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Applied Machine Learning and High-Performance Computing on AWS
Applied Machine Learning and High-Performance Computing on AWS

Applied Machine Learning and High-Performance Computing on AWS: Accelerate the development of machine learning applications following architectural best practices

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Profile Icon Mani Khanuja Profile Icon Trenton Potgieter Profile Icon Shreyas Subramanian Profile Icon Farooq Sabir
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Full star icon Full star icon Full star icon Full star icon Half star icon 4.7 (11 Ratings)
Paperback Dec 2022 382 pages 1st Edition
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Arrow left icon
Profile Icon Mani Khanuja Profile Icon Trenton Potgieter Profile Icon Shreyas Subramanian Profile Icon Farooq Sabir
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$46.99
Full star icon Full star icon Full star icon Full star icon Half star icon 4.7 (11 Ratings)
Paperback Dec 2022 382 pages 1st Edition
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Applied Machine Learning and High-Performance Computing on AWS

High-Performance Computing Fundamentals

High-Performance Computing (HPC) impacts every aspect of your life, from your morning coffee to driving a car to get to the office, knowing the weather forecast, your vaccinations, movies that you watch, flights that you take, games that you play, and many other aspects. Many of our actions leave a digital footprint, leading to the generation of massive amounts of data. In order to process such data, we need a large amount of processing power. HPC, also known as accelerated computing, aggregates the computing power from a cluster of nodes and divides the work among various interconnected processors, to achieve much higher performance than could be achieved by using a single computer or machine, as shown in Figure 1.1. This helps in solving complex scientific and engineering problems, in critical business applications such as drug discovery, flight simulations, supply chain optimization, financial risk analysis, and so on:

Figure 1.1 – HPC

Figure 1.1 – HPC

For example, drug discovery is a data-intensive process, which involves computationally heavy calculations to simulate how virus protein binds with human protein. This is an extremely expensive process and may take weeks or months to finish. With the unification of Machine Learning (ML) with accelerated computing, researchers can simulate drug interactions with protein with higher speed and accuracy. This leads to faster experimentation and significantly reduces the time to market.

In this chapter, we will learn the fundamentals and importance of HPC, followed by technological advancements in the area. We will understand the constraints and how developers can benefit from the elasticity of the cloud while still optimizing costs to innovate faster to gain a competitive business advantage.

In this chapter, we will cover the following topics:

  • Why do we need HPC?
  • Limitations of on-premises HPC
  • Benefits of doing HPC on the cloud
  • Driving innovation across industries with HPC

Why do we need HPC?

According to Statista, the rate of growth of data globally is forecast to increase rapidly and reached 64.2 zettabytes in 2020. By 2025, the volume of data is estimated to grow to more than 180 zettabytes. Due to the COVID-19 pandemic, data growth in 2020 reached a new high as more people were learning online and working remotely from home. As data is continuously increasing, the need to be able to analyze and process it also increases. This is where HPC is a useful mechanism. It helps organizations to think beyond their existing capabilities and explore possibilities with advanced computing technologies. Today HPC applications, which were once confined to large enterprises and academia, are trending across a wide range of industries. Some of these industries include material sciences, manufacturing, product quality improvement, genomics, numerical optimization, computational fluid dynamics, and many more. The list of applications for HPC will continue to increase, as cloud infrastructure is making it accessible to more organizations irrespective of their size, while still optimizing cost, helping to innovate faster and gain a competitive advantage.

Before we take a deeper look into doing HPC on the cloud, let’s understand the limitations of running HPC applications on-premises, and how we can overcome them by using specialized HPC services provided by the cloud.

Limitations of on-premises HPC

HPC applications are often based on complex models trained on a large amount of data, which require high-performing hardware such as Graphical Processing Units (GPUs) and software for distributing the workload among different machines. Some applications may need parallel processing while others may require low-latency and high-throughput networking. Similarly, applications such as gaming and video analysis may need performance acceleration using a fast input or output subsystem and GPUs. Catering to all of the different types of HPC applications on-premises might be daunting in terms of cost and maintenance.

