Zion Tech Group

Tag: pipelines

  • RAG-Driven Generative AI: Build custom retrieval augmented generation pipelines

    RAG-Driven Generative AI: Build custom retrieval augmented generation pipelines



    RAG-Driven Generative AI: Build custom retrieval augmented generation pipelines

    Price : 47.94

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    Artificial intelligence has made significant advancements in recent years, with one of the most groundbreaking developments being the emergence of retrieval-augmented generation models. These models, known as RAG-driven generative AI, combine the power of large-scale language models with the ability to retrieve relevant information from external sources.

    With RAG-driven generative AI, developers can build custom pipelines that not only generate text based on a given prompt but also incorporate information from external sources to enhance the quality and relevance of the generated output. This opens up a wide range of possibilities for applications in natural language processing, content generation, and more.

    By leveraging RAG-driven generative AI, developers can create highly customized and specialized models that are tailored to specific use cases and domains. Whether it’s generating product descriptions, creating personalized recommendations, or automating content creation, RAG-driven generative AI offers a powerful tool for building advanced AI systems.

    To get started with building custom retrieval-augmented generation pipelines, developers can explore existing frameworks and libraries such as Hugging Face’s Transformers library, which provides pre-trained models and tools for fine-tuning and customizing models for specific tasks. By experimenting with different configurations and datasets, developers can create highly effective and efficient RAG-driven generative AI systems that can revolutionize how we interact with and utilize artificial intelligence.
    #RAGDriven #Generative #Build #custom #retrieval #augmented #generation #pipelines

  • RAG-Driven Generative AI: Build custom retrieval augmented generation pipelines

    RAG-Driven Generative AI: Build custom retrieval augmented generation pipelines



    RAG-Driven Generative AI: Build custom retrieval augmented generation pipelines

    Price : 59.53 – 49.61

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    In recent years, the field of natural language processing has seen significant advancements in the development of generative AI models. One such model, known as Retrieval-Augmented Generation (RAG), combines the power of both retrieval-based and generative models to enhance the quality of generated text.

    RAG-driven generative AI models work by first retrieving relevant information from a large database or knowledge base and then using this information to generate coherent and contextually relevant text. This approach allows the model to leverage the vast amount of existing knowledge available on the internet to improve the quality and accuracy of generated text.

    One of the key advantages of RAG-driven generative AI is its ability to build custom retrieval augmented generation pipelines. By fine-tuning the retrieval mechanism and training the generative model on specific datasets, developers can create customized pipelines that are tailored to their specific needs and requirements.

    These custom pipelines can be used for a wide range of applications, including content generation, question answering, and language translation. By combining the strengths of both retrieval-based and generative models, RAG-driven generative AI offers a powerful and versatile tool for developers looking to build advanced natural language processing applications.

    Overall, RAG-driven generative AI represents a significant step forward in the field of natural language processing, offering new opportunities for innovation and advancement. By building custom retrieval augmented generation pipelines, developers can harness the full potential of this technology to create sophisticated and highly accurate text generation systems.
    #RAGDriven #Generative #Build #custom #retrieval #augmented #generation #pipelines

  • RAG-Driven Generative AI: Build custom retrieval augmented generation pipelines with LlamaIndex, Deep Lake, and Pinecone

    RAG-Driven Generative AI: Build custom retrieval augmented generation pipelines with LlamaIndex, Deep Lake, and Pinecone


    Price: $43.99 – $41.79
    (as of Dec 17,2024 12:33:17 UTC – Details)


    From the brand

    Packt's Brand Story

    Packt's Brand Story

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    Packt is a leading publisher of technical learning content with the ability to publish books on emerging tech faster than any other.

    Our mission is to increase the shared value of deep tech knowledge by helping tech pros put software to work.

    We help the most interesting minds and ground-breaking creators on the planet distill and share the working knowledge of their peers.

