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Data Orchestration In Deep Learning Accelerators
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Book Synopsis Data Orchestration in Deep Learning Accelerators by : Tushar Krishna
Download or read book Data Orchestration in Deep Learning Accelerators written by Tushar Krishna and published by Springer Nature. This book was released on 2022-05-31 with total page 158 pages. Available in PDF, EPUB and Kindle. Book excerpt: This Synthesis Lecture focuses on techniques for efficient data orchestration within DNN accelerators. The End of Moore's Law, coupled with the increasing growth in deep learning and other AI applications has led to the emergence of custom Deep Neural Network (DNN) accelerators for energy-efficient inference on edge devices. Modern DNNs have millions of hyper parameters and involve billions of computations; this necessitates extensive data movement from memory to on-chip processing engines. It is well known that the cost of data movement today surpasses the cost of the actual computation; therefore, DNN accelerators require careful orchestration of data across on-chip compute, network, and memory elements to minimize the number of accesses to external DRAM. The book covers DNN dataflows, data reuse, buffer hierarchies, networks-on-chip, and automated design-space exploration. It concludes with data orchestration challenges with compressed and sparse DNNs and future trends. The target audience is students, engineers, and researchers interested in designing high-performance and low-energy accelerators for DNN inference.
Book Synopsis Deep Learning Systems by : Andres Rodriguez
Download or read book Deep Learning Systems written by Andres Rodriguez and published by Springer Nature. This book was released on 2022-05-31 with total page 245 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes deep learning systems: the algorithms, compilers, and processor components to efficiently train and deploy deep learning models for commercial applications. The exponential growth in computational power is slowing at a time when the amount of compute consumed by state-of-the-art deep learning (DL) workloads is rapidly growing. Model size, serving latency, and power constraints are a significant challenge in the deployment of DL models for many applications. Therefore, it is imperative to codesign algorithms, compilers, and hardware to accelerate advances in this field with holistic system-level and algorithm solutions that improve performance, power, and efficiency. Advancing DL systems generally involves three types of engineers: (1) data scientists that utilize and develop DL algorithms in partnership with domain experts, such as medical, economic, or climate scientists; (2) hardware designers that develop specialized hardware to accelerate the components in the DL models; and (3) performance and compiler engineers that optimize software to run more efficiently on a given hardware. Hardware engineers should be aware of the characteristics and components of production and academic models likely to be adopted by industry to guide design decisions impacting future hardware. Data scientists should be aware of deployment platform constraints when designing models. Performance engineers should support optimizations across diverse models, libraries, and hardware targets. The purpose of this book is to provide a solid understanding of (1) the design, training, and applications of DL algorithms in industry; (2) the compiler techniques to map deep learning code to hardware targets; and (3) the critical hardware features that accelerate DL systems. This book aims to facilitate co-innovation for the advancement of DL systems. It is written for engineers working in one or more of these areas who seek to understand the entire system stack in order to better collaborate with engineers working in other parts of the system stack. The book details advancements and adoption of DL models in industry, explains the training and deployment process, describes the essential hardware architectural features needed for today's and future models, and details advances in DL compilers to efficiently execute algorithms across various hardware targets. Unique in this book is the holistic exposition of the entire DL system stack, the emphasis on commercial applications, and the practical techniques to design models and accelerate their performance. The author is fortunate to work with hardware, software, data scientist, and research teams across many high-technology companies with hyperscale data centers. These companies employ many of the examples and methods provided throughout the book.