Some of the well-known challenges include, but are not limited to, the following:

  • High upfront capital investment
  • Long procurement cycles
  • Maintaining the infrastructure over its life cycle
  • Technology refreshes
  • Forecasting the annual budget and capacity requirement

Due to the above-mentioned constraints, planning for an HPC system can be a grueling process, Return On Investment (ROI) for which might be difficult to justify. This can be a barrier to innovation, with slow growth, reduced efficiency, lost opportunities, and limited scalability and elasticity. Let’s understand the impact of each of these in detail.

Barrier to innovation

The constraints of on-premises infrastructure can limit the system design, which will be more focused on the availability of the hardware instead of the business use case. You might not consider some new ideas if they are not supported by the existing infrastructure, thus obstructing your creativity and hindering innovation within the organization.

Reduced efficiency

Once you finish developing the various components of the system, you might have to wait in long prioritized queues to test your jobs, which might take weeks, even if it takes only a few hours to run. On-premises infrastructure is designed to capitalize on the utilization of expensive hardware, often resulting in very convoluted policies for prioritizing the execution of jobs, thus decreasing your productivity and ability to innovate.

Lost opportunities

In order to take full advantage of the latest technology, organizations have to refresh their hardware. Earlier, the typical refresh cycle of three years was enough to stay current, to meet the demands of HPC workloads. However, due to fast technological advancements and a faster pace of innovation, organizations need to refresh their infrastructure more often, otherwise, it might have a larger downstream business impact in terms of revenue. For example, technologies such as Artificial Intelligence (AI), ML, data visualization, risk analysis of financial markets, and so on, are pushing the limits of on-premises infrastructure. Moreover, due to the advent of the cloud, a lot of these technologies are cloud native, and deliver higher performance on large datasets when running in the cloud, especially with workloads that use transient data.

Limited scalability and elasticity

HPC applications rely heavily on infrastructure elements such as containers, GPUs, and serverless technologies, which are not readily available in an on-premises environment, and often have a long procurement and budget approval process. Moreover, maintaining these environments, making sure they are fully utilized, and even upgrading the OS or software packages, requires skills and dedicated resources. Deploying different types of HPC applications on the same hardware is very limiting in terms of scalability and flexibility and does not provide you with the right tools for the job.

Now that we understand the limitations of doing HPC on-premises, let’s see how we can overcome them by running HPC workloads on the cloud.

Benefits of doing HPC on the cloud

With virtually unlimited capacity on the cloud, you can move beyond the constraints of on-premises HPC. You can reimagine new approaches based on the business use case, experiment faster, and gain insights from large amounts of data, without the need for costly on-premises upgrades and long procurement cycles. You can run complex simulations and deep learning models in the cloud and quickly move from idea to market using scalable compute capacity, high-performance storage, and high-throughput networking. In summary, it enables you to drive innovation, collaborate among distributed teams, improve operational efficiency, and optimize performance and cost. Let’s take a deeper look into each of these benefits.

Drives innovation

Moving HPC workloads to the cloud, helps you break barriers to innovation, and opens the door for unlimited possibilities. You can quickly fail forward, try out thousands of experiments, and make business decisions based on data. The benefit that I really like is that, once you solve the problem, it remains solved and you don’t have to revisit it after a system upgrade or a technology refresh. It eliminates reworking and the maintenance of hardware, lets you focus on the business use case, and enables you to quickly design, develop, and test new products. The elasticity offered by the cloud, allows you to grow and shrink the infrastructure as per the requirements. Additionally, cloud-based services offer native features, which remove the heavy lifting and let you adopt tested and verified HPC applications, without having to write and manage all the utility libraries on your own.