    New Releases

    LLMs and Generative AI

    Machine Learning

    See Our Full Range

    Publisher ‏ : ‎ Packt Publishing (September 30, 2024)
    Language ‏ : ‎ English
    Paperback ‏ : ‎ 334 pages
    ISBN-10 ‏ : ‎ 1836200919
    ISBN-13 ‏ : ‎ 978-1836200918
    Item Weight ‏ : ‎ 1.59 pounds
    Dimensions ‏ : ‎ 0.47 x 7.5 x 9.25 inches


    Are you looking to take your generative AI models to the next level? Look no further than RAG-Driven Generative AI, where you can build custom retrieval augmented generation pipelines using cutting-edge tools like LlamaIndex, Deep Lake, and Pinecone.

    LlamaIndex is a powerful indexing system that allows you to efficiently search and retrieve information from large datasets. By integrating LlamaIndex into your generative AI workflow, you can quickly access relevant data to enhance the output of your models.

    Deep Lake is a sophisticated deep learning platform that enables you to train and deploy complex AI models with ease. With Deep Lake, you can fine-tune your generative AI models for specific tasks and improve their performance significantly.

    Pinecone is a scalable vector database that allows you to store and query high-dimensional vectors efficiently. By leveraging Pinecone in your generative AI pipelines, you can easily compare and retrieve embeddings to enhance the quality of your model’s output.

    By combining these powerful tools, you can create robust and efficient generative AI systems that deliver high-quality results consistently. Take your AI projects to the next level with RAG-Driven Generative AI and revolutionize the way you approach machine learning.
    #RAGDriven #Generative #Build #custom #retrieval #augmented #generation #pipelines #LlamaIndex #Deep #Lake #Pinecone

  • Learning Azure DevOps: Outperform DevOps using Azure Pipelines, Artifacts, Boards, Azure CLI, Test Plans and Repos

    Learning Azure DevOps: Outperform DevOps using Azure Pipelines, Artifacts, Boards, Azure CLI, Test Plans and Repos


    Price: $34.99
    (as of Dec 16,2024 10:34:37 UTC – Details)


    From the Publisher

    Learning Azure DevOps

    Learning Azure DevOps

    Learning Azure DevOps

    Learning Azure DevOps

    Learning Azure DevOps

    Learning Azure DevOps

    Learning Azure DevOps

    Chapters You Must Read..

    Getting Started with Azure DevOps
    Pipeline as Code with YAML
    Continuous Integration with Azure Pipelines
    Continuous Delivery with Azure Pipelines
    Managing Dependencies with Azure Artifacts
    Testing and Quality with Azure Test Plans
    Infrastructure Automation with Azure Pipelines
    Collaboration and Team Management in Azure DevOps

    Assist DevOps teams to automate, orchestrate, and manage applications and service delivery

    This book will teach you automate everything from builds and tests to database migrations and infrastructure provisioning. From creating shared pipelines to collaborating with multiple teams, it teaches to align DevOps practices with teamwork.

    This book is about providing you with tools to use Azure DevOps effectively in your daily work. “Learning Azure DevOps,” is a reflection of my experiences, and it serves as a comprehensive practices to adopt DevOps at scale.

    Develop YAML-based Pipeline as Code to streamline the process of automating builds, tests, and deployments.
    Utilize Azure Boards and Project Boards to manage and monitor work items, tasks, and user stories.
    Add Postman, JUnit, and Mockito to your continuous integration pipelines to automate your application testing.
    Integrate Flyway into Azure Pipelines to automate database schema migrations and achieve continuous delivery.
    Facilitate cross-team and cross-project cooperation through shared pipelines and resources.
    Use Azure DevOps Analytics and performance insights for project management and monitoring.
    Use Terraform in conjunction with Azure Pipelines to deploy cloud-based IaC.
    Deploy backups and failover procedures automatically in Azure DevOps.

