-to-end solutions with Artificial Intelligence (AI) pair programming using technologies like GitHub Copilot to build solutions for machine learning (ML) and AI applications. This course is for people working (or seeking to work) as data scientists, software engineers or developers, data analysts, or other roles that use ML

In MLOps (Machine Learning Operations) Platforms: Amazon SageMaker and Azure ML you will learn the necessary skills to build, train, and deploy machine learning solutions in a production environment using two leading cloud platforms: Amazon Web Services (AWS) and Microsoft Azure. This course is also a great resource for individuals looking to prepare for AWS or Azure machine learning certifications or who are working (or seek to work) as data scientists, software engineers, software developers,… Show more

In MLOps (Machine Learning Operations) Platforms: Amazon SageMaker and Azure ML you will learn the necessary skills to build, train, and deploy machine learning solutions in a production environment using two leading cloud platforms: Amazon Web Services (AWS) and Microsoft Azure. This course is also a great resource for individuals looking to prepare for AWS or Azure machine learning certifications or who are working (or seek to work) as data scientists, software engineers, software developers, data analysts, or other roles that use machine learning.

Through a series of hands-on exercises, you will gain an intuition for basic machine learning algorithms and practical experience working with these leading Cloud platforms. By the end of the course, you will be able to deploy machine learning solutions in a production environment using AWS and Azure technology.


Week 1. Explore data engineering with AWS technology. We’ll discuss topics such as getting started with machine learning on AWS, creating data repositories, and identifying and implementing solutions for data ingestion and transformation.

Week 2. Gain basic data science skills with AWS technology. You will learn data cleaning techniques, perform feature engineering, data analysis, and data visualization for machine learning. We’ll prioritize using serverless solutions that are available on AWS to make the process more efficient.

Week 3. Learn machine learning models with AWS technology. We’ll examine how to select appropriate models for the task at hand, choose hyperparameters, train models on the platform, and evaluate models.

Week 4. Learn MLOps with AWS: the final phase of putting machine learning into production. We’ll discuss topics such as operationalizing a machine learning model, deciding between CPU and GPU, and deploying and maintaining the model.

Week 5. Learn how to work with data and machine learning in a second leading Cloud-based platform: Azure ML. Show less

This course covers two of the most popular open source platforms for MLOps (Machine Learning Operations): MLflow and Hugging Face. We’ll go through the foundations on what it takes to get started in these platforms with basic model and dataset operations. You will start with MLflow using projects and models with its powerful tracking system and you will learn how to interact with these registered models from MLflow with full lifecycle examples. Then, you will explore Hugging Face repositories… Show more

This course covers two of the most popular open source platforms for MLOps (Machine Learning Operations): MLflow and Hugging Face. We’ll go through the foundations on what it takes to get started in these platforms with basic model and dataset operations. You will start with MLflow using projects and models with its powerful tracking system and you will learn how to interact with these registered models from MLflow with full lifecycle examples. Then, you will explore Hugging Face repositories so that you can store datasets, models, and create live interactive demos.

By the end of the course, you will be able to apply MLOps concepts like fine-tuning and deploying containerized models to the Cloud. This course is ideal for anyone looking to break into the field of MLOps or for experienced MLOps professionals who want to improve their programming skills. Show less

Python Essentials for MLOps (Machine Learning Operations) is a course designed to provide learners with the fundamental Python skills needed to succeed in an MLOps role. This course covers the basics of the Python programming language, including data types, functions, modules and testing techniques. It also covers how to work effectively with data sets and other data science tasks with Pandas and NumPy. Through a series of hands-on exercises, learners will gain practical experience working with… Show more

Python Essentials for MLOps (Machine Learning Operations) is a course designed to provide learners with the fundamental Python skills needed to succeed in an MLOps role. This course covers the basics of the Python programming language, including data types, functions, modules and testing techniques. It also covers how to work effectively with data sets and other data science tasks with Pandas and NumPy. Through a series of hands-on exercises, learners will gain practical experience working with Python in the context of an MLOps workflow. By the end of the course, learners will have the necessary skills to write Python scripts for automating common MLOps tasks. This course is ideal for anyone looking to break into the field of MLOps or for experienced MLOps professionals who want to improve their Python skills. Show less

With demand for scaling, real-time access, and other capabilities, businesses need to consider building operational machine learning pipelines. This practical guide helps your company bring data science to life for different real-world MLOps scenarios. Senior data scientists, MLOps engineers, and machine learning engineers will learn how to tackle challenges that prevent many businesses from moving ML models to production.

Authors Yaron Haviv and Noah Gift take a production-first… Show more

With demand for scaling, real-time access, and other capabilities, businesses need to consider building operational machine learning pipelines. This practical guide helps your company bring data science to life for different real-world MLOps scenarios. Senior data scientists, MLOps engineers, and machine learning engineers will learn how to tackle challenges that prevent many businesses from moving ML models to production.

Authors Yaron Haviv and Noah Gift take a production-first approach. Rather than beginning with the ML model, you'll learn how to design a continuous operational pipeline, while making sure that various components and practices can map into it. By automating as many components as possible, and making the process fast and repeatable, your pipeline can scale to match your organization's needs.

You'll learn how to provide rapid business value while answering dynamic MLOps requirements. This book will help you:

Learn the MLOps process, including its technological and business value
Build and structure effective MLOps pipelines
Efficiently scale MLOps across your organization
Explore common MLOps use cases
Build MLOps pipelines for hybrid deployments, real-time predictions, and composite AI
Learn how to prepare for and adapt to the future of MLOps
Effectively use pre-trained models like HuggingFace and OpenAI to complement your MLOps strategy Show less

Many organizations today have begun to modernize their Windows workloads to take full advantage of cloud economics. If you're a C# developer at one of these companies, you need options for rehosting, replatforming, and refactoring your existing .NET Framework applications. This practical book guides you through the process of converting your monolithic application to microservices on AWS.