Enables secure collaboration among distributed teams

HPC workloads on the cloud allow you to share designs, data, visualizations, and other artifacts globally with your teams, without the need to duplicate or proliferate sensitive data. For example, building a digital twin (a real-time digital counterpart of a physical object) can help in predictive maintenance. It can get the state of the object in real time and it monitors and diagnoses the object (asset) to optimize its performance and utilization. To build a digital twin, a cross-team skill set is needed, which might be remotely located to capture data from various IoT sensors, performing extensive what-if analysis and meticulously building a simulation model to develop an accurate representation of the physical object. The cloud provides a collaboration platform, where different teams can interact with a simulation model in near real time, without moving or copying data to different locations, and ensures compliance with rapidly changing industry regulations. Moreover, you can use native features and services offered by the cloud, for example, AWS IoT TwinMaker, which can use the existing data from multiple sources, create virtual replicas of physical systems, and combine 3D models to give you a holistic view of your operations faster and with less effort. With a broad global presence of HPC technologies on the cloud, it allows you to work together with your remote teams across different geographies without trading off security and cost.

Amplifies operational efficiency

Operational efficiency means that you are able to support the development and execution of workloads, gain insights, and continuously improve the processes that are supporting your applications. The design principles and best practices include automating processes, making frequent and reversible changes, refining your operations frequently, and being able to anticipate and recover from failures. Having your HPC applications on the cloud enables you to do that, as you can version control your infrastructure as code, similar to your application code, and integrate it with your Continuous Integration and Continuous Delivery (CI/CD) pipelines. Additionally, with on-demand access to unlimited compute capacity, you will no longer have to wait in long queues for your jobs to run. You can skip the wait and focus on solving business critical problems, providing you with the right tools for the right job.

Optimizes performance

Performance optimization involves the ability to use resources efficiently and to be able to maintain them as the application changes or evolves. Some of the best practices include making the implementation easier for your team, using serverless architectures where possible, and being able to experiment faster. For example, developing ML models and integrating them into your application requires special expertise, which can be alleviated by using out-of-the-box models provided by cloud vendors, such as services in the AI and ML stack by AWS. Moreover, you can leverage the compute, storage, and networking services specially designed for HPC and eliminate long procurement cycles for specialized hardware. You can quickly carry out benchmarking or load testing and use that data to optimize your workloads without worrying about cost, as you only pay for the amount of time you are using the resources on the cloud. We will understand this concept more in Chapters 5, Data Analysis, and Chapter 6, Distributed Training of Machine Learning Models.

Optimizes cost

Cost optimization is a continuous process of monitoring and improving resource utilization over an application’s life cycle. By adopting the pay-as-you-go consumption model and increasing or decreasing the usage depending on the business needs, you can achieve potential cost savings. You can quickly commission and decommission HPC clusters in minutes, instead of days or weeks. This lets you gain access to resources rapidly, as and when needed. You can measure the overall efficiency by calculating the business value achieved and the cost of delivery. With this data, you can make informed decisions as well as understanding the gains from increasing the application’s functionality and reducing cost.

Running HPC in the cloud helps you overcome the limitations associated with traditional on-premises infrastructure: fixed capacity, long procurement cycles, technology obsolescence, high upfront capital investment, maintaining the hardware, and applying regular Operating System (OS) and software updates. The cloud gives you unlimited HPC capacity virtually, with the latest technology to promote innovation, which helps you design your architecture based on business needs instead of available hardware, minimizes the need for job queues, and improves operational and performance efficiency while still optimizing cost.

Next, let’s see how different industries such as Autonomous Vehicles (AVs), manufacturing, media and entertainment, life sciences, and financial services are driving innovation with HPC workloads.

Driving innovation across industries with HPC

Every industry and type of HPC application poses different kinds of challenges. The HPC solutions provided by cloud vendors such as AWS help all companies, irrespective of their size, which leads to emerging HPC applications such as reinforcement learning, digital twins, supply chain optimization, and AVs.