    GitforGits | Asian Publishing House

    GitforGits | Asian Publishing House

    Publisher ‏ : ‎ GitforGits (August 4, 2024)
    Language ‏ : ‎ English
    Paperback ‏ : ‎ 200 pages
    ISBN-10 ‏ : ‎ 8119177312
    ISBN-13 ‏ : ‎ 978-8119177318
    Item Weight ‏ : ‎ 12.5 ounces
    Dimensions ‏ : ‎ 7.5 x 0.46 x 9.25 inches


    Are you looking to take your DevOps practices to the next level? Look no further than Azure DevOps! In this post, we will explore how you can outperform DevOps using Azure Pipelines, Artifacts, Boards, Azure CLI, Test Plans, and Repos.

    Azure Pipelines allows you to automate your build and deployment processes, ensuring faster and more reliable releases. With features like multi-stage pipelines, YAML support, and integration with popular CI/CD tools, you can streamline your development workflows and deliver high-quality software with ease.

    Azure Artifacts provides a centralized repository for your packages, ensuring that your dependencies are always up-to-date and accessible to your team. By managing your artifacts in one place, you can simplify package management and improve collaboration across your projects.

    Azure Boards offers a flexible and customizable way to plan, track, and discuss work across your team. With features like backlogs, sprints, and Kanban boards, you can easily organize your tasks and prioritize your work to ensure that you meet your project deadlines.

    Azure CLI allows you to manage your Azure resources from the command line, enabling you to automate repetitive tasks and streamline your operations. With support for scripting and automation, you can easily scale your infrastructure and deploy your applications with confidence.

    Azure Test Plans provides a comprehensive solution for testing your applications, with features like manual and exploratory testing, test case management, and automated testing. By integrating testing into your development process, you can identify and fix issues early, ensuring that your software meets your quality standards.

    Azure Repos offers a secure and scalable way to manage your source code, with support for Git repositories, pull requests, and code reviews. By hosting your code in Azure Repos, you can collaborate with your team, track changes, and ensure that your code is always secure and up-to-date.

    By leveraging these powerful Azure DevOps services, you can take your DevOps practices to the next level and outperform your competition. Whether you are a developer, a tester, or an operations professional, Azure DevOps has the tools and capabilities you need to succeed. So why wait? Start learning Azure DevOps today and unlock the full potential of your DevOps journey.
    #Learning #Azure #DevOps #Outperform #DevOps #Azure #Pipelines #Artifacts #Boards #Azure #CLI #Test #Plans #Repos

  • Cloud Policy: A History of Regulating Pipelines, Platforms, and Data (Distribution Matters)

    Cloud Policy: A History of Regulating Pipelines, Platforms, and Data (Distribution Matters)


    Price: $65.00
    (as of Dec 15,2024 06:38:16 UTC – Details)




    Publisher ‏ : ‎ The MIT Press (September 17, 2024)
    Language ‏ : ‎ English
    Paperback ‏ : ‎ 326 pages
    ISBN-10 ‏ : ‎ 0262548062
    ISBN-13 ‏ : ‎ 978-0262548069
    Item Weight ‏ : ‎ 13.3 ounces
    Dimensions ‏ : ‎ 6.06 x 0.83 x 9 inches


    Cloud Policy: A History of Regulating Pipelines, Platforms, and Data (Distribution Matters)

    The evolution of cloud computing has brought about a myriad of benefits for businesses and consumers alike, from increased efficiency and scalability to improved collaboration and accessibility. However, with these advancements also come new challenges and concerns surrounding data privacy, security, and fair competition.

    Over the years, policymakers have grappled with how to regulate the cloud effectively, particularly when it comes to the distribution of data through pipelines and platforms. The issue of data ownership and control has become increasingly important as more and more businesses rely on cloud services to store and process their information.

    One of the key aspects of cloud policy has been the regulation of pipelines, or the physical infrastructure that enables the transfer of data between users and cloud servers. This includes regulations around data sovereignty, data localization, and data residency requirements to ensure that sensitive information is stored and processed in compliance with regional laws and regulations.

