Data Science on AWS: Implementing End-to-End, Continuous AI and Machine Learning Pipelines
Price: $31.41
(as of Nov 22,2024 16:12:55 UTC – Details)
From the Publisher
Who Should Read This Book
This e-book is for anybody who makes use of information to make important enterprise choices. The steerage right here will assist information analysts, information scientists, information engineers, ML engineers, analysis scientists, utility builders, and DevOps engineers broaden their understanding of the fashionable information science stack and stage up their abilities within the cloud.
The Amazon AI and ML stack unifies information science, information engineering, and utility growth to assist customers stage up their abilities past their present roles. We present the way to construct and run pipelines within the cloud, then combine the outcomes into purposes in minutes as an alternative of days.
Ideally, and to get most out of this e-book, we recommend readers have the next information:
Basic understanding of cloud computingBasic programming abilities with Python, R, Java/Scala, or SQLBasic familiarity with information science instruments reminiscent of Jupyter Notebook, pandas, NumPy, or scikit-learn
Overview of the Chapters
Chapter 1 supplies an outline of the broad and deep Amazon AI and ML stack, an enormously highly effective and numerous set of providers, open supply libraries, and infrastructure to make use of for information science tasks of any complexity and scale.
Chapter 2 describes the way to apply the Amazon AI and ML stack to real-world use instances for suggestions, laptop imaginative and prescient, fraud detection, pure language understanding (NLU), conversational units, cognitive search, buyer assist, industrial predictive upkeep, residence automation, Internet of Things (IoT), healthcare, and quantum computing.
Chapter 3 demonstrates the way to use AutoML to implement a particular subset of those use instances with SageMaker Autopilot.
Chapters 4–9 dive deep into the entire mannequin growth life cycle (MDLC) for a BERT-based NLP use case, together with information ingestion and evaluation, function choice and engineering, mannequin coaching and tuning, and mannequin deployment with Amazon SageMaker, Amazon Athena, Amazon Redshift, Amazon EMR, TensorFlow, PyTorch, and serverless Apache Spark.
Chapter 10 ties every little thing collectively into repeatable pipelines utilizing MLOps with SageMaker Pipelines, Kubeflow Pipelines, Apache Airflow, MLflow, and TFX.
Chapter 11 demonstrates real-time ML, anomaly detection, and streaming analytics on real-time information streams with Amazon Kinesis and Apache Kafka.
Chapter 12 presents a complete set of safety finest practices for information science tasks and workflows, together with IAM, authentication, authorization, community isolation, information encryption at relaxation, post-quantum community encryption in transit, governance, and auditability.
Throughout the e-book, we offer tricks to cut back value and enhance efficiency for information science tasks on AWS.
O’Reilly
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Publisher:O’Reilly Media; 1st version (April 27, 2021)
Language:English
Paperback:524 pages
ISBN-10:1492079391
ISBN-13:978-1492079392
Item Weight:1.82 kilos
Dimensions:7 x 1.05 x 9.19 inches