Let’s take a look at some of the use cases in life sciences and healthcare, AV, and supply chain optimization.

Life sciences and healthcare

In life sciences and the healthcare domain, a large amount of sensitive and meaningful data is captured almost every minute of the day. Using HPC technology, we can harness this data to gain meaningful insights into critical diseases to save lives by reducing the time taken testing lab samples, drug discovery, and much more, as well as meeting the core security and compliance requirements.

The following are some of the emerging applications in the healthcare and life sciences domain.

Genomics

You can use cloud services provided by AWS to store and share genomic data securely, which helps you build and run predictive or real-time applications to accelerate the journey from genomic data to genomic insights. This helps to reduce data processing times significantly and perform casual analysis of critical diseases such as cancer and Alzheimer’s.

Imaging

Using computer vision and data integration services, you can elevate image analysis and facilitate long-term data retention. For example, by using ML to analyze MRI or X-ray scans, radiology companies can improve operational efficiency and quickly generate lab reports for their patients. Some of the technologies provided by AWS for imaging include Amazon EC2 GPU instances, AWS Batch, AWS ParallelCluster, AWS DataSync, and Amazon SageMaker, which we will discuss in detail in subsequent chapters.

Computational chemistry and structure-based drug design

Combining the state-of-the-art deep learning models for protein classification, advancements in protein structure solutions, and algorithms for describing 3D molecular models with HPC computing resources, allows you to grow and reduce the time to market drastically. For example, in a project performed by Novartis on the AWS cloud, where they were able to screen 10 million compounds against a common cancer target in less than a week, based on their internal calculations, if they had performed a similar experiment in-house, then it would have resulted in about a $40 million investment. By running this experiment on the cloud using AWS services and features, they were able to use their 39 years of computational chemistry data and knowledge. Moreover, it only took 9 hours and $4,232 to conduct the experiment, hence increasing their pace of innovation and experimentation. They were able to successfully identify three out of the ten million compounds, that were screened.

Now that we understand some of the applications in the life sciences and healthcare domain, let us discuss how the automobile and transport industry is using HPC for building AVs.

AVs

The advancement of deep learning models such as reinforcement learning, object detection, and image segmentation, as well technological advancements in compute technology, and deploying models on edge devices, have paved the way for AV. In order to design and build an AV, all the components of the system have to work in tandem, including planning, perception, and control systems. It also requires collecting and processing massive amounts of data and using it to create a feedback loop, for vehicles to adjust their state based on the changing condition of the traffic on roads in real time. This entails having high I/O performance, networking, specialized hardware coprocessors such as GPUs or Field Programmable Gate Arrays (FPGAs), as well as analytics and deep learning frameworks. Moreover, before an AV can even start testing on actual roads, it has to undergo millions of miles of simulation to demonstrate safety performance, due to the high dimensionality of the environment, which is complex and time-consuming. By using the AWS cloud’s virtually unlimited compute and storage capacity, support for advanced deep learning frameworks, and purpose-built services, you can drive a faster time to market. For example, In 2017, Lyft, an American transportation company, launched its AV division. To enhance the performance and safety of its system, it uses petabytes of data collected from its AV fleet to execute millions of simulations every year, which involves a lot of compute power. To run these simulations at a lower cost, they decided to take advantage of unused compute capacity on the AWS cloud, by using Amazon EC2 Spot Instances, which also helped them to increase their capacity to run the simulations at this magnitude.

Next, let us understand supply chain optimization and its processes!

Supply chain optimization

Supply chains are worldwide networks of manufacturers, distributors, suppliers, logistics, and e-commerce retailers that work together to get products from the factory to the customer’s door without delays or damage. By enabling information flow through these networks, you can automate decisions without any human intervention. The key attributes to consider are real-time inventory forecasts, end-to-end visibility, and the ability to track and trace the entire production process with unparalleled efficiency. Your teams will no longer have to handle the minuscular details associated with supply chain decisions. With automation and ML you can resolve bottlenecks in product movement. For example, in the event of a pandemic or natural disaster, you can quickly divert goods to alternative shipping routes without affecting their on-time delivery.