    Platforms, on the other hand, refer to the software and applications that enable users to access and interact with cloud services. Regulating platforms involves ensuring fair competition and preventing monopolistic practices that could stifle innovation and harm consumers. This has led to debates around net neutrality, data portability, and interoperability to promote a level playing field for all players in the cloud ecosystem.

    Data itself has also been a focal point of cloud policy, with regulations such as the General Data Protection Regulation (GDPR) in the European Union and the California Consumer Privacy Act (CCPA) in the United States aimed at protecting individuals’ personal information and holding companies accountable for how they collect, store, and use data.

    As cloud computing continues to evolve and expand, it is clear that distribution matters when it comes to regulating pipelines, platforms, and data. Policymakers must strike a balance between promoting innovation and competition while safeguarding privacy and security to ensure a fair and transparent cloud ecosystem for all.
    #Cloud #Policy #History #Regulating #Pipelines #Platforms #Data #Distribution #Matters

  • Data Science on the Google Cloud Platform: Implementing End-to-End Real-Time Data Pipelines: From Ingest to Machine Learning

    Data Science on the Google Cloud Platform: Implementing End-to-End Real-Time Data Pipelines: From Ingest to Machine Learning


    Price: $64.99 – $38.11
    (as of Dec 14,2024 16:59:11 UTC – Details)


    From the Publisher

    Data Science on the Google Cloud Platform: Implementing End-to-End Real-Time Data Pipelines: From InData Science on the Google Cloud Platform: Implementing End-to-End Real-Time Data Pipelines: From In

    From the Preface

    In this book, we walk through an example of this new transformative, more collaborative way of doing data science. You will learn how to implement an end-to-end data pipeline-we will begin with ingesting the data in a serverless way and work our way through data exploration, dashboards, relational databases, and streaming data all the way to training and making operational a machine learning model. I cover all these aspects of data-based services because data engineers will be involved in designing the services, developing the statistical and machine learning models and implementing them in large-scale production and in real time.

    Who This Book Is For

    If you use computers to work with data, this book is for you. You might go by the title of data analyst, database administrator, data engineer, data scientist, or systems programmer today. Although your role might be narrower today (perhaps you do only data analysis, or only model building, or only DevOps), you want to stretch your wings a bit-you want to learn how to create data science models as well as how to implement them at scale in production systems.

    Google Cloud Platform is designed to make you forget about infrastructure. The marquee data services-Google BigQuery, Cloud Dataflow, Cloud Pub/Sub, and Cloud ML Engine-are all serverless and autoscaling. When you submit a query to BigQuery, it is run on thousands of nodes, and you get your result back; you don’t spin up a cluster or install any software. Similarly, in Cloud Dataflow, when you submit a data pipeline, and in Cloud Machine Learning Engine, when you submit a machine learning job, you can process data at scale and train models at scale without worrying about cluster management or failure recovery. Cloud Pub/Sub is a global messaging service that autoscales to the throughput and number of subscribers and publishers without any work on your part. Even when you’re running open source software like Apache Spark that’s designed to operate on a cluster, Google Cloud Platform makes it easy. Leave your data on Google Cloud Storage, not in HDFS, and spin up a job-specific cluster to run the Spark job. After the job completes, you can safely delete the cluster. Because of this job-specific infrastructure, there’s no need to fear overprovisioning hardware or running out of capacity to run a job when you need it. Plus, data is encrypted, both at rest and in transit, and kept secure. As a data scientist, not having to manage infrastructure is incredibly liberating.

    The reason that you can afford to forget about virtual machines and clusters when running on Google Cloud Platform comes down to networking. The network bisection bandwidth within a Google Cloud Platform datacenter is 1 PBps, and so sustained reads off Cloud Storage are extremely fast. What this means is that you don’t need to shard your data as you would with traditional MapReduce jobs. Instead, Google Cloud Platform can autoscale your compute jobs by shuffling the data onto new compute nodes as needed. Hence, you’re liberated from cluster management when doing data science on Google Cloud Platform.