Authors Noah Gift, founder of Pragmatic AI Labs, and James Charlesworth, engineering manager at… Show more

Many organizations today have begun to modernize their Windows workloads to take full advantage of cloud economics. If you're a C# developer at one of these companies, you need options for rehosting, replatforming, and refactoring your existing .NET Framework applications. This practical book guides you through the process of converting your monolithic application to microservices on AWS.

Authors Noah Gift, founder of Pragmatic AI Labs, and James Charlesworth, engineering manager at Pendo, take you through the depth and breadth of .NET tools on AWS. You'll examine modernization techniques and pathways for incorporating Linux and Windows containers and serverless architecture to build, maintain, and scale modern .NET apps on AWS. With this book, you'll learn how to make your applications more modern, resilient, and cost-effective.

Get started building solutions with C# on AWS
Learn DevOps best practices for AWS
Explore the development tools and services that AWS provides
Successfully migrate a legacy .NET application to AWS
Develop serverless .NET microservices on AWS
Containerize your .NET applications and move into the cloud
Monitor and test your AWS .NET applications
Build cloud native solutions that combine the best of the .NET platform and AWS Show less

Getting certified by AWS can be a big boost for your career in cloud application development and hybrid architectural design. In this course—the first in a five-part certification prep series—instructor Noah Gift shows you the skills you need to know to prepare for and tackle the AWS Certified Solutions Architect Professional exam (SAP-C01).

Discover the benefits of the AWS Certified Solutions Architect Professional certification, which validates your ability to design, deploy, and… Show more

Getting certified by AWS can be a big boost for your career in cloud application development and hybrid architectural design. In this course—the first in a five-part certification prep series—instructor Noah Gift shows you the skills you need to know to prepare for and tackle the AWS Certified Solutions Architect Professional exam (SAP-C01).

Discover the benefits of the AWS Certified Solutions Architect Professional certification, which validates your ability to design, deploy, and evaluate applications on AWS within diverse, complex requirements. Learn about the two question formats you’ll encounter on the exam, as well as the exam content outline, the scoring rubric, and the recommended prerequisites for aspiring candidates. Along the way, Noah reviews cross-account authentication and access strategy, designing networks, and setting up multi-account AWS environment Show less

Getting certified by AWS can be a big boost for your career in cloud application development and hybrid architectural design. In this course—the second in a five-part certification prep series—instructor Noah Gift shows you the skills you need to know to prepare for and tackle the AWS Certified Solutions Architect Professional exam (SAP-C01).

Discover the benefits of the AWS Certified Solutions Architect Professional certification, which validates your ability to design, deploy, and… Show more

Getting certified by AWS can be a big boost for your career in cloud application development and hybrid architectural design. In this course—the second in a five-part certification prep series—instructor Noah Gift shows you the skills you need to know to prepare for and tackle the AWS Certified Solutions Architect Professional exam (SAP-C01).

Discover the benefits of the AWS Certified Solutions Architect Professional certification, which validates your ability to design, deploy, and evaluate applications on AWS within diverse, complex requirements. Learn about security requirements and controls, and solution design and implementation strategy to meet reliability requirements, ensure business continuity, and achieve performance objectives. Show less

Getting certified by AWS can be a big boost for your career in cloud application development and hybrid architectural design. In this course—the third in a five-part certification prep series—instructor Noah Gift shows you the skills you need to know to prepare for and tackle the AWS Certified Solutions Architect Professional exam (SAP-C01).

Discover the benefits of the AWS Certified Solutions Architect Professional certification, which validates your ability to design, deploy, and… Show more

Getting certified by AWS can be a big boost for your career in cloud application development and hybrid architectural design. In this course—the third in a five-part certification prep series—instructor Noah Gift shows you the skills you need to know to prepare for and tackle the AWS Certified Solutions Architect Professional exam (SAP-C01).

Discover the benefits of the AWS Certified Solutions Architect Professional certification, which validates your ability to design, deploy, and evaluate applications on AWS within diverse, complex requirements. Learn about selecting existing workloads and processes, selecting migration tools and services, determining a new cloud architecture for an existing solution, and determining a strategy for migrating existing on-premises workloads to the cloud. Show less

If you are interested in developing the skills needed to be a data engineer, the Python, Bash and SQL Essentials for Data Engineering Specialization is a great place to start. We live in a world that is driven by big data - from what we search online to the route we take to our favorite restaurant, and everything in between. Businesses and organizations use this data to make decisions that impact the ways in which we navigate our lives. How do engineers collect this data? How can this data be… Show more

If you are interested in developing the skills needed to be a data engineer, the Python, Bash and SQL Essentials for Data Engineering Specialization is a great place to start. We live in a world that is driven by big data - from what we search online to the route we take to our favorite restaurant, and everything in between. Businesses and organizations use this data to make decisions that impact the ways in which we navigate our lives. How do engineers collect this data? How can this data be organized so that it can be appropriately analyzed? A data engineer is specialized in this initial step of accessing, cleaning and managing big data.

Data engineers today need a solid foundation in a few essential areas: Python, Bash and SQL. In Python, Bash and SQL Essentials for Data Engineering, we provide a nuts and bolts overview of these fundamental skills needed for entering the world of data engineering. Led by three professional data engineers, this Specialization will provide quick and accessible ways to learn data engineering strategies, give you a chance to practice what you’ve learned in integrated lab exercises, and then immediately apply these techniques in your professional or academic life. Show less

In this fourth course of the Python, Bash and SQL Essentials for Data Engineering Specialization, you will build upon the data engineering concepts introduced in the first three courses to apply Python, Bash and SQL techniques in tackling real-world problems. First, we will dive deeper into leveraging Jupyter notebooks to create and deploy models for machine learning tasks. Then, we will explore how to use Python microservices to break up your data warehouse into small, portable solutions that… Show more

In this fourth course of the Python, Bash and SQL Essentials for Data Engineering Specialization, you will build upon the data engineering concepts introduced in the first three courses to apply Python, Bash and SQL techniques in tackling real-world problems. First, we will dive deeper into leveraging Jupyter notebooks to create and deploy models for machine learning tasks. Then, we will explore how to use Python microservices to break up your data warehouse into small, portable solutions that can scale. Finally, you will build a powerful command-line tool to automate testing and quality control for publishing and sharing your tool with a data registry Show less

In this second course of the Python, Bash and SQL Essentials for Data Engineering Specialization, you will learn the fundamentals of Linux necessary to perform data engineering tasks. Additionally, you will explore how to use both Bash and zsh configurations, and develop the syntax needed to interact and control Linux. These skills will allow you to manage and manipulate databases in a Bash environment.