Book Synopsis Algorithm-accelerator Co-design for High-performance and Secure Deep Learning by : Weizhe Hua
Download or read book Algorithm-accelerator Co-design for High-performance and Secure Deep Learning written by Weizhe Hua and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning has emerged as a new engine for many of today's artificial intelligence/machine learning systems, leading to several recent breakthroughs in vision and natural language processing tasks.However, as we move into the era of deep learning with billions and even trillions of parameters, meeting the computational and memory requirements to train and serve state-of-the-art models has become extremely challenging. Optimizing the computational cost and memory footprint of deep learning models for better system performance is critical to the widespread deployment of deep learning. Moreover, a massive amount of sensitive and private user data is exposed to the deep learning system during the training or serving process. Therefore, it is essential to investigate potential vulnerabilities in existing deep learning hardware, and then design secure deep learning systems that provide strong privacy guarantees for user data and the models that learn from the data. In this dissertation, we propose to co-design the deep learning algorithms and hardware architectural techniques to improve both the performance and security/privacy of deep learning systems. On high-performance deep learning, we first introduce channel gating neural network (CGNet), which exploits the dynamic sparsity of specific inputs to reduce computation of convolutional neural networks. We also co-develop an ASIC accelerator for CGNet that can turn theoretical FLOP reduction into wall-clock speedup. Secondly, we present Fast Linear Attention with a Single Head (FLASH), a state-of-the-art language model specifically designed for Google's TPU that can achieve transformer-level quality with linear complexity with respect to the sequence length. Through our empirical studies on masked language modeling, auto-regressive language modeling, and fine-tuning for question answering, FLASH achieves at least similar if not better quality compared to the augmented transformer, while being significantly faster (e.g., up to 12 times faster). On the security of deep learning, we study the side-channel vulnerabilities of existing deep learning accelerators. We then introduce a secure accelerator architecture for privacy-preserving deep learning, named GuardNN. GuardNN provides a trusted execution environment (TEE) with specialized protection for deep learning, and achieves a small trusted computing base and low protection overhead at the same time. The FPGA prototype of GuardNN achieves a maximum performance overhead of 2.4\% across four different modern DNNs models for ImageNet.
Download or read book Deep Learning written by Josh Patterson and published by "O'Reilly Media, Inc.". This book was released on 2017-07-28 with total page 532 pages. Available in PDF, EPUB and Kindle. Book excerpt: Although interest in machine learning has reached a high point, lofty expectations often scuttle projects before they get very far. How can machine learning—especially deep neural networks—make a real difference in your organization? This hands-on guide not only provides the most practical information available on the subject, but also helps you get started building efficient deep learning networks. Authors Adam Gibson and Josh Patterson provide theory on deep learning before introducing their open-source Deeplearning4j (DL4J) library for developing production-class workflows. Through real-world examples, you’ll learn methods and strategies for training deep network architectures and running deep learning workflows on Spark and Hadoop with DL4J. Dive into machine learning concepts in general, as well as deep learning in particular Understand how deep networks evolved from neural network fundamentals Explore the major deep network architectures, including Convolutional and Recurrent Learn how to map specific deep networks to the right problem Walk through the fundamentals of tuning general neural networks and specific deep network architectures Use vectorization techniques for different data types with DataVec, DL4J’s workflow tool Learn how to use DL4J natively on Spark and Hadoop
Book Synopsis Exploiting Data Characteristics in The Design of Accelerators for Deep Learning by : Patrick H. Judd
Download or read book Exploiting Data Characteristics in The Design of Accelerators for Deep Learning written by Patrick H. Judd and published by . This book was released on 2019 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The recent "Cambrian explosion" of Deep Learning (DL) algorithms in concert with the end of Moore's Law and Dennard Scaling has spurred interest in the design of custom hardware accelerators for DL algorithms. While DL has progressed quickly thanks in part to the abundance of efficient parallel computation provided by General Purpose Graphics Processing Units, newer DL algorithms demand even higher levels of compute density and efficiency. Furthermore, applications of DL in the mobile and embedded domains demand the energy efficiency of special purpose hardware. DL algorithms are dominated by large matrix-vector product computations, making them ideal targets for wide Single Instruction Multiple Data architectures. For the most part, efficiently mapping the structure of these computations to hardware is straightforward. Building on such designs, this thesis examines the data characteristics of these computations and proposes hardware modifications to exploit them for performance and energy efficiency. Specifically, this thesis examines the sparsity and precision requirements of Deep Convolutional Neural Networks, which comprise multiple layers of matrix-vector product computations. We propose a profiling method to find per layer reduced precision configurations while maintaining high classification accuracy. Following this, we propose three accelerator designs that build on top of the state-of-the-art DaDianNao accelerator. 1) Proteus exploits the reduced precision profiles by adding a light weight memory compression layer, saving energy in memory access and communication, and enabling larger networks in a fixed memory budget. 2) Cnvlutin exploits the presence of zero, and near zero, values in the inter-layer data by applying sparse compression to the data stream while maintain efficient utilization of the wide memory and compute structure of the SIMD accelerator. 3) Stripes exploits the reduced precision profiles for performance by processing data bit-serially, compensating for serial latency by exploiting the abundant parallelism in the convolution operation. All three designs exploit approximation, in terms of reduced precision and computation skipping to improve energy efficiency and/or performance while maintaining high classification accuracy. By approximating more aggressively, these designs can also dynamically trade-off accuracy for further improvements in performance and energy.