    These autoscaled, fully managed services make it easier to implement data science models at scale-which is why data scientists no longer need to hand off their models to data engineers. Instead, they can write a data science workload, submit it to the cloud, and have that workload executed automatically in an autoscaled manner. At the same time, data science packages are becoming simpler and simpler. So, it has become extremely easy for an engineer to slurp in data and use a canned model to get an initial (and often very good) model up and running. With well-designed packages and easy-to-consume APIs, you don’t need to know the esoteric details of data science algorithms-only what each algorithm does, and how to link algorithms together to solve realistic problems. This convergence between data science and data engineering is why you can stretch your wings beyond your current role.

    Rather than simply read this book cover-to-cover, I strongly encourage you to follow along with me by also trying out the code. The full source code for the end-to-end pipeline I build in this book is on GitHub. Create a Google Cloud Platform project and after reading each chapter, try to repeat what I did by referring to the code and to the Readme file in each folder of the GitHub repository.

    Publisher ‏ : ‎ O’Reilly Media; 1st edition (February 6, 2018)
    Language ‏ : ‎ English
    Paperback ‏ : ‎ 402 pages
    ISBN-10 ‏ : ‎ 1491974567
    ISBN-13 ‏ : ‎ 978-1491974568
    Item Weight ‏ : ‎ 1.44 pounds
    Dimensions ‏ : ‎ 7.25 x 1 x 9.25 inches


    Data Science on the Google Cloud Platform: Implementing End-to-End Real-Time Data Pipelines

    In today’s fast-paced digital world, the ability to quickly and efficiently analyze large amounts of data has become essential for businesses to stay competitive. Data science is a crucial tool in this process, allowing companies to extract valuable insights from their data and make informed decisions.

    Google Cloud Platform (GCP) offers a powerful set of tools and services for data science and analytics, making it easier than ever to build end-to-end real-time data pipelines. From data ingestion to machine learning, GCP provides a comprehensive suite of services to help businesses harness the power of their data.

    In this post, we will walk you through the process of implementing an end-to-end real-time data pipeline on the Google Cloud Platform. We will cover the following steps:

    1. Data Ingestion: We will start by ingesting data from various sources into GCP using tools like Cloud Storage, Cloud Pub/Sub, and Dataflow. These tools make it easy to collect, store, and process data in real-time.

    2. Data Processing: Once the data is ingested, we will use tools like Cloud Dataflow and BigQuery to process and analyze the data. These tools allow us to run complex data transformations and queries at scale, making it easy to extract valuable insights from our data.

    3. Machine Learning: Finally, we will use tools like Cloud AI Platform and TensorFlow to build and deploy machine learning models on GCP. These tools make it easy to train, test, and deploy models at scale, allowing us to make accurate predictions and automate decision-making processes.

    By following these steps, you can build a robust end-to-end real-time data pipeline on the Google Cloud Platform, enabling your business to make data-driven decisions and stay ahead of the competition. So, what are you waiting for? Start harnessing the power of data science on GCP today!
    #Data #Science #Google #Cloud #Platform #Implementing #EndtoEnd #RealTime #Data #Pipelines #Ingest #Machine #Learning

  • Ultimate Data Engineering with Databricks: Develop Scalable Data Pipelines Using Data Engineering’s Core Tenets Such as Delta Tables, Ingestion, … Security, and Scalability (English Edition)

    Ultimate Data Engineering with Databricks: Develop Scalable Data Pipelines Using Data Engineering’s Core Tenets Such as Delta Tables, Ingestion, … Security, and Scalability (English Edition)


    Price: $37.95
    (as of Dec 14,2024 11:32:00 UTC – Details)


    From the Publisher

    Know more about the book

    Ultimate-Data-Engineering-with-DatabricksUltimate-Data-Engineering-with-Databricks

    Ultimate-Data-Engineering-with-DatabricksUltimate-Data-Engineering-with-Databricks

    Ultimate Data Engineering with Databricks Navigating Databricks with Ease for Unparalleled Data Engineering Insights.