In this first course of the Python, Bash and SQL Essentials for Data Engineering Specialization, you will learn how to set up a version-controlled Python working environment which can utilize third party libraries. You will learn to use Python and the powerful Pandas library for data analysis and manipulation. Additionally, you will also be introduced to Vim and Visual Studio Code, two popular tools for writing software. This course is valuable for beginning and intermediate students in order… Show more

In this first course of the Python, Bash and SQL Essentials for Data Engineering Specialization, you will learn how to set up a version-controlled Python working environment which can utilize third party libraries. You will learn to use Python and the powerful Pandas library for data analysis and manipulation. Additionally, you will also be introduced to Vim and Visual Studio Code, two popular tools for writing software. This course is valuable for beginning and intermediate students in order to begin transforming and manipulating data as a data engineer. Show less

In this third course of the Python, Bash and SQL Essentials for Data Engineering Specialization, you will explore techniques to work effectively with Python and SQL. We will go through useful data structures in Python scripting and connect to databases like MySQL. Additionally, you will learn how to use a modern text editor to connect and run SQL queries against a real database, performing operations to load and extract data. Finally, you will use extracted data from websites using scraping… Show more

In this third course of the Python, Bash and SQL Essentials for Data Engineering Specialization, you will explore techniques to work effectively with Python and SQL. We will go through useful data structures in Python scripting and connect to databases like MySQL. Additionally, you will learn how to use a modern text editor to connect and run SQL queries against a real database, performing operations to load and extract data. Finally, you will use extracted data from websites using scraping techniques. These skills will allow you to work effectively when data is not readily available, or when spatial queries are required to extract useful information from databases. Show less

Are you preparing for the Microsoft Azure Data Engineering (DP-203) exam, or just looking for a stronger understanding of how to design and implement data storage? Either way, this course, the first in a series, can help you. Noah Gift, founder of Pragmatic A.I. Labs and a Python Software Foundation Fellow, shows you how to design and implement data storage on Azure. Noah walks you through the full process of designing a data storage structure, from designing an Azure Data Lake solution to… Show more

Are you preparing for the Microsoft Azure Data Engineering (DP-203) exam, or just looking for a stronger understanding of how to design and implement data storage? Either way, this course, the first in a series, can help you. Noah Gift, founder of Pragmatic A.I. Labs and a Python Software Foundation Fellow, shows you how to design and implement data storage on Azure. Noah walks you through the full process of designing a data storage structure, from designing an Azure Data Lake solution to designing a data archiving solution. He explains horizontal, vertical, and functional data partitioning strategies and how to choose which strategy will work best for your data. Noah goes over designing both analytical stores and metastores, then dives into implementing table geometries, data storage structures, data architectures, and more. Show less

Are you preparing for the Microsoft Azure Data Engineering (DP-203) exam, or seeking a better understanding of how to design and develop data processing? This course, the second in a series, can help you. Noah Gift, founder of Pragmatic A.I. Labs and a Python Software Foundation Fellow, covers how to design and develop data processing with Azure. Noah shows you how to use Apache Spark, Data Factory, and Databricks to ingest and transform data in Azure. He goes over data cleaning and common data… Show more

Are you preparing for the Microsoft Azure Data Engineering (DP-203) exam, or seeking a better understanding of how to design and develop data processing? This course, the second in a series, can help you. Noah Gift, founder of Pragmatic A.I. Labs and a Python Software Foundation Fellow, covers how to design and develop data processing with Azure. Noah shows you how to use Apache Spark, Data Factory, and Databricks to ingest and transform data in Azure. He goes over data cleaning and common data transformation tasks, then dives into batch processing solutions. After explaining how to integrate Jupyter/Python notebooks into your data pipeline, Noah discusses stream processing, the differences between stream and batch processing, and using the Azure Data Factory solution to manage your batches and pipelines. Show less

Getting your models into production is the fundamental challenge of machine learning. MLOps offers a set of proven principles aimed at solving this problem in a reliable and automated way. This insightful guide takes you through what MLOps is (and how it differs from DevOps) and shows you how to put it into practice to operationalize your machine learning models.

Launch Your Career in Cloud Computing. Master strategies and tools to become proficient in developing data science and machine learning solutions in the Cloud

Welcome to the first course in the Building Cloud Computing Solutions at Scale Specialization! In this course, you will learn how to build foundational Cloud computing infrastructure, including websites involving serverless technology and virtual machines. You will also learn how to apply Agile software development techniques to projects which will be useful in building portfolio projects and global-scale Cloud infrastructures.

This course is ideal for beginners as well as intermediate… Show more

Welcome to the first course in the Building Cloud Computing Solutions at Scale Specialization! In this course, you will learn how to build foundational Cloud computing infrastructure, including websites involving serverless technology and virtual machines. You will also learn how to apply Agile software development techniques to projects which will be useful in building portfolio projects and global-scale Cloud infrastructures.