Here are some examples of using ML to improve supply chain processes:

  • Demand Forecasting: You can combine a time series with additional correlated data, such as holidays, weather, and demographic events, and use deep learning models such as DeepAR to get more accurate results. This will help you to meet variable demand and avoid over-provisioning.
  • Inventory Management: You can automate inventory management using ML models to determine stock levels and reduce costs by preventing excess inventory. Moreover, you can use ML models for anomaly detection in your supply chain processes, which can help you in optimizing inventory management, and deflect potential issues more proactively, for example, by transferring stock to the right location using optimized routing ahead of time.
  • Boost Efficiency with Automated Product Quality Inspection: By using computer vision models, you can identify product defects faster with improved consistency and accuracy at an early stage so that customers receive high-quality products in a timely fashion. This reduces the number of customer returns and insurance claims that are filed due to issues in product quality, thus saving costs and time.

All the components of supply chain optimization discussed above need to work together as part of the workflow and therefore require low latency and high throughput in order to meet the goal of delivering an optimal quality product to a customer’s doorstep in a timely fashion. Using cloud services to build the workflow provides you with greater elasticity and scalability at an optimized cost. Moreover, with native purpose-built services, you can eliminate the heavy lifting and reduce the time to market.

Summary

In this chapter, we started by understanding HPC fundamentals and its importance in processing massive amounts of data to gain meaningful insights. We then discussed the limitations of running HPC workloads on-premises, as different types of HPC applications will have different hardware and software requirements, which becomes time-consuming and costly to procure in-house. Moreover, it will hinder innovation as developers and engineers are limited to the availability of resources instead of the application requirements. Then, we talked about how having HPC workloads on the cloud can help in overcoming these limitations and foster collaboration across global teams, break barriers to innovation, improve architecture design, and optimize performance and cost. Cloud infrastructure has made the specialized hardware needed for HPC applications more accessible, which has led to innovation in this space across a wide range of industries. Therefore, in the last section, we discussed some emerging workloads in HPC, such as in life sciences and healthcare, supply chain optimization, and AVs, along with real-world examples.

In the next chapter, we will dive into data management and transfer, which is the first step to running HPC workloads on the cloud.

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Key benefits

  • Understand the need for high-performance computing (HPC)
  • Build, train, and deploy large ML models with billions of parameters using Amazon SageMaker
  • Learn best practices and architectures for implementing ML at scale using HPC

Description

Machine learning (ML) and high-performance computing (HPC) on AWS run compute-intensive workloads across industries and emerging applications. Its use cases can be linked to various verticals, such as computational fluid dynamics (CFD), genomics, and autonomous vehicles. This book provides end-to-end guidance, starting with HPC concepts for storage and networking. It then progresses to working examples on how to process large datasets using SageMaker Studio and EMR. Next, you’ll learn how to build, train, and deploy large models using distributed training. Later chapters also guide you through deploying models to edge devices using SageMaker and IoT Greengrass, and performance optimization of ML models, for low latency use cases. By the end of this book, you’ll be able to build, train, and deploy your own large-scale ML application, using HPC on AWS, following industry best practices and addressing the key pain points encountered in the application life cycle.

Who is this book for?

The book begins with HPC concepts, however, it expects you to have prior machine learning knowledge. This book is for ML engineers and data scientists interested in learning advanced topics on using large datasets for training large models using distributed training concepts on AWS, deploying models at scale, and performance optimization for low latency use cases. Practitioners in fields such as numerical optimization, computation fluid dynamics, autonomous vehicles, and genomics, who require HPC for applying ML models to applications at scale will also find the book useful.