    In an age where data is the new currency, mastering the art of data engineering has become more crucial than ever. This book, Ultimate Data Engineering with Databricks, is a culmination of my experiences and learnings, designed to guide you through the intricacies of data engineering in the modern cloud environment.

    The journey begins with Chapter 1, Fundamentals of Data Engineering with Databricks, providing a solid foundation for those new to the field or looking to strengthen their core understanding. Following this, Chapter 2, Mastering Delta Tables in Databricks, dives into the specifics of handling data at scale, a skill pivotal in today’s data-intensive world.

    As you progress through the chapters, from Chapter 3, Data Ingestion and Extraction, to Chapter 4, Data Transformation and ETL Processes, the focus shifts to the practical aspects of managing and manipulating data.

    WHAT WILL YOU LEARN

    ● Acquire proficiency in Databricks fundamentals, enabling the construction of efficient data pipelines.

    ● Design and implement high-performance data solutions for scalability.

    ● Apply essential best practices for ensuring data integrity in pipelines.

    ● Explore advanced Databricks features for tackling complex data tasks.

    ● Learn to optimize data pipelines for streamlined workflows.

    WHO IS THIS BOOK FOR?

    This book caters to a diverse audience, including data engineers, data architects, BI analysts, data scientists, and technology enthusiasts. Suitable for both professionals and students, the book appeals to those eager to master Databricks and stay at the forefront of data engineering trends.

    KEY FEATURES Navigate Databricks with a seamless progression from fundamental principles to advanced engineering techniques. Gain hands-on experience with real-world examples, ensuring immediate relevance and practicality. Discover expert insights and best practices for refining your data engineering skills and achieving superior results with Databricks.

    Mayank MalhotraMayank Malhotra

    About the Author

    Mayank Malhotra’s journey in the tech world began as a big data engineer, quickly evolving into a versatile data engineering His extensive experience spans various cloud platforms such as AWS, Azure, and Databricks, as well as On-Prem Infrastructure, showcasing his adaptability and depth of knowledge. A BTech graduate, Mayank’s academic foundation laid the groundwork for his successful career.

    In the realm of data engineering, Mayank has tackled a diverse range of projects, from data migration and modeling to data transformation and quality validation. His ability to navigate complex data landscapes has not only honed his skills but also made him a sought-after expert in the field. One of his key beliefs, “Be the senior you needed as a junior,” reflects his passion for mentoring. He thrives on guiding others, sharing insights, and discussing new design approaches in data engineering, making him a valuable mentor and leader.

    Nawaz AbbasNawaz Abbas

    Meet the Technical Reviewer

    Nawaz Abbas started his career with Accenture 12 years ago. His journey in the field of Information Technology has given him a chance to explore multiple domains such as Banking, Security, and Consumer sectors, with exposure to various technologies in the field of Big Data and Analytics.

    He likes to be involved in building and designing data pipelines using various Big Data Technologies like PySpark, Databricks, Scala, Java, Kafka, Hive, Airflow, and more. More recently, he has taken on the roles ofa Technical Lead and/or Big Data Engineer. He has worked on various AWS components, including AWS Lambda, SNS, Athena, S3, EC2, Load Balancer, Elastic Beanstalk, ASG, and more.

    As an avid reader, Nawaz likes to remain close to newer technologies and stay connected to the latest industry trends. In his free time, you might find him spending time with his family, traveling, watching soccer, playing cricket, or participating in CSR events.

    Copyright Disclaimer

    Copyright at 2024, Orange Education Pvt Ltd, AVA

    All rights reserved. No part of this book may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, without the prior written permission of the publisher, except in the case of brief quotations embedded in critical articles or reviews. Every effort has been made in the preparation of this book to ensure the accuracy of the information presented. However, the information contained in this book is sold without convincing, either express or implied.