This course is ideal for beginners as well as intermediate students interested in applying Cloud computing to data science, machine learning and data engineering. Students should have beginner level Linux and intermediate level Python skills. For your project in this course, you will build a statically hosted website using the Hugo framework, AWS Code Pipelines, AWS S3 and GitHub. Show less

Welcome to the third course in the Building Cloud Computing Solutions at Scale Specialization! In this course, you will learn how to apply Data Engineering to real-world projects using the Cloud computing concepts introduced in the first two courses of this series. By the end of this course, you will be able to develop Data Engineering applications and use software development best practices to create data engineering applications. These will include continuous deployment, code quality tools… Show more

Welcome to the third course in the Building Cloud Computing Solutions at Scale Specialization! In this course, you will learn how to apply Data Engineering to real-world projects using the Cloud computing concepts introduced in the first two courses of this series. By the end of this course, you will be able to develop Data Engineering applications and use software development best practices to create data engineering applications. These will include continuous deployment, code quality tools, logging, instrumentation and monitoring. Finally, you will use Cloud-native technologies to tackle complex data engineering solutions.

This course is ideal for beginners as well as intermediate students interested in applying Cloud computing to data science, machine learning and data engineering. Students should have beginner level Linux and intermediate level Python skills. For your project in this course, you will build a serverless data engineering pipeline in a Cloud platform: Amazon Web Services (AWS), Azure or Google Cloud Platform (GCP). Show less

Welcome to the fourth course in the Building Cloud Computing Solutions at Scale Specialization! In this course, you will build upon the Cloud computing and data engineering concepts introduced in the first three courses to apply Machine Learning Engineering to real-world projects. First, you will develop Machine Learning Engineering applications and use software development best practices to create Machine Learning Engineering applications. Then, you will learn to use AutoML to solve problems… Show more

Welcome to the fourth course in the Building Cloud Computing Solutions at Scale Specialization! In this course, you will build upon the Cloud computing and data engineering concepts introduced in the first three courses to apply Machine Learning Engineering to real-world projects. First, you will develop Machine Learning Engineering applications and use software development best practices to create Machine Learning Engineering applications. Then, you will learn to use AutoML to solve problems more efficiently than traditional machine learning approaches alone. Finally, you will dive into emerging topics in Machine Learning including MLOps, Edge Machine Learning and AI APIs.

This course is ideal for beginners as well as intermediate students interested in applying Cloud computing to data science, machine learning and data engineering. Students should have beginner level Linux and intermediate level Python skills. For your project in this course, you will build a Flask web application that serves out Machine Learning predictions. Show less

Welcome to the second course in the Building Cloud Computing Solutions at Scale Specialization! In this course, you will learn to design Cloud-native systems with the fundamental building blocks of Cloud computing. These building blocks include virtual machines and containers. You will also learn how to build effective Microservices using technologies like Flask and Kubernetes. Finally, you will analyze successful patterns in Operations including: Effective alerts, load testing and Kaizen… Show more

Welcome to the second course in the Building Cloud Computing Solutions at Scale Specialization! In this course, you will learn to design Cloud-native systems with the fundamental building blocks of Cloud computing. These building blocks include virtual machines and containers. You will also learn how to build effective Microservices using technologies like Flask and Kubernetes. Finally, you will analyze successful patterns in Operations including: Effective alerts, load testing and Kaizen.

This course is ideal for beginners as well as intermediate students interested in applying Cloud computing to data science, machine learning and data engineering. Students should have beginner level Linux and intermediate level Python skills. For your project in this course, you build a containerized Flask application that is continuously deployed to a Cloud platform: Amazon Web Services (AWS), Azure or Google Cloud Platform (GCP). Show less

Chinese Version of Python for DevOps

This book is designed to give you a comprehensive view of cloud computing including Big Data and Machine Learning. A variety of learning resources will be used including interactive labs on Cloud Platforms (Google, AWS, Azure) using Python. This is a project-based book with extensive hands-on assignments. A practical guide to Data Science, Machine Learning Engineering and Data Engineering

Noah and Alfredo have decades of experience testing with Python in major production environments. Learn from the best on how to get started and advance your automation with easy examples and code to follow up.

This course takes a user on a journey to understand and then master the AWS Cloud. This material is taught in many forms at top universities around the world. This material has been adjusted to the unique properties of the Udemy platform. There are many techniques exposed that will also prepare a student interested in Python, Data Science, Data Engineering or Machine Learning to use the AWS cloud in those domains.

Some of the hands-on demos include:

° How to build websites… Show more

This course takes a user on a journey to understand and then master the AWS Cloud. This material is taught in many forms at top universities around the world. This material has been adjusted to the unique properties of the Udemy platform. There are many techniques exposed that will also prepare a student interested in Python, Data Science, Data Engineering or Machine Learning to use the AWS cloud in those domains.

Some of the hands-on demos include:

° How to build websites using: AWS S3, AWS Lambda, and AWS EC2 Instances

° How to use AWS Spot Instances and run machine learning workloads

° How to write simple Python functions and deploy them using AWS Lambda

* How to create AWS S3 buckets and synchronize data to them

° How to use AWS Cloud9 to develop software and interact with the AWS Cloud

° How to create a user account in AWS IAM

° How to manage billing and set up alerts to protect you from unexpected charges Show less

There are certain skills that will stay with you your entire life. One of those skills is learning to automate things. There is a motto for automation that gets straight to the point, "If it isn't automated...it's broken". In this course, you learn to adopt this mindset. In one of the many examples, you will create automation code that will traverse a filesystem, find files that match a pattern, and then detect which files are duplicates. Following the course, you will be able to automate many… Show more

There are certain skills that will stay with you your entire life. One of those skills is learning to automate things. There is a motto for automation that gets straight to the point, "If it isn't automated...it's broken". In this course, you learn to adopt this mindset. In one of the many examples, you will create automation code that will traverse a filesystem, find files that match a pattern, and then detect which files are duplicates. Following the course, you will be able to automate many common file system tasks and be able to manage and communicate with Unix processes. Show less

Learn to design and deploy infrastructure as code, build and monitor CI/CD pipelines for different deployment strategies, and deploy scalable microservices using Kubernetes. At the end of the program, you’ll combine your new skills by completing a capstone project.

