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MLOps Engineering at Scale
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MLOps Engineering at Scale
MLOps Engineering at Scale teaches you how to implement efficient machine learning systems using pre-built services from AWS and other cloud vendors.
MLOps Engineering at Scale
Artigo n.º: 38043469

MLOps Engineering at Scale

89% of respondents would recommend this to a friend

Artigo n.º: 38043469

AOA 61105

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MLOps Engineering at Scale teaches you how to implement efficient machine learning systems using pre-built services from AWS and other cloud vendors.
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O que se Destaca

Scalable Solutions
MLOps Engineering at Scale offers robust frameworks that adapt seamlessly to growing workloads, ensuring efficient deployment and management of machine learning models without compromising performance.
Streamlined Workflows
The platform simplifies complex processes through automated pipelines, enhancing collaboration among data scientists and engineers, reducing time-to-market for machine learning applications tremendously.
Continuous Monitoring
With built-in monitoring tools, it provides real-time insights into model performance and data drift, allowing proactive adjustments that maintain model accuracy and reliability over time.

Detalhes do produto

Shop MLOps Engineering at Scale online at a best price in Angola. 1617297763
  • Learn how to rapidly bring machine learning models to production with MLOps and pre-built serverless tools
  • Gain knowledge in extracting, transforming, and loading datasets
  • Understand the concept of automatic differentiation in PyTorch
  • Deploy model training pipelines as a service endpoint
  • Learn to monitor and manage the lifecycle of your ML pipeline
  • Implement efficient machine learning systems using pre-built services from AWS and other cloud vendors
Publisher Manning
Publication date March 1, 2022
Language English
Print length 344 pages
ISBN-10 1617297763
ISBN-13 978-1617297762
Item Weight 1.25 pounds (570 grams)
Dimensions 7.38 x 0.7 x 9.25 inches (18.7 x 1.8 x 23.5 cm)

Quem Deverá Comprar?

Suitable For
  • Data Scientists

    MLOps Engineering at Scale streamlines deployment processes, allowing data scientists to focus on model development and iteration.

  • AI/ML Teams

    Teams can utilize this product to manage workflows and collaboration, increasing efficiency in model training and deployment.

  • Enterprise Companies

    Large organizations can benefit from structured MLOps frameworks for scaling and managing multiple machine learning projects effectively.

Not Suitable For
  • Small Startups

    Startups may find the extensive features overwhelming and unnecessary for their early-stage data science projects.

DESCRIÇÃO DO PRODUTO

MLOps Engineering at Scale

Tem alguma Dúvida? Converse connosco

Perguntas e respostas do cliente

  • Pergunta: What is MLOps Engineering at Scale?

    Resposta: MLOps Engineering at Scale refers to the practices and technologies involved in deploying and managing machine learning models in a scalable manner. This approach integrates machine learning, DevOps, and data engineering to streamline workflows, implement continuous integration and deployment (CI/CD) for ML models, and ensure efficient resource utilization. Organizations can benefit by improving collaboration between data scientists and operations teams, enabling faster model iteration, and achieving better performance in production environments.
  • Pergunta: Who can benefit from MLOps Engineering at Scale?

    Resposta: Organizations of all sizes can benefit from MLOps Engineering at Scale, particularly those with large-scale machine learning initiatives. Business analysts, data scientists, and IT operations teams are primary stakeholders who will see improvements in collaboration and productivity. For example, a tech company working on product recommendations can implement MLOps to quickly update their models, ensuring they deliver timely suggestions to customers based on recent behavior.
  • Pergunta: What are the key components of MLOps?

    Resposta: Key components of MLOps include model versioning, automated testing, continuous integration, model deployment, monitoring, and logging. These components work together to create a robust infrastructure for managing ML models throughout their lifecycle. For instance, a financial organization could use these components to ensure their fraud detection models are rigorously tested and can be updated in real-time as new fraud patterns emerge.
  • Pergunta: How does MLOps improve collaboration among teams?

    Resposta: MLOps improves collaboration by fostering a shared responsibility model between data scientists and IT operations through clear processes and tools. This minimizes silos and ensures that everyone is aligned on objectives. For instance, a healthcare provider can use MLOps to smoothly integrate data engineers and analysts, allowing them to swiftly adjust predictive models for patient outcomes based on new data from clinical trials.
  • Pergunta: What challenges does MLOps address?

    Resposta: MLOps addresses the challenges of model deployment, scaling, and monitoring in production environments. It helps in managing complex dependencies and ensuring that models are not only deployed but also performing optimally. For example, a retail company can overcome issues related to seasonal changes in purchasing patterns by leveraging MLOps to automatically retrain and deploy models based on sales data.
  • Pergunta: Can MLOps be applied