    Neither the author nor Orange Education Pvt Ltd. or its dealers and distributors, will be held liable for any damages caused or alleged to have been caused directly or indirectly by this book.

    Orange Education Pvt Ltd. has endeavored to provide brand information about all of the companies and products mentioned in this book by the appropriate use of capital. However, Orange Education Pvt Ltd. cannot surety the accuracy of this information. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use.

    First published: February 2024

    Published by: Orange Education Pvt Ltd, AVA

    Publisher ‏ : ‎ Orange Education Pvt Ltd (February 15, 2024)
    Language ‏ : ‎ English
    Paperback ‏ : ‎ 267 pages
    ISBN-10 ‏ : ‎ 8196994788
    ISBN-13 ‏ : ‎ 978-8196994785
    Item Weight ‏ : ‎ 1.03 pounds
    Dimensions ‏ : ‎ 7.5 x 0.64 x 9.25 inches


    Are you looking to take your data engineering skills to the next level? Look no further than “Ultimate Data Engineering with Databricks: Develop Scalable Data Pipelines Using Data Engineering’s Core Tenets Such as Delta Tables, Ingestion, Security, and Scalability.”

    In this comprehensive guide, you will learn how to leverage Databricks, a unified analytics platform, to build scalable and efficient data pipelines. From understanding the fundamentals of data engineering to mastering advanced techniques such as Delta tables, ingestion, security, and scalability, this book covers everything you need to know to excel in the field of data engineering.

    Whether you are a beginner looking to get started with data engineering or an experienced professional looking to enhance your skills, “Ultimate Data Engineering with Databricks” has something for everyone. So why wait? Dive into the world of data engineering and unlock the true potential of your data with this essential guide.
    #Ultimate #Data #Engineering #Databricks #Develop #Scalable #Data #Pipelines #Data #Engineerings #Core #Tenets #Delta #Tables #Ingestion #Security #Scalability #English #Edition

  • Data Science on AWS: Implementing End-to-End, Continuous AI and Machine Learning Pipelines

    Data Science on AWS: Implementing End-to-End, Continuous AI and Machine Learning Pipelines


    Price: $79.99 – $29.86
    (as of Dec 13,2024 22:18:00 UTC – Details)


    From the brand

    oreillyoreilly

    Explore more AWS resources

    OreillyOreilly

    Sharing the knowledge of experts

    O’Reilly’s mission is to change the world by sharing the knowledge of innovators. For over 40 years, we’ve inspired companies and individuals to do new things (and do them better) by providing the skills and understanding that are necessary for success.

    Our customers are hungry to build the innovations that propel the world forward. And we help them do just that.

    Publisher ‏ : ‎ O’Reilly Media; 1st edition (May 11, 2021)
    Language ‏ : ‎ English
    Paperback ‏ : ‎ 521 pages
    ISBN-10 ‏ : ‎ 1492079391
    ISBN-13 ‏ : ‎ 978-1492079392
    Item Weight ‏ : ‎ 1.82 pounds
    Dimensions ‏ : ‎ 7 x 1.05 x 9.19 inches


    Data Science on AWS: Implementing End-to-End, Continuous AI and Machine Learning Pipelines

    In the world of data science, implementing end-to-end, continuous AI and machine learning pipelines is essential for delivering accurate and timely insights. With the vast amount of data being generated every day, organizations need to leverage advanced tools and technologies to extract valuable information from their data.

    One such tool that is widely used in the data science community is Amazon Web Services (AWS). AWS provides a comprehensive suite of services that enable data scientists to build, train, and deploy machine learning models at scale. By leveraging AWS, data scientists can streamline their workflow and focus on developing innovative solutions rather than managing infrastructure.

    To implement end-to-end, continuous AI and machine learning pipelines on AWS, data scientists can follow these steps:

    1. Data Collection: The first step in building a machine learning pipeline is to collect and store the data. AWS offers services like Amazon S3 and Amazon RDS for storing and managing large datasets.