There is a rapid evolution occurring in machine learning with tools like AutoML that basically automate many of the tedious aspects of machine learning and allow developers to focus on getting results into production. Noah Gift illustrates just now to harness this technology and deploy it successfully on Google Cloud Platform, demonstrating for developers how to employ the current best practices and automated tools to create analytics applications that solve real-world… Show more

There is a rapid evolution occurring in machine learning with tools like AutoML that basically automate many of the tedious aspects of machine learning and allow developers to focus on getting results into production. Noah Gift illustrates just now to harness this technology and deploy it successfully on Google Cloud Platform, demonstrating for developers how to employ the current best practices and automated tools to create analytics applications that solve real-world problems.

Developers who want to take their Data Science skills to the next level and build AutoML applications in the Cloud will benefit from this unique course, as they learn how to use AutoML, Big Query, Python, and Google App Engine to create sophisticated AI. Show less

AWS leads the world in cloud computing and big data. This course offers the complete package to help practitioners master the core skills and competencies needed to build successful, high-value big data applications, with a clear path toward passing the certification exam AWS Certified Big Data - Specialty.
This course provides a solid foundation in all areas required to pass the AWS Certified Big Data Specialty Exam–including Collection, Storage, Processing, Analysis, Visualization, and… Show more

AWS leads the world in cloud computing and big data. This course offers the complete package to help practitioners master the core skills and competencies needed to build successful, high-value big data applications, with a clear path toward passing the certification exam AWS Certified Big Data - Specialty.
This course provides a solid foundation in all areas required to pass the AWS Certified Big Data Specialty Exam–including Collection, Storage, Processing, Analysis, Visualization, and Data Security. In addition, multiple quizzes and a practice exam prepare the student for the formal Certification Exam administered by AWS. Show less

Much has changed in technology over the past decade. Data is hot, the cloud is ubiquitous, and many organizations need some form of automation. Throughout all these transformations, Python has become one of the most popular languages in the world. This practical guide shows you how to use Python for everyday Linux systems administration tasks with today’s most useful DevOps tools, including Docker, Kubernetes, and Terraform.

Learning how to interact and automate with Linux is an… Show more

Much has changed in technology over the past decade. Data is hot, the cloud is ubiquitous, and many organizations need some form of automation. Throughout all these transformations, Python has become one of the most popular languages in the world. This practical guide shows you how to use Python for everyday Linux systems administration tasks with today’s most useful DevOps tools, including Docker, Kubernetes, and Terraform.

Learning how to interact and automate with Linux is an essential skill for millions of professionals. Python makes it much easier. With this book, you’ll learn how to develop software and solve problems using containers, as well as how to monitor, instrument, load-test, and operationalize your software. If you’re looking for effective ways to "get stuff done" in Python, this is your guide.

Automate several tasks using Python
Work more efficiently by using a smaller subset of the language
Use continuous integration systems to increase software quality
Mix shell and Python commands to solve problems Show less

An interactive Jupyter book companion to Python for Data Science video from Pearson.

This 8-hour LiveLesson video course shows how AWS and Google Cloud Platform can be used to solve real-world business problems in Machine Learning and AI. Noah Gift covers how to get started with Python via Jupyter Notebook, and then proceeds to dive into nuts and bolts of Data Science libraries in Python, including Pandas, Seaborn, scikit-learn, and TensorFlow.

EDA, or exploratory data analysis, is at the heart of the Machine Learning; therefore, this series also highlights how to… Show more

This 8-hour LiveLesson video course shows how AWS and Google Cloud Platform can be used to solve real-world business problems in Machine Learning and AI. Noah Gift covers how to get started with Python via Jupyter Notebook, and then proceeds to dive into nuts and bolts of Data Science libraries in Python, including Pandas, Seaborn, scikit-learn, and TensorFlow.

EDA, or exploratory data analysis, is at the heart of the Machine Learning; therefore, this series also highlights how to perform EDA in Python and Jupyter Notebook. Software engineering fundamentals tie the series together, with key instruction on linting, testing, command-line tools, data engineering APIs, and more. Show less

There is an overwhelming demand to learn business focused Python-based Machine Learning. This training is about learning how to apply Machine Learning techniques in Python to common business applications. Examples of this could be classifying types of users registered on a shopping site, to using regression to predict the sales for the next month.

The live training shows how to get started with the basics in Python via Jupyter notebooks, then proceeds to dive into nuts and bolts of Data… Show more

There is an overwhelming demand to learn business focused Python-based Machine Learning. This training is about learning how to apply Machine Learning techniques in Python to common business applications. Examples of this could be classifying types of users registered on a shopping site, to using regression to predict the sales for the next month.

The live training shows how to get started with the basics in Python via Jupyter notebooks, then proceeds to dive into nuts and bolts of Data Science libraries in Python. EDA, or exploratory data analysis, is at the heart of the Machine Learning feedback look, and this series will highlight how to perform this in Python and Jupyter Notebook.

Finally, AWS will be used to expand the machine learning concepts to real world environments in the cloud. Machine Learning on AWS concepts will cover how to do batch based job workflows for Machine Learning pipelines, as well as the use of the boto library. Show less

Pragmatic AI is the first truly practical guide to solving real-world problems with contemporary machine learning, artificial intelligence, and cloud computing tools. Writing for business professionals, decision-makers, and students who aren’t professional data scientists, Noah Gift demystifies all the tools and technologies you need to get results. He illuminates powerful off-the-shelf cloud-based solutions from Google, Amazon, and Microsoft, as well as accessible techniques using Python and… Show more

Pragmatic AI is the first truly practical guide to solving real-world problems with contemporary machine learning, artificial intelligence, and cloud computing tools. Writing for business professionals, decision-makers, and students who aren’t professional data scientists, Noah Gift demystifies all the tools and technologies you need to get results. He illuminates powerful off-the-shelf cloud-based solutions from Google, Amazon, and Microsoft, as well as accessible techniques using Python and R. Throughout, you’ll find simple, clear, and effective working solutions that show how to apply machine learning, AI and cloud computing together in virtually any organization, creating solutions that deliver results, and offer virtually unlimited scalability. Coverage includes:

Getting and configuring all the tools you’ll need
Quickly and efficiently deploying AI applications using spreadsheets, R, and Python
Mastering the full application lifecycle: Download, Extract, Transform, Model, Serve Results
Getting started with Cloud Machine Learning Services, Amazon’s AWS AI Services, and Microsoft’s Cognitive Services API
Uncovering signals in Facebook, Twitter and Wikipedia
Listening to channels via Slack bots running on AWS Lambda (serverless)
Retrieving data via the Twitter API and extract follower relationships
Solving project problems and find highly-productive developers for data science projects
Forecasting current and future home sales prices with Zillow
Using the increasingly popular Jupyter Notebook to create and share documents integrating live code, equations, visualizations, and text
And much more Show less

As the first technical employee and CTO of Sqor Sports, Noah Gift discovers how social media influences the NBA and vice versa. As a result, Sqor Sports has been able to grow to millions of monthly active users by leveraging influencers that were found by machine learning algorithms.

Noah shares his research and lessons learned on how social media and the NBA intersect and explains how Sqor Sports uses data science and machine learning to determine NBA team valuation and attendance as… Show more

As the first technical employee and CTO of Sqor Sports, Noah Gift discovers how social media influences the NBA and vice versa. As a result, Sqor Sports has been able to grow to millions of monthly active users by leveraging influencers that were found by machine learning algorithms.

Noah shares his research and lessons learned on how social media and the NBA intersect and explains how Sqor Sports uses data science and machine learning to determine NBA team valuation and attendance as well as individual player performance. You’ll learn how to recreate this research, working with a set of shared Juypter notebooks.

Topics include:

What drives the valuation of teams (attendance, the local real estate market, etc.)?
Does winning bring more fans to games?
Does salary correlate with social media performance?
For more information, check out “Social power, influence, and performance in the NBA: Part 1” and “Part 2” from IBM developerWorks. Show less

The first section is an intentionally brief, functional, data science centric introduction to Python. The assumption is a someone with zero experience in programming can follow this tutorial and learn Python with the smallest amount of information possible.

The sections after that, involve varying levels of difficulty and cover topics as diverse as Machine Learning and Linear Optimization to build systems and commandline tools.

In Part 1 of this series, you created the basic structure of a data science project and downloaded the data programmatically from GitHub, transforming it to be statistically analyzed with pandas. Here in Part 2, you use Jupyter Notebook to explore many aspects of a software project and learn how to deploy the project to the Python Package index, both as a library and a command line tool.

This series covers two problems: how to use data science to investigate project management around software engineering, and how to publish a data science tool to the Python Package Index.
Data science as a discipline is exploding, and many articles discuss the ins and outs of such topics as which algorithm to use. However, only a few explain how to collect data, create a project structure, and ultimately publish your software to the Python Package Index. This tutorial provides detailed… Show more

This series covers two problems: how to use data science to investigate project management around software engineering, and how to publish a data science tool to the Python Package Index.
Data science as a discipline is exploding, and many articles discuss the ins and outs of such topics as which algorithm to use. However, only a few explain how to collect data, create a project structure, and ultimately publish your software to the Python Package Index. This tutorial provides detailed hands-on instructions on both topics. The source code for this series is available on GitHub. Show less

Social power, influence, and performance in the NBA, Part 2
Exploring the individual NBA players

Explore valuation and attendance using data science and machine learning

Hidden in the discussion of rising energy costs and consumption in datacenters is the selection of software language.

"The cloud, and especially Amazon, has raised the bar for systems administration and large-scale systems architecture. In this talk I will go through how we have used Erlang's location transparency features to manage clusters of machines on using EC2 and rightscale."

To explain how Erlang fits into game and web development in games.

Putting data science to work

Build a scalable architecture in the cloud
Combining Pyomo, RabbitMQ, and Tornado

Explore ways to model optimization applications in Python using Python Optimization Modeling Objects (Pyomo), an open source tool. You can use Pyomo to define symbolic problems, create concrete problem instances, and solve these instances with standard solvers. This article series shows how to leverage Pyomo's ability to integrate with Python to model optimization applications. This first article covers the basics. Part 2 shows how to add more tools and build a scalable architecture. Part 3… Show more

Explore ways to model optimization applications in Python using Python Optimization Modeling Objects (Pyomo), an open source tool. You can use Pyomo to define symbolic problems, create concrete problem instances, and solve these instances with standard solvers. This article series shows how to leverage Pyomo's ability to integrate with Python to model optimization applications. This first article covers the basics. Part 2 shows how to add more tools and build a scalable architecture. Part 3 presents some hands-on examples of investment analysis and statistical analysis using IPython and pandas. Show less

Erlang is destined for great things in the age of multi-core, due to its unique process loving architecture and functional nature. In this article, you can learn the basics about programming in Erlang.

Given an arbitrary amount of change, say 1.34, determine the correct amount of change to give using a greedy match, which uses the highest coins first. With US coins, 25,10, 5,1, greedy match will lead to the lowest coins possible.

Explore Node.js, an event-driven I/O framework for the version 8 JavaScript™ engine on UNIX®-like platforms designed for writing scalable network programs such as web servers. This article examines the framework, the ecosystem surrounding it (including cloud offerings), and wraps up with a comprehensive example of how to build a chat server in Node.js.

In this article, I hope to share some of my excitement about F#, Windows Azure and MapReduce. I’ll pull together all of the ideas to show how you can use F# and the MapReduce algorithm to parse log files on Windows Azure. First, I’ll cover some prototyping techniques to make MapReduce programming less complex; then, I’ll take the results … to the cloud.

