Zion Tech Group

Tag: pipelines

  • Data Observability for Data Engineering: Proactive strategies for ensuring data accuracy and addressing broken data pipelines

    Data Observability for Data Engineering: Proactive strategies for ensuring data accuracy and addressing broken data pipelines


    Price: $34.67
    (as of Dec 27,2024 18:57:44 UTC – Details)




    ASIN ‏ : ‎ B0B71HNZHR
    Publisher ‏ : ‎ Packt Publishing; 1st edition (December 29, 2023)
    Publication date ‏ : ‎ December 29, 2023
    Language ‏ : ‎ English
    File size ‏ : ‎ 8242 KB
    Text-to-Speech ‏ : ‎ Enabled
    Screen Reader ‏ : ‎ Supported
    Enhanced typesetting ‏ : ‎ Enabled
    X-Ray ‏ : ‎ Not Enabled
    Word Wise ‏ : ‎ Not Enabled
    Print length ‏ : ‎ 363 pages


    Data observability is a critical component of any data engineering strategy, as it involves monitoring, analyzing, and troubleshooting data pipelines to ensure data accuracy and reliability. In today’s data-driven world, organizations rely on data to make informed decisions and drive business outcomes. However, without proper data observability practices in place, data engineers may face challenges in detecting and addressing issues in data pipelines, leading to inaccuracies, delays, and potentially costly errors.

    To proactively address these challenges, data engineers can implement a set of strategies for ensuring data observability. Here are some key proactive strategies for data observability in data engineering:

    1. Establish data quality metrics: Define and monitor key data quality metrics, such as completeness, accuracy, consistency, and timeliness, to ensure that data pipelines are functioning as expected. By establishing these metrics, data engineers can quickly identify any deviations or anomalies in the data and take corrective actions to address them.

    2. Implement automated monitoring and alerting: Set up automated monitoring tools and alerts to detect issues in data pipelines in real-time. By leveraging monitoring tools, data engineers can proactively identify potential bottlenecks, failures, or performance issues in data pipelines and take immediate actions to resolve them before they impact the business.

    3. Conduct regular data validation and testing: Perform regular data validation and testing to ensure the accuracy and integrity of data in the pipelines. By validating data against predefined rules, data engineers can identify discrepancies, missing values, or inconsistencies in the data and address them before they lead to downstream issues.

    4. Document data lineage and dependencies: Document data lineage and dependencies across different stages of the data pipeline to understand the flow of data and identify potential points of failure. By mapping out data lineage, data engineers can trace data back to its source and pinpoint any issues or discrepancies that may arise along the way.

    5. Collaborate cross-functionally: Foster collaboration and communication among data engineering, data science, and business teams to ensure alignment on data requirements, expectations, and priorities. By working together, teams can proactively address data quality issues, optimize data pipelines, and drive better outcomes for the organization.

    In conclusion, data observability is a critical aspect of data engineering that requires proactive strategies to ensure data accuracy and reliability. By implementing these proactive strategies, data engineers can detect and address issues in data pipelines before they impact the business, ultimately driving better decision-making and business outcomes.
    #Data #Observability #Data #Engineering #Proactive #strategies #ensuring #data #accuracy #addressing #broken #data #pipelines

  • Hands-On Machine Learning with C++: Build, train, and deploy end-to-end machine learning and deep learning pipelines

    Hands-On Machine Learning with C++: Build, train, and deploy end-to-end machine learning and deep learning pipelines


    Price: $49.99
    (as of Dec 27,2024 07:16:35 UTC – Details)




    Publisher ‏ : ‎ Packt Publishing; 2nd edition (January 24, 2025)
    Language ‏ : ‎ English
    Paperback ‏ : ‎ 512 pages
    ISBN-10 ‏ : ‎ 1805120573
    ISBN-13 ‏ : ‎ 978-1805120575
    Item Weight ‏ : ‎ 2.38 pounds
    Dimensions ‏ : ‎ 1.4 x 7.5 x 9.25 inches


    Are you ready to take your machine learning skills to the next level? Look no further than our latest book, “Hands-On Machine Learning with C++: Build, train, and deploy end-to-end machine learning and deep learning pipelines.”