Book Synopsis Deep Learning for Data Architects by : Shekhar Khandelwal
Download or read book Deep Learning for Data Architects written by Shekhar Khandelwal and published by BPB Publications. This book was released on 2023-08-16 with total page 251 pages. Available in PDF, EPUB and Kindle. Book excerpt: A hands-on guide to building and deploying deep learning models with Python KEY FEATURES ● Acquire the skills to perform exploratory data analysis, uncover insights, and preprocess data for deep learning tasks. ● Build and train various types of neural networks, including Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). ● Gain hands-on experience by working on practical projects and applying deep learning techniques to real-world problems. DESCRIPTION “Deep Learning for Data Architects” is a comprehensive guide that bridges the gap between data architecture and deep learning. It provides a solid foundation in Python for data science and serves as a launchpad into the world of AI and deep learning. The book begins by addressing the challenges of transforming raw data into actionable insights. It provides a practical understanding of data handling and covers the construction of neural network-based predictive models. The book then explores specialized networks such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs). The book delves into the theory and practical aspects of these networks and offers Python code implementations for each. The final chapter of the book introduces Transformers, a revolutionary model that has had a significant impact on natural language processing (NLP). This chapter provides you with a thorough understanding of how Transformers work and includes Python code implementations. By the end of the book, you will be able to use deep learning to solve real-world problems. WHAT YOU WILL LEARN ● Develop a comprehensive understanding of neural networks' key concepts and principles. ● Gain proficiency in Python as you code and implement major deep-learning algorithms from scratch. ● Build and implement predictive models using various neural networks ● Learn how to use Transformers for complex NLP tasks ● Explore techniques to enhance the performance of your deep learning models. WHO THIS BOOK IS FOR This book is for anyone who is interested in a career in emerging technologies, such as artificial intelligence (AI), data analytics, machine learning, deep learning, and data science. It is a comprehensive guide that covers the fundamentals of these technologies, as well as the skills and knowledge that you need to succeed in this field. TABLE OF CONTENTS 1. Python for Data Science 2. Real-World Challenges for Data Professionals in Converting Data Into Insights 3. Build a Neural Network-Based Predictive Model 4. Convolutional Neural Networks 5. Optical Character Recognition 6. Object Detection 7. Image Segmentation 8. Recurrent Neural Networks 9. Generative Adversarial Networks 10. Transformers
Book Synopsis Integrating Deep Learning Algorithms to Overcome Challenges in Big Data Analytics by : R. Sujatha
Download or read book Integrating Deep Learning Algorithms to Overcome Challenges in Big Data Analytics written by R. Sujatha and published by CRC Press. This book was released on 2021-09-22 with total page 216 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data science revolves around two giants: Big Data analytics and Deep Learning. It is becoming challenging to handle and retrieve useful information due to how fast data is expanding. This book presents the technologies and tools to simplify and streamline the formation of Big Data as well as Deep Learning systems. This book discusses how Big Data and Deep Learning hold the potential to significantly increase data understanding and decision-making. It also covers numerous applications in healthcare, education, communication, media, and entertainment. Integrating Deep Learning Algorithms to Overcome Challenges in Big Data Analytics offers innovative platforms for integrating Big Data and Deep Learning and presents issues related to adequate data storage, semantic indexing, data tagging, and fast information retrieval. FEATURES Provides insight into the skill set that leverages one’s strength to act as a good data analyst Discusses how Big Data and Deep Learning hold the potential to significantly increase data understanding and help in decision-making Covers numerous potential applications in healthcare, education, communication, media, and entertainment Offers innovative platforms for integrating Big Data and Deep Learning Presents issues related to adequate data storage, semantic indexing, data tagging, and fast information retrieval from Big Data This book is aimed at industry professionals, academics, research scholars, system modelers, and simulation experts.