AMQP is a powerful way to enable cross language and process messaging. In the AMQP architecture publisher clients send asynchronous messages to Brokers, using the AMQP protocol.

A talk on how PowerShell has changed the way command-line tools need to be written on Windows. Strings don't cut it anymore.

A five minute talk on why marathons are the new golf.

Find patterns in multitudes of cloud business analytics data.

IBM Developerworks Podcast on Business Analytics

At times, you need to be able to access more physical and virtual resources to achieve complex compute-intensive results, but setting up a grid system within an organization can face resource, logistical, and technical hurdles; even some political ones. Cloud computing comes to the rescue in this case. It also combines perfectly with the MapReduce function for handling lots of Big Data computations by making it both transparent and irrelevant where two numbers get added together. The author… Show more

At times, you need to be able to access more physical and virtual resources to achieve complex compute-intensive results, but setting up a grid system within an organization can face resource, logistical, and technical hurdles; even some political ones. Cloud computing comes to the rescue in this case. It also combines perfectly with the MapReduce function for handling lots of Big Data computations by making it both transparent and irrelevant where two numbers get added together. The author demonstrates why cloud computing and MapReduce are helpful in solving Big Data problems. Show less

Catastrophically bad code can be written in any language, including the elegant and powerful Python language. In this article, we explore how thinking about testing actually produces dramatically different Python code. Lastly, we learn how to measure scientifically the difference.

Porting legacy Perl to Python can be a daunting task. In this article, learn some of the theory behind dealing with legacy code, including what not to do.

The days of cowboy coding are long gone at most organizations, replaced by a renewed interest in generating quality software. Continuous integration (CI) testing is a vital component in the practice of agile programming techniques that lead to high-quality software. Learn the theory and practice of CI testing by exploring Buildbot, an open source CI system written in Python.

Learn to scale your UNIX® Python applications to multiple cores by using the multiprocessing module which is built into Python 2.6. Multiprocessing mimics parts of the threading API in Python to give the developer a high level of control over flocks of processes, but also incorporates many additional features unique to processes.

If you are entering into the cloud, testing becomes even more critical for your applications to be reliable. Learn to master automated, functional testing using the open source tools, Selenium, Windmill, and twill. The techniques covered in this article work on Google App Engine, blogging software, or your own home grown application.

Cloud computing and software development for handheld devices are two very hot technologies that are increasingly being combined to create hybrid solutions. With this article, learn how to connect Google App Engine, Google's cloud computing offering, with the iPhone, Apple's mobile platform. You'll also see how to use the open source library, TouchEngine, to dynamically control application data on the iPhone by connecting to the App Engine cloud and caching that data for offline use.

At some point in their careers, most systems administrators need to interact with an LDAP server. This article shows how LDAP can be used for Apache authentication, as well as how to perform CRUD, or Create, Read, Update, and Delete operations on an OpenLDAP database, using the Python module python-ldap.

The dd command is one of those ancient UNIX tools that is extremely powerful, yet at the same time, the syntax can make it feel slightly archaic. A lot of seasoned sysadmins and developers still remember the first time they saw the dd command used by a bearded wizard. He might have used it to test the disk I/O, capture a disk image, or restore it.

Learn how to extend your Python command-line tools by writing plug-ins.

SQLAlchemy is a next-generation Python Object Relational mapper. Learn how to use the new 0.5 API, work with third-party components, and a build a basic Web application.

Do a little spring cleaning, and solve your network problems with open source software. Setting up a DHCP server with Red Hat Enterprise Linux 5 or Fedora is a piece of cake. In this article we’ll go over the basics of setting up DHCP, doing basic troubleshooting, and finally setting up static mapping DHCP.

Python is an ideal language for solving problems, especially for Linux and Unix. With this pragmatic book, administrators can review various tasks that often occur in the management of these systems, and learn how Python can provide a more efficient way to handle them. Once you finish this book, you'll be able to develop your own set of command-line utilities with Python to tackle a wide range of problems.

Often it is difficult to make the transition from procedural scripting to object-oriented programming. This article explores how to reuse knowledge from PHP, Bash, or Python scripting to transition to object-oriented programming in Python. The article also briefly touches on the appropriate use of functional programming.

An article on "Understanding, Configuring, and Customizing SNMP on OS X Leopard" that might change the way you think about your Mac.

Threaded programming in Python can be done with a minimal amount of complexity by combining threads with Queues. This article explores using threads and queues together to create simple yet effective patterns for solving problems that require concurrency.

Configuring, using, and creating Run Levels on Red Hat systems.

First "unofficial" article ever published on Google App Engine.

Relational databases are not the only solution available for Python programmers in the enterprise. Often an object database can be a more natural fit for solving certain problems. This article discusses ZODB, a scalable and redundant object database that specializes in storing extensible objects, without the natural Object-relational impedance mismatch that can occur by attempting to make an Object Oriented Language and a Relational Query System map objects to relations.

Noah Gift and Jeremy Jones interview Mark Shuttleworth.

If you work in IT, as a UNIX® Sysadmin, a software developer, or even a manager, there a few skills that will set you apart from the crowd. Do you fully understand the OSI model? Are you comfortable with subnetting? Do you understand UNIX permissions? Let me add to this list the humble command line tool. By the end of this article, anyone involved in IT at any capacity should be able to create at least a simple command line tool.

An article introducing Python to Bash scripters.

Configuring Your Mac To Work With Linux Samba and LDAP Servers

An article, in a series, on "Configuring Your Mac To Work With Linux Samba and LDAP Servers."

Configuring Your Mac To Work With Linux Samba and LDAP Servers

Data centers and production facilities are embracing Simple Network Management Protocol (SNMP) as a way to get a handle on dense and complex infrastructures. The Net-SNMP library now has Python™ bindings, and it is an excellent choice to write custom code to manage a data center or supplement full-blown Network Management Systems (NMS). Due to the complexity of SNMP, using an interactive tool like IPython can make all the difference. In this article, learn how to use Net-SNMP, Python, and the… Show more

Data centers and production facilities are embracing Simple Network Management Protocol (SNMP) as a way to get a handle on dense and complex infrastructures. The Net-SNMP library now has Python™ bindings, and it is an excellent choice to write custom code to manage a data center or supplement full-blown Network Management Systems (NMS). Due to the complexity of SNMP, using an interactive tool like IPython can make all the difference. In this article, learn how to use Net-SNMP, Python, and the IPython shell to interactively explore and manage a network. Show less

This article will show a pragmatic implementation of SSH port forwarding by demonstrating how to use configuration files and conditional statements to create permanent, yet dynamic, SSH configurations for your home, office, and any virtual machines you may have on your systems.

By and large, most Red Hat Linux systems will have Bash as the default shell. Bash is a darn great shell, but this article is about another equally great shell, called Z-Shell, that has most of the attributes of Bash, but in some cases goes the extra mile to give you the flexibility to customize your shell more than Bash allows.

First technical article published on Open Directory. Includes instructions on using Open Directory with Mac, Linux, and Windows shared home directories.

Unless you have been living in an underground bunker, you have probably heard of Version Control, and possibly even Subversion (SVN). If you want to get the latest source code to compile it yourself, contribute to an open source project, keep track of files and documents, or work on the same document tree with a team of people, then you will need to use SVN.

First technical article published on Open Directory. Includes instructions on using Open Directory with Mac, Linux, and Windows shared home directories.

Configuring ACLS in Squid, a proxy server.

First technical article published on Open Directory. Includes instructions on using Open Directory with Mac, Linux, and Windows shared home directories.

Article on how to dual boot RHEL 5 on a MacBook Pro laptop.

An article about using Applescript and Python via an bridge library called appscript.

Configuring a Squid proxy server in 5 minutes.

Working with O'Reilly and AWS to write a book on building solutions on AWS with C#/.NET 6.

Some of the many benefits of programming with AWS Lambda in Python include no servers to manage, continuous scaling, and subsecond metering. Several use cases include data processing, stream processing, IoT backends, mobile, and web applications. Learn to take advantage of a new paradigm in software architecture that will make your code easier to write, maintain, and deploy.

AWS Lambda functions are the building blocks for creating sophisticated applications and services on AWS. In this… Show more

Some of the many benefits of programming with AWS Lambda in Python include no servers to manage, continuous scaling, and subsecond metering. Several use cases include data processing, stream processing, IoT backends, mobile, and web applications. Learn to take advantage of a new paradigm in software architecture that will make your code easier to write, maintain, and deploy.

AWS Lambda functions are the building blocks for creating sophisticated applications and services on AWS. In this LiveLesson, you learn to use Python to develop Lambda functions that communicate with key AWS services: API Gateway, SQS, and CloudWatch functions. You also learn how a new cloud-based development environment, Cloud9, can streamline writing, debugging, and deploying AWS Lambda functions. Show less

Data Engineering with Python and AWS Lambda LiveLessons shows users how to build complete and powerful data engineering pipelines in the same language that Data Scientists use to build Machine Learning models. By embracing serverless data engineering in Python, you can build highly scalable distributed systems on the back of the AWS backplane. Users learn to think in the new paradigm of serverless, which means to embrace events and event-driven programs that replace expensive and complicated… Show more

Data Engineering with Python and AWS Lambda LiveLessons shows users how to build complete and powerful data engineering pipelines in the same language that Data Scientists use to build Machine Learning models. By embracing serverless data engineering in Python, you can build highly scalable distributed systems on the back of the AWS backplane. Users learn to think in the new paradigm of serverless, which means to embrace events and event-driven programs that replace expensive and complicated servers. Show less

Getting your models into production is the fundamental challenge of machine learning. MLOps offers a set of proven principles aimed at solving this problem in a reliable and automated way. This insightful guide takes you through what MLOps is (and how it differs from DevOps) and shows you how to put it into practice to operationalize your machine learning models.

Current and aspiring machine learning engineers--or anyone familiar with data science and Python--will build a foundation in… Show more

Getting your models into production is the fundamental challenge of machine learning. MLOps offers a set of proven principles aimed at solving this problem in a reliable and automated way. This insightful guide takes you through what MLOps is (and how it differs from DevOps) and shows you how to put it into practice to operationalize your machine learning models.

Current and aspiring machine learning engineers--or anyone familiar with data science and Python--will build a foundation in MLOps tools and methods (along with AutoML and monitoring and logging), then learn how to implement them in AWS, Microsoft Azure, and Google Cloud. The faster you deliver a machine learning system that works, the faster you can focus on the business problems you're trying to crack. This book gives you a head start.

You'll discover how to:

Apply DevOps best practices to machine learning
Build production machine learning systems and maintain them
Monitor, instrument, load-test, and operationalize machine learning systems
Choose the correct MLOps tools for a given machine learning task
Run machine learning models on a variety of platforms and devices, including mobile phones and specialized hardware Show less

Machine Learning is the scientific study of models and algorithms that train a computer to make predictions without explicit instruction. Machine Learning is a subset of Artificial Intelligence, which can be defined as computers that mimic human problem-solving. This video demonstrates the core principles of Machine Learning and AI, including supervised Machine Learning, unsupervised Machine Learning, neural networks, and social network theory.

Learn to master the foundational concepts… Show more

Machine Learning is the scientific study of models and algorithms that train a computer to make predictions without explicit instruction. Machine Learning is a subset of Artificial Intelligence, which can be defined as computers that mimic human problem-solving. This video demonstrates the core principles of Machine Learning and AI, including supervised Machine Learning, unsupervised Machine Learning, neural networks, and social network theory.

Learn to master the foundational concepts of AI and Machine Learning. The LiveLessons video starts with an overview of Artificial Intelligence and covers applications of AI across industries and opportunities in AI for individuals, organizations, and ecosystems. It also covers the difference between narrow, general, and super AI. Show less