What you will learn

  • Explore data management, storage, and fast networking for HPC applications
  • Focus on the analysis and visualization of a large volume of data using Spark
  • Train visual transformer models using SageMaker distributed training
  • Deploy and manage ML models at scale on the cloud and at the edge
  • Get to grips with performance optimization of ML models for low latency workloads
  • Apply HPC to industry domains such as CFD, genomics, AV, and optimization
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Table of Contents

19 Chapters
Part 1: Introducing High-Performance Computing Chevron down icon Chevron up icon
Chapter 1: High-Performance Computing Fundamentals Chevron down icon Chevron up icon
Chapter 2: Data Management and Transfer Chevron down icon Chevron up icon
Chapter 3: Compute and Networking Chevron down icon Chevron up icon
Chapter 4: Data Storage Chevron down icon Chevron up icon
Part 2: Applied Modeling Chevron down icon Chevron up icon
Chapter 5: Data Analysis Chevron down icon Chevron up icon
Chapter 6: Distributed Training of Machine Learning Models Chevron down icon Chevron up icon
Chapter 7: Deploying Machine Learning Models at Scale Chevron down icon Chevron up icon
Chapter 8: Optimizing and Managing Machine Learning Models for Edge Deployment Chevron down icon Chevron up icon
Chapter 9: Performance Optimization for Real-Time Inference Chevron down icon Chevron up icon
Chapter 10: Data Visualization Chevron down icon Chevron up icon
Part 3: Driving Innovation Across Industries Chevron down icon Chevron up icon
Chapter 11: Computational Fluid Dynamics Chevron down icon Chevron up icon
Chapter 12: Genomics Chevron down icon Chevron up icon
Chapter 13: Autonomous Vehicles Chevron down icon Chevron up icon
Chapter 14: Numerical Optimization Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

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tt0507 Mar 18, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This is a wonderful and very practical guide to applying machine learning (ML) on AWS. The book details important high-performance computing (HPC) concepts such as computing, networking, and data storage. The book also provides detailed explanations and examples of data analysis, distributed training, model deployment and optimization, and data visualization. The book acts as a great resource for anyone who uses AWS for ML applications. It dives deep into several components of AWS and provides detailed instructions and examples on how to use develop ML applications on AWS as a whole instead of just using several services.
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ojo123 Feb 08, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I was asked to review the "Applied Machine Learning on AWS" text and here is my review. The book provides in-depth coverage of the AWS platform and how ML applications can be built and run on it. It discusses data storage on AWS using Amazon Simple Storage Service (S3) and AWS Lake Formation with the pros and cons of each storage service. It demonstrates how to load and preprocess data using Pyspark and Pandas Dataframe on AWS. It explains how to build models using SageMaker's built-in PyTorch and then goes on to explain how to perform distributed training of machine learning models on AWS to speed up the training process. Lastly, it explains how to deploy machine models on AWS. The book is a great addition for anyone interested in building and running machine on the cloud, especially on the AWS platform
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Amazon Customer Jan 10, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Recommended reading, to understand HPC applications, using Amazon SageMaker, which is an IDE to build, train and deploy machine learning applications.
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Deep P. Mar 31, 2023
Full star icon Full star icon Full star icon Full star icon Full star icon 5
The book is intended for developers and data scientists who want to take advantage of the powerful tools and services offered by AWS to develop, train, and deploy machine learning models.The author provides a step-by-step approach to building machine learning models on AWS, covering all types of variety of options available as tools on AWS for developing the highly scalable architecture for the high-end ML application. The book also covers essential topics such as selecting the appropriate Data Storage, transforming, and handling large datasets.One of the strengths of this book is its focus on high-performance computing. The author shows how to use AWS's powerful computing resources, including GPUs, to train machine learning models at scale. The book includes detailed examples of how to use AWS services such as Amazon SageMaker, Amazon EC2, and Amazon S3 to build and deploy machine learning models.Also, Covering the key concepts of AWS provides for High-Performance Computing optimization, Scaling, and different types of training such as Distributed and also Deployment to the edge devices and maintaining them.Overall, "Applied Machine Learning and High-Performance Computing on AWS" is an excellent resource for developers and data scientists who want to leverage the power of AWS to build and deploy machine learning models. It covers various topics and provides practical guidance that readers can apply to real-world projects. I highly recommend this book to anyone interested in applied machine learning on AWS.
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Parth Acharya Oct 08, 2023
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The book "Applied Machine Learning and High-Performance Computing on AWS" provides a comprehensive guide to building, training, and deploying large-scale machine learning models on AWS, covering topics like HPC fundamentals, distributed training, deployment at scale, optimizing models, and applying ML to domains like CFD and genomics.Ideal for data scientists new to AWS or looking to leverage AWS for ML, it gives practical guidance and detailed examples using services like SageMaker and EC2 to build real-world ML solutions.
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Premium: Delivery to most Asian addresses within 5-9 business days

Disclaimer:
All orders received before 5 PM U.K time would start printing from the next business day. So the estimated delivery times start from the next day as well. Orders received after 5 PM U.K time (in our internal systems) on a business day or anytime on the weekend will begin printing the second to next business day. For example, an order placed at 11 AM today will begin printing tomorrow, whereas an order placed at 9 PM tonight will begin printing the day after tomorrow.


Unfortunately, due to several restrictions, we are unable to ship to the following countries:

  1. Afghanistan
  2. American Samoa
  3. Belarus
  4. Brunei Darussalam
  5. Central African Republic
  6. The Democratic Republic of Congo
  7. Eritrea
  8. Guinea-bissau
  9. Iran
  10. Lebanon
  11. Libiya Arab Jamahriya
  12. Somalia
  13. Sudan
  14. Russian Federation
  15. Syrian Arab Republic
  16. Ukraine
  17. Venezuela
What is custom duty/charge? Chevron down icon Chevron up icon

Customs duty are charges levied on goods when they cross international borders. It is a tax that is imposed on imported goods. These duties are charged by special authorities and bodies created by local governments and are meant to protect local industries, economies, and businesses.

Do I have to pay customs charges for the print book order? Chevron down icon Chevron up icon

The orders shipped to the countries that are listed under EU27 will not bear custom charges. They are paid by Packt as part of the order.

List of EU27 countries: www.gov.uk/eu-eea:

A custom duty or localized taxes may be applicable on the shipment and would be charged by the recipient country outside of the EU27 which should be paid by the customer and these duties are not included in the shipping charges been charged on the order.

How do I know my custom duty charges? Chevron down icon Chevron up icon

The amount of duty payable varies greatly depending on the imported goods, the country of origin and several other factors like the total invoice amount or dimensions like weight, and other such criteria applicable in your country.

For example:

  • If you live in Mexico, and the declared value of your ordered items is over $ 50, for you to receive a package, you will have to pay additional import tax of 19% which will be $ 9.50 to the courier service.
  • Whereas if you live in Turkey, and the declared value of your ordered items is over € 22, for you to receive a package, you will have to pay additional import tax of 18% which will be € 3.96 to the courier service.
How can I cancel my order? Chevron down icon Chevron up icon

Cancellation Policy for Published Printed Books:

You can cancel any order within 1 hour of placing the order. Simply contact [email protected] with your order details or payment transaction id. If your order has already started the shipment process, we will do our best to stop it. However, if it is already on the way to you then when you receive it, you can contact us at [email protected] using the returns and refund process.

Please understand that Packt Publishing cannot provide refunds or cancel any order except for the cases described in our Return Policy (i.e. Packt Publishing agrees to replace your printed book because it arrives damaged or material defect in book), Packt Publishing will not accept returns.

What is your returns and refunds policy? Chevron down icon Chevron up icon

Return Policy:

We want you to be happy with your purchase from Packtpub.com. We will not hassle you with returning print books to us. If the print book you receive from us is incorrect, damaged, doesn't work or is unacceptably late, please contact Customer Relations Team on [email protected] with the order number and issue details as explained below:

  1. If you ordered (eBook, Video or Print Book) incorrectly or accidentally, please contact Customer Relations Team on [email protected] within one hour of placing the order and we will replace/refund you the item cost.
  2. Sadly, if your eBook or Video file is faulty or a fault occurs during the eBook or Video being made available to you, i.e. during download then you should contact Customer Relations Team within 14 days of purchase on [email protected] who will be able to resolve this issue for you.
  3. You will have a choice of replacement or refund of the problem items.(damaged, defective or incorrect)
  4. Once Customer Care Team confirms that you will be refunded, you should receive the refund within 10 to 12 working days.
  5. If you are only requesting a refund of one book from a multiple order, then we will refund you the appropriate single item.
  6. Where the items were shipped under a free shipping offer, there will be no shipping costs to refund.

On the off chance your printed book arrives damaged, with book material defect, contact our Customer Relation Team on [email protected] within 14 days of receipt of the book with appropriate evidence of damage and we will work with you to secure a replacement copy, if necessary. Please note that each printed book you order from us is individually made by Packt's professional book-printing partner which is on a print-on-demand basis.

What tax is charged? Chevron down icon Chevron up icon

Currently, no tax is charged on the purchase of any print book (subject to change based on the laws and regulations). A localized VAT fee is charged only to our European and UK customers on eBooks, Video and subscriptions that they buy. GST is charged to Indian customers for eBooks and video purchases.

What payment methods can I use? Chevron down icon Chevron up icon

You can pay with the following card types:

  1. Visa Debit
  2. Visa Credit
  3. MasterCard
  4. PayPal
What is the delivery time and cost of print books? Chevron down icon Chevron up icon

Shipping Details

USA:

'

Economy: Delivery to most addresses in the US within 10-15 business days

Premium: Trackable Delivery to most addresses in the US within 3-8 business days

UK:

Economy: Delivery to most addresses in the U.K. within 7-9 business days.
Shipments are not trackable

Premium: Trackable delivery to most addresses in the U.K. within 3-4 business days!
Add one extra business day for deliveries to Northern Ireland and Scottish Highlands and islands

EU:

Premium: Trackable delivery to most EU destinations within 4-9 business days.

Australia:

Economy: Can deliver to P. O. Boxes and private residences.
Trackable service with delivery to addresses in Australia only.
Delivery time ranges from 7-9 business days for VIC and 8-10 business days for Interstate metro
Delivery time is up to 15 business days for remote areas of WA, NT & QLD.

Premium: Delivery to addresses in Australia only
Trackable delivery to most P. O. Boxes and private residences in Australia within 4-5 days based on the distance to a destination following dispatch.

India:

Premium: Delivery to most Indian addresses within 5-6 business days

Rest of the World:

Premium: Countries in the American continent: Trackable delivery to most countries within 4-7 business days

Asia:

Premium: Delivery to most Asian addresses within 5-9 business days

Disclaimer:
All orders received before 5 PM U.K time would start printing from the next business day. So the estimated delivery times start from the next day as well. Orders received after 5 PM U.K time (in our internal systems) on a business day or anytime on the weekend will begin printing the second to next business day. For example, an order placed at 11 AM today will begin printing tomorrow, whereas an order placed at 9 PM tonight will begin printing the day after tomorrow.


Unfortunately, due to several restrictions, we are unable to ship to the following countries:

  1. Afghanistan
  2. American Samoa
  3. Belarus
  4. Brunei Darussalam
  5. Central African Republic
  6. The Democratic Republic of Congo
  7. Eritrea
  8. Guinea-bissau
  9. Iran
  10. Lebanon
  11. Libiya Arab Jamahriya
  12. Somalia
  13. Sudan
  14. Russian Federation
  15. Syrian Arab Republic
  16. Ukraine
  17. Venezuela