Book Synopsis Efficient Processing of Deep Neural Networks by : Vivienne Sze
Download or read book Efficient Processing of Deep Neural Networks written by Vivienne Sze and published by Springer Nature. This book was released on 2022-05-31 with total page 254 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a structured treatment of the key principles and techniques for enabling efficient processing of deep neural networks (DNNs). DNNs are currently widely used for many artificial intelligence (AI) applications, including computer vision, speech recognition, and robotics. While DNNs deliver state-of-the-art accuracy on many AI tasks, it comes at the cost of high computational complexity. Therefore, techniques that enable efficient processing of deep neural networks to improve key metrics—such as energy-efficiency, throughput, and latency—without sacrificing accuracy or increasing hardware costs are critical to enabling the wide deployment of DNNs in AI systems. The book includes background on DNN processing; a description and taxonomy of hardware architectural approaches for designing DNN accelerators; key metrics for evaluating and comparing different designs; features of DNN processing that are amenable to hardware/algorithm co-design to improve energy efficiency and throughput; and opportunities for applying new technologies. Readers will find a structured introduction to the field as well as formalization and organization of key concepts from contemporary work that provide insights that may spark new ideas.
Book Synopsis Deep Learning: Convergence to Big Data Analytics by : Murad Khan
Download or read book Deep Learning: Convergence to Big Data Analytics written by Murad Khan and published by Springer. This book was released on 2018-12-30 with total page 79 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents deep learning techniques, concepts, and algorithms to classify and analyze big data. Further, it offers an introductory level understanding of the new programming languages and tools used to analyze big data in real-time, such as Hadoop, SPARK, and GRAPHX. Big data analytics using traditional techniques face various challenges, such as fast, accurate and efficient processing of big data in real-time. In addition, the Internet of Things is progressively increasing in various fields, like smart cities, smart homes, and e-health. As the enormous number of connected devices generate huge amounts of data every day, we need sophisticated algorithms to deal, organize, and classify this data in less processing time and space. Similarly, existing techniques and algorithms for deep learning in big data field have several advantages thanks to the two main branches of the deep learning, i.e. convolution and deep belief networks. This book offers insights into these techniques and applications based on these two types of deep learning. Further, it helps students, researchers, and newcomers understand big data analytics based on deep learning approaches. It also discusses various machine learning techniques in concatenation with the deep learning paradigm to support high-end data processing, data classifications, and real-time data processing issues. The classification and presentation are kept quite simple to help the readers and students grasp the basics concepts of various deep learning paradigms and frameworks. It mainly focuses on theory rather than the mathematical background of the deep learning concepts. The book consists of 5 chapters, beginning with an introductory explanation of big data and deep learning techniques, followed by integration of big data and deep learning techniques and lastly the future directions.
Book Synopsis Advanced Deep Learning Applications in Big Data Analytics by : Bouarara, Hadj Ahmed
Download or read book Advanced Deep Learning Applications in Big Data Analytics written by Bouarara, Hadj Ahmed and published by IGI Global. This book was released on 2020-10-16 with total page 351 pages. Available in PDF, EPUB and Kindle. Book excerpt: Interest in big data has swelled within the scholarly community as has increased attention to the internet of things (IoT). Algorithms are constructed in order to parse and analyze all this data to facilitate the exchange of information. However, big data has suffered from problems in connectivity, scalability, and privacy since its birth. The application of deep learning algorithms has helped process those challenges and remains a major issue in today’s digital world. Advanced Deep Learning Applications in Big Data Analytics is a pivotal reference source that aims to develop new architecture and applications of deep learning algorithms in big data and the IoT. Highlighting a wide range of topics such as artificial intelligence, cloud computing, and neural networks, this book is ideally designed for engineers, data analysts, data scientists, IT specialists, programmers, marketers, entrepreneurs, researchers, academicians, and students.
Book Synopsis Deep Learning in Data Analytics by : Debi Prasanna Acharjya
Download or read book Deep Learning in Data Analytics written by Debi Prasanna Acharjya and published by Springer Nature. This book was released on 2021-08-11 with total page 271 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book comprises theoretical foundations to deep learning, machine learning and computing system, deep learning algorithms, and various deep learning applications. The book discusses significant issues relating to deep learning in data analytics. Further in-depth reading can be done from the detailed bibliography presented at the end of each chapter. Besides, this book's material includes concepts, algorithms, figures, graphs, and tables in guiding researchers through deep learning in data science and its applications for society. Deep learning approaches prevent loss of information and hence enhance the performance of data analysis and learning techniques. It brings up many research issues in the industry and research community to capture and access data effectively. The book provides the conceptual basis of deep learning required to achieve in-depth knowledge in computer and data science. It has been done to make the book more flexible and to stimulate further interest in topics. All these help researchers motivate towards learning and implementing the concepts in real-life applications.
Book Synopsis Data Science by : Gyanendra K. Verma
Download or read book Data Science written by Gyanendra K. Verma and published by Springer Nature. This book was released on 2021-08-19 with total page 444 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book targets an audience with a basic understanding of deep learning, its architectures, and its application in the multimedia domain. Background in machine learning is helpful in exploring various aspects of deep learning. Deep learning models have a major impact on multimedia research and raised the performance bar substantially in many of the standard evaluations. Moreover, new multi-modal challenges are tackled, which older systems would not have been able to handle. However, it is very difficult to comprehend, let alone guide, the process of learning in deep neural networks, there is an air of uncertainty about exactly what and how these networks learn. By the end of the book, the readers will have an understanding of different deep learning approaches, models, pre-trained models, and familiarity with the implementation of various deep learning algorithms using various frameworks and libraries.
Book Synopsis Data Accelerator for AI and Analytics by : Simon Lorenz
Download or read book Data Accelerator for AI and Analytics written by Simon Lorenz and published by IBM Redbooks. This book was released on 2021-01-20 with total page 88 pages. Available in PDF, EPUB and Kindle. Book excerpt: This IBM® Redpaper publication focuses on data orchestration in enterprise data pipelines. It provides details about data orchestration and how to address typical challenges that customers face when dealing with large and ever-growing amounts of data for data analytics. While the amount of data increases steadily, artificial intelligence (AI) workloads must speed up to deliver insights and business value in a timely manner. This paper provides a solution that addresses these needs: Data Accelerator for AI and Analytics (DAAA). A proof of concept (PoC) is described in detail. This paper focuses on the functions that are provided by the Data Accelerator for AI and Analytics solution, which simplifies the daily work of data scientists and system administrators. This solution helps increase the efficiency of storage systems and data processing to obtain results faster while eliminating unnecessary data copies and associated data management.
Book Synopsis Simulating Dataflow Accelerators for Deep Learning Application in Heterogeneous System by : Quang Anh Hoang
Download or read book Simulating Dataflow Accelerators for Deep Learning Application in Heterogeneous System written by Quang Anh Hoang and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: For the past few decades, deep learning has emerged as an essential discipline that broadens the horizon of the knowledge of humankind. At its core, Deep Neural Networks (DNN) play a vital role in processing input data to generate predictions or decisions (inference step), with their accuracy ameliorated by extensive training (training step). As the complexity of the problem increases, the number of layers in DNN models tends to rise. Such complex models require more computations and take longer to produce an output. Additionally, the large number of calculations require a tremendous amount of power. Therefore, improving energy efficiency is a primary design consideration. To address this concern, researchers have studied domain-specific architecture to develop highly efficient hardware tailored for a given application, which performs a given set of computations at a lower energy cost. An energy-efficient yet high-performance system is created by pairing this application-specific accelerator with a General-Purpose Processor (GPP). This heterogeneity helps offload the heavy computations to the accelerator while handling less computation intensive tasks on the GPP. In this thesis, we study the performance of dataflow accelerators integrated into a heterogeneous architecture for executing deep learning workloads. Fundamental to these accelerators is their high levels of concurrency in executing computations simultaneously, making them suitable to exploit data parallelism present in DNN operations. With the limited bandwidth of interconnection between accelerator and main memory being one of the critical constraints of a heterogeneous system, a tradeoff between memory overhead and computational runtime is worth considering. This tradeoff is the main criteria we use in this thesis to evaluate the performance of each architecture and configuration. A model of dataflow memristive crossbar array accelerator is first proposed to expand the scope of the heterogeneous simulation framework towards architectures with analog and mixed-signal circuits. At the core of this accelerator, an array of resistive memory cells connected in crossbar architecture is used for computing matrix multiplications. This design aims to study the effect of memory-performance tradeoffs on systems with analog components. Therefore, a comparison between memristive crossbar array architecture and its digital counterpart, systolic array, is presented. While existing studies focus on heterogeneous systems with digital components, this approach is the first to consider a mixed-signal accelerator incorporated with a general-purpose processor for deep learning workloads. Finally, an application interface software is designed to configure the system's architecture and map DNN layers to simulated hardware. At the core of this software is a DNN model parser-partitioner, which provides subsequent tasks of generating a hardware configuration for the accelerator and assigns partitioned workload to the simulated accelerator. The interface provided by this software can be developed further to incorporate scheduling and mapping algorithms. This extension will produce a synthesizer that will facilitate the following: • Hardware configuration: generate the optimal configuration of system hardware, incorporating the key hardware characteristics such as the number of accelerators, dimension of processing array, and memory allocation for each accelerator. • Schedule of execution: implement a mapping algorithm to decide on an efficient distribution and schedule of partitioned workloads. For future development, this synthesizer will unite the first two stages in system's design flow. In the first analysis stage, simulators search for optimal design aspects under a short time frame based on abstract application graphs and the system's specifications. In architecture stage, within the optimal design region from previous stage, simulators refine their findings by studying further details on architectural level. This inter-stage fusion, once finished, can bring the high accuracy of architectural-level simulation tool closer to analysis stage. In the opposite direction, mapping algorithms implemented in analysis tools can provide architectural exploration with near-optimal scheduling. Together, this stack of software can significantly reduce the time searching for specifications with optimal efficiency.
Download or read book Deep Learning written by Li Deng and published by . This book was released on 2014 with total page 212 pages. Available in PDF, EPUB and Kindle. Book excerpt: Provides an overview of general deep learning methodology and its applications to a variety of signal and information processing tasks
Book Synopsis Deep Learning for Computer Architects by : Brandon Reagen
Download or read book Deep Learning for Computer Architects written by Brandon Reagen and published by Morgan & Claypool Publishers. This book was released on 2017-08-22 with total page 182 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is a primer written for computer architects in the new and rapidly evolving field of deep learning. It reviews how machine learning has evolved since its inception in the 1960s and tracks the key developments leading up to the emergence of the powerful deep learning techniques that emerged in the last decade. Machine learning, and specifically deep learning, has been hugely disruptive in many fields of computer science. The success of deep learning techniques in solving notoriously difficult classification and regression problems has resulted in their rapid adoption in solving real-world problems. The emergence of deep learning is widely attributed to a virtuous cycle whereby fundamental advancements in training deeper models were enabled by the availability of massive datasets and high-performance computer hardware. It also reviews representative workloads, including the most commonly used datasets and seminal networks across a variety of domains. In addition to discussing the workloads themselves, it also details the most popular deep learning tools and show how aspiring practitioners can use the tools with the workloads to characterize and optimize DNNs. The remainder of the book is dedicated to the design and optimization of hardware and architectures for machine learning. As high-performance hardware was so instrumental in the success of machine learning becoming a practical solution, this chapter recounts a variety of optimizations proposed recently to further improve future designs. Finally, it presents a review of recent research published in the area as well as a taxonomy to help readers understand how various contributions fall in context.
Book Synopsis Building Machine Learning Pipelines by : Hannes Hapke
Download or read book Building Machine Learning Pipelines written by Hannes Hapke and published by O'Reilly Media. This book was released on 2020-07-13 with total page 367 pages. Available in PDF, EPUB and Kindle. Book excerpt: Companies are spending billions on machine learning projects, but it’s money wasted if the models can’t be deployed effectively. In this practical guide, Hannes Hapke and Catherine Nelson walk you through the steps of automating a machine learning pipeline using the TensorFlow ecosystem. You’ll learn the techniques and tools that will cut deployment time from days to minutes, so that you can focus on developing new models rather than maintaining legacy systems. Data scientists, machine learning engineers, and DevOps engineers will discover how to go beyond model development to successfully productize their data science projects, while managers will better understand the role they play in helping to accelerate these projects. Understand the steps to build a machine learning pipeline Build your pipeline using components from TensorFlow Extended Orchestrate your machine learning pipeline with Apache Beam, Apache Airflow, and Kubeflow Pipelines Work with data using TensorFlow Data Validation and TensorFlow Transform Analyze a model in detail using TensorFlow Model Analysis Examine fairness and bias in your model performance Deploy models with TensorFlow Serving or TensorFlow Lite for mobile devices Learn privacy-preserving machine learning techniques