    In this comprehensive guide, you will learn how to build, train, and deploy machine learning and deep learning models using the power of C++. With hands-on exercises and real-world examples, you will gain a deep understanding of the principles and techniques behind machine learning, as well as practical experience in implementing them.

    Whether you are a beginner looking to get started with machine learning or an experienced developer looking to expand your skills, this book is the perfect resource for you. Don’t miss out on this opportunity to master machine learning with C++ and take your projects to the next level. Order your copy today!
    #HandsOn #Machine #Learning #Build #train #deploy #endtoend #machine #learning #deep #learning #pipelines

  • Python For Effect: Master Data Visualization and Analysis: Learn Data Pipelines, Machine Learning, Advanced Statistical Analysis and Visualization with Jupyter Notebook

    Python For Effect: Master Data Visualization and Analysis: Learn Data Pipelines, Machine Learning, Advanced Statistical Analysis and Visualization with Jupyter Notebook


    Price: $4.99
    (as of Dec 27,2024 01:06:02 UTC – Details)




    ASIN ‏ : ‎ B0DKTYVK1W
    Publication date ‏ : ‎ October 23, 2024
    Language ‏ : ‎ English
    File size ‏ : ‎ 2043 KB
    Text-to-Speech ‏ : ‎ Enabled
    Screen Reader ‏ : ‎ Supported
    Enhanced typesetting ‏ : ‎ Enabled
    X-Ray ‏ : ‎ Not Enabled
    Word Wise ‏ : ‎ Not Enabled
    Print length ‏ : ‎ 229 pages


    Are you looking to level up your data visualization and analysis skills using Python? Look no further! In this post, we will explore how you can master data visualization and analysis with Python for maximum effect.

    Python is a powerful programming language that is widely used in the field of data science and analytics. With its vast array of libraries and tools, Python allows you to easily manipulate, analyze, and visualize data to uncover valuable insights.

    One of the key tools in the Python ecosystem for data visualization and analysis is Jupyter Notebook. Jupyter Notebook is an interactive computing environment that allows you to create and share documents that contain live code, equations, visualizations, and narrative text.

    In this post, we will cover how you can use Jupyter Notebook to create data pipelines, perform machine learning tasks, conduct advanced statistical analysis, and visualize your data in a meaningful way. By the end of this post, you will have a solid understanding of how to leverage Python for effective data visualization and analysis.

    So if you’re ready to take your data analysis skills to the next level, stay tuned for our upcoming posts on mastering data visualization and analysis with Python and Jupyter Notebook. Get ready to unlock the full potential of your data!
    #Python #Effect #Master #Data #Visualization #Analysis #Learn #Data #Pipelines #Machine #Learning #Advanced #Statistical #Analysis #Visualization #Jupyter #Notebook

  • Machine Learning Engineering on AWS: Operationalize and optimize Generative AI systems and LLMOps pipelines in production

    Machine Learning Engineering on AWS: Operationalize and optimize Generative AI systems and LLMOps pipelines in production


    Price: $49.99 – $47.49
    (as of Dec 26,2024 16:49:14 UTC – Details)



    Machine Learning Engineering on AWS: Operationalize and optimize Generative AI systems and MLOps pipelines in production

    In the world of machine learning, operationalizing and optimizing Generative AI systems and MLOps pipelines in production is crucial for achieving successful outcomes. As organizations strive to leverage the power of artificial intelligence to drive innovation and competitive advantage, the ability to efficiently deploy and manage machine learning models at scale becomes increasingly important.

    One platform that has gained significant traction in the machine learning community is Amazon Web Services (AWS). With its wide range of tools and services specifically designed for machine learning, AWS provides a robust environment for building, training, and deploying sophisticated AI models.

    When it comes to operationalizing and optimizing Generative AI systems on AWS, there are several best practices to keep in mind. Generative AI systems, which are capable of creating new data based on existing patterns, require careful monitoring and tuning to ensure they are generating high-quality outputs. By leveraging AWS services such as Amazon SageMaker, organizations can streamline the process of training and deploying generative models, while also incorporating real-time feedback mechanisms to continuously improve their performance.

    In addition to Generative AI systems, MLOps pipelines play a critical role in ensuring the smooth operation of machine learning models in production. By implementing best practices for MLOps on AWS, such as version control, automated testing, and continuous integration/continuous deployment (CI/CD), organizations can optimize the efficiency and reliability of their machine learning workflows.

    Overall, by leveraging the capabilities of AWS for operationalizing and optimizing Generative AI systems and MLOps pipelines, organizations can unlock the full potential of their machine learning initiatives and drive impactful business outcomes.
    #Machine #Learning #Engineering #AWS #Operationalize #optimize #Generative #systems #LLMOps #pipelines #production

  • Data Science in Production: Building Scalable Model Pipelines with Python

    Data Science in Production: Building Scalable Model Pipelines with Python


    Price: $39.99
    (as of Dec 26,2024 14:37:07 UTC – Details)




    Publisher ‏ : ‎ Independently published (January 1, 2020)
    Language ‏ : ‎ English
    Paperback ‏ : ‎ 234 pages
    ISBN-10 ‏ : ‎ 165206463X
    ISBN-13 ‏ : ‎ 978-1652064633
    Item Weight ‏ : ‎ 14.7 ounces
    Dimensions ‏ : ‎ 6 x 0.55 x 9 inches


    Data Science in Production: Building Scalable Model Pipelines with Python

    In the world of data science, building models is only half the battle. To truly leverage the power of data, these models need to be deployed and maintained in production systems. This is where the concept of model pipelines comes into play.

    Model pipelines are a series of interconnected steps that take raw data as input, transform it, and then use it to make predictions. These pipelines are crucial for scaling data science projects, as they allow for consistent, repeatable processes that can be easily deployed and monitored.

    Python is a popular choice for building model pipelines, thanks to its extensive libraries for data manipulation and machine learning. In this post, we’ll explore how to build scalable model pipelines using Python.

    1. Data Preprocessing: The first step in building a model pipeline is data preprocessing. This involves cleaning and transforming raw data into a format that can be used by machine learning algorithms. Python libraries like pandas and scikit-learn are invaluable for this task.

    2. Feature Engineering: Once the data is cleaned, the next step is feature engineering. This involves creating new features or transforming existing ones to improve the performance of the model. Python libraries like feature-engine and featuretools can help automate this process.

    3. Model Training: With the data preprocessed and the features engineered, it’s time to train the model. Python libraries like scikit-learn and TensorFlow offer a wide range of machine learning algorithms that can be used for this task.

    4. Model Evaluation: After training the model, it’s important to evaluate its performance. Python libraries like scikit-learn and matplotlib can help visualize the results and identify areas for improvement.

    5. Deployment: Once the model is trained and evaluated, it’s time to deploy it in a production environment. Python libraries like Flask and FastAPI can help build APIs for serving predictions, while tools like Docker and Kubernetes can help manage the deployment process.

    By following these steps and leveraging the power of Python libraries, data scientists can build scalable model pipelines that can be easily deployed and maintained in production systems. This is essential for turning data science projects into actionable insights that drive business value.
    #Data #Science #Production #Building #Scalable #Model #Pipelines #Python

  • Hands-On Machine Learning with C++: Build, train, and deploy end-to-end machine learning and deep learning pipelines

    Hands-On Machine Learning with C++: Build, train, and deploy end-to-end machine learning and deep learning pipelines


    Price: $54.99 – $51.72
    (as of Dec 25,2024 01:16:12 UTC – Details)




    Publisher ‏ : ‎ Packt Publishing (May 15, 2020)
    Language ‏ : ‎ English
    Paperback ‏ : ‎ 530 pages
    ISBN-10 ‏ : ‎ 1789955335
    ISBN-13 ‏ : ‎ 978-1789955330
    Item Weight ‏ : ‎ 2.02 pounds
    Dimensions ‏ : ‎ 9.25 x 7.52 x 1.07 inches


    Hands-On Machine Learning with C++: Build, train, and deploy end-to-end machine learning and deep learning pipelines

    In today’s rapidly evolving technological landscape, machine learning and deep learning have become essential tools for businesses and organizations looking to gain valuable insights from their data. While Python is often the language of choice for developing machine learning models, C++ offers a powerful alternative for those looking to build high-performance and efficient models.

    In our upcoming book, “Hands-On Machine Learning with C++,” we will guide you through the process of building, training, and deploying end-to-end machine learning and deep learning pipelines using C++. You will learn how to leverage popular libraries such as TensorFlow and OpenCV to create robust and scalable models that can handle large datasets and complex tasks.

    Whether you are a seasoned C++ developer looking to expand your skill set or a machine learning enthusiast looking to explore new languages, this book will provide you with the knowledge and tools you need to take your machine learning projects to the next level. Stay tuned for updates on the release date and pre-order information! #MachineLearning #DeepLearning #C++ #DataScience #BookRelease
    #HandsOn #Machine #Learning #Build #train #deploy #endtoend #machine #learning #deep #learning #pipelines

  • Building Machine Learning Pipelines: Automating Model Life Cycles with TensorFlow

    Building Machine Learning Pipelines: Automating Model Life Cycles with TensorFlow


    Price: $79.99 – $74.39
    (as of Dec 24,2024 19:01:28 UTC – Details)


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    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 (August 18, 2020)
    Language ‏ : ‎ English
    Paperback ‏ : ‎ 364 pages
    ISBN-10 ‏ : ‎ 1492053198
    ISBN-13 ‏ : ‎ 978-1492053194
    Item Weight ‏ : ‎ 2.31 pounds
    Dimensions ‏ : ‎ 7 x 0.76 x 9.19 inches


    Building Machine Learning Pipelines: Automating Model Life Cycles with TensorFlow

    In the world of machine learning, the process of developing and deploying models can be time-consuming and complex. However, with the right tools and frameworks, it is possible to automate many aspects of this process, making it more efficient and scalable.

    One such tool is TensorFlow, an open-source machine learning library developed by Google. TensorFlow provides a powerful set of tools for building and training machine learning models, as well as for deploying them in production environments. One of the key features of TensorFlow is its support for building machine learning pipelines, which are sequences of data processing and model training steps that can be automated and optimized.

    By using TensorFlow’s pipeline capabilities, data scientists and machine learning engineers can streamline the process of developing and deploying models, reducing the time and effort required to bring models from prototype to production. This allows teams to iterate more quickly on model development, experiment with different approaches, and ultimately deliver more accurate and effective models.

    In this post, we will explore how to build machine learning pipelines using TensorFlow, including how to preprocess data, train models, and deploy them in production environments. We will also discuss best practices for automating and optimizing these pipelines, helping you to get the most out of TensorFlow’s powerful capabilities for automating model life cycles.
    #Building #Machine #Learning #Pipelines #Automating #Model #Life #Cycles #TensorFlow

  • RAG Generative AI: A Practical Guide to Building Custom Retrieval-Augmented Pipelines and Enhancing AI Systems.

    RAG Generative AI: A Practical Guide to Building Custom Retrieval-Augmented Pipelines and Enhancing AI Systems.


    Price: $9.99
    (as of Dec 24,2024 15:39:55 UTC – Details)




    ASIN ‏ : ‎ B0DMMPDRRZ
    Publication date ‏ : ‎ November 9, 2024
    Language ‏ : ‎ English
    File size ‏ : ‎ 878 KB
    Simultaneous device usage ‏ : ‎ Unlimited
    Text-to-Speech ‏ : ‎ Enabled
    Enhanced typesetting ‏ : ‎ Enabled
    X-Ray ‏ : ‎ Not Enabled
    Word Wise ‏ : ‎ Not Enabled
    Print length ‏ : ‎ 131 pages


    Are you looking to take your AI systems to the next level with the power of RAG Generative AI? In this practical guide, we’ll walk you through the process of building custom retrieval-augmented pipelines to enhance your AI systems.

    First, let’s start with the basics. RAG Generative AI, short for Retrieval-Augmented Generation, is a cutting-edge technology that combines the power of natural language processing (NLP) and information retrieval to generate high-quality responses to user queries. By integrating a retrieval component into the generative model, RAG AI can access a vast amount of external knowledge sources to provide more accurate and contextually relevant answers.

    To build a custom retrieval-augmented pipeline, you’ll need to follow these steps:

    1. Define your retrieval sources: Identify the external knowledge bases or databases that you want your AI system to access for information retrieval. This could include websites, scientific papers, or any other relevant sources of data.

    2. Preprocess the data: Clean and preprocess the data from your retrieval sources to ensure that it is formatted correctly and ready for input into your AI model.

    3. Train the retrieval model: Build and train a retrieval model that can efficiently search through the external knowledge sources to retrieve relevant information based on user queries.

    4. Integrate the retrieval model with your generative AI model: Connect the retrieval model with your existing generative AI system to enhance its capabilities and provide more accurate responses to user queries.

    By following these steps and leveraging the power of RAG Generative AI, you can create a custom retrieval-augmented pipeline that significantly improves the performance of your AI systems. Stay tuned for more tips and tricks on how to maximize the potential of RAG AI in your projects.
    #RAG #Generative #Practical #Guide #Building #Custom #RetrievalAugmented #Pipelines #Enhancing #Systems

  • Data Pipelines with Apache Airflow – Paperback, by Harenslak Bas P.; de – Good

    Data Pipelines with Apache Airflow – Paperback, by Harenslak Bas P.; de – Good



    Data Pipelines with Apache Airflow – Paperback, by Harenslak Bas P.; de – Good

    Price : 36.77

    Ends on : N/A

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    Data Pipelines with Apache Airflow – Paperback, by Harenslak Bas P.; de – Good

    Looking for a comprehensive guide on Apache Airflow and how to build efficient data pipelines? Look no further than “Data Pipelines with Apache Airflow” by Harenslak Bas P. and de.

    This book covers everything you need to know about Apache Airflow, from installation and configuration to building complex data pipelines. With clear explanations, practical examples, and hands-on exercises, this book will help you master the art of data pipeline orchestration with Apache Airflow.

    Whether you’re a data engineer, data scientist, or just someone looking to level up their data skills, this book is a must-have for anyone working with data pipelines. Get your hands on a copy today and start building robust and scalable data pipelines with Apache Airflow.
    #Data #Pipelines #Apache #Airflow #Paperback #Harenslak #Bas #Good

  • DevOps for Salesforce: Build, test, and streamline data pipelines to simplify development in Salesforce

    DevOps for Salesforce: Build, test, and streamline data pipelines to simplify development in Salesforce


    Price: $34.19
    (as of Dec 18,2024 21:02:32 UTC – Details)




    ASIN ‏ : ‎ B07HY69QMK
    Publisher ‏ : ‎ Packt Publishing; 1st edition (September 29, 2018)
    Publication date ‏ : ‎ September 29, 2018
    Language ‏ : ‎ English
    File size ‏ : ‎ 28655 KB
    Text-to-Speech ‏ : ‎ Enabled
    Screen Reader ‏ : ‎ Supported
    Enhanced typesetting ‏ : ‎ Enabled
    X-Ray ‏ : ‎ Not Enabled
    Word Wise ‏ : ‎ Not Enabled
    Print length ‏ : ‎ 222 pages


    In the world of Salesforce development, implementing DevOps practices can greatly improve efficiency and collaboration among teams. By integrating DevOps principles into your Salesforce development process, you can streamline data pipelines, automate testing, and ultimately simplify the development lifecycle.

    Building a strong foundation for DevOps in Salesforce starts with understanding the unique challenges and opportunities that come with developing on the platform. Salesforce’s cloud-based architecture and customization capabilities require a thoughtful approach to managing code changes, data migrations, and testing procedures.

    To begin implementing DevOps for Salesforce, consider the following steps:

    1. Establish clear communication and collaboration channels between developers, admins, and other stakeholders involved in the development process.
    2. Use version control systems like Git to track changes to your Salesforce metadata and code. This will make it easier to manage and review changes, as well as roll back to previous versions if needed.
    3. Automate the deployment process by using tools like Salesforce DX, Jenkins, or CircleCI. This will help ensure consistency in deployments and reduce the risk of human error.
    4. Implement continuous integration and continuous deployment (CI/CD) practices to automate testing and deployment processes. This will help catch bugs early and speed up the release cycle.
    5. Monitor and analyze the performance of your Salesforce applications using tools like Salesforce Health Check and Salesforce Event Monitoring. This will help identify bottlenecks and optimize performance.

    By following these steps and leveraging the right tools and practices, you can build, test, and streamline data pipelines in Salesforce to simplify development and deliver high-quality applications to your users. Embracing DevOps for Salesforce will not only improve the efficiency of your development process but also enhance the overall quality and reliability of your Salesforce applications.
    #DevOps #Salesforce #Build #test #streamline #data #pipelines #simplify #development #Salesforce

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