    2. Data Preprocessing: Once the data is collected, it needs to be cleaned and preprocessed before it can be used for training machine learning models. AWS provides services like Amazon SageMaker for data preprocessing and feature engineering.

    3. Model Training: After the data is preprocessed, data scientists can train their machine learning models using AWS SageMaker. SageMaker offers built-in algorithms and tools for training models on large datasets.

    4. Model Deployment: Once the model is trained, it needs to be deployed in a production environment. AWS provides services like Amazon SageMaker hosting for deploying machine learning models as RESTful APIs.

    5. Continuous Integration and Deployment: To ensure that the machine learning pipeline is always up to date, data scientists can use AWS CodePipeline and AWS CodeBuild for continuous integration and deployment.

    By following these steps and leveraging the power of AWS, data scientists can build end-to-end, continuous AI and machine learning pipelines that deliver valuable insights to organizations. With AWS’s scalable infrastructure and advanced tools, data scientists can focus on developing innovative solutions and driving business growth.
    #Data #Science #AWS #Implementing #EndtoEnd #Continuous #Machine #Learning #Pipelines

  • Data Pipelines Pocket Reference: Moving and Processing Data for Analytics

    Data Pipelines Pocket Reference: Moving and Processing Data for Analytics


    Price: $29.99 – $17.29
    (as of Dec 03,2024 15:47:03 UTC – Details)



    Are you looking for a handy guide to help you navigate the world of data pipelines for analytics? Look no further than the “Data Pipelines Pocket Reference”! This essential resource will provide you with everything you need to know about moving and processing data efficiently for analytics.

    From understanding the basics of data pipelines to mastering the tools and techniques for seamless data movement, this pocket reference has got you covered. Whether you’re a data scientist, data engineer, or anyone working with data, this guide will help you streamline your processes and optimize your analytics.

    Don’t let the complexities of data pipelines overwhelm you. Pick up your copy of the “Data Pipelines Pocket Reference” today and take your data analytics skills to the next level!
    #Data #Pipelines #Pocket #Reference #Moving #Processing #Data #Analytics

  • High-Performance Computing with Julia: Optimizing Algorithms and Applications (Practical Julia books : Machine learning, Numerical methods, Data pipelines, … statistics & High performance computing)

    High-Performance Computing with Julia: Optimizing Algorithms and Applications (Practical Julia books : Machine learning, Numerical methods, Data pipelines, … statistics & High performance computing)


    Price: $6.11
    (as of Nov 30,2024 06:58:20 UTC – Details)




    ASIN ‏ : ‎ B0D6VYZ9H2
    Publication date ‏ : ‎ June 11, 2024
    Language ‏ : ‎ English
    File size ‏ : ‎ 290 KB
    Simultaneous device usage ‏ : ‎ Unlimited
    Text-to-Speech ‏ : ‎ Enabled
    Screen Reader ‏ : ‎ Supported
    Enhanced typesetting ‏ : ‎ Enabled
    X-Ray ‏ : ‎ Not Enabled
    Word Wise ‏ : ‎ Not Enabled
    Print length ‏ : ‎ 212 pages


    High-Performance Computing with Julia: Optimizing Algorithms and Applications

    Are you looking to take your high-performance computing skills to the next level? Look no further than Julia, the high-level, high-performance programming language that is revolutionizing the world of scientific computing.

    In this practical guide, you will learn how to optimize algorithms and applications using Julia. From machine learning and numerical methods to data pipelines and statistics, this book covers a wide range of topics that will help you unleash the full potential of Julia for high-performance computing.

    Whether you are a seasoned programmer or just starting out, this book has something for everyone. With step-by-step instructions and real-world examples, you will learn how to write efficient code, leverage parallel computing techniques, and speed up your computations in no time.

    So why wait? Dive into the world of high-performance computing with Julia and unlock a whole new level of speed and performance for your algorithms and applications. Get your copy of this practical Julia book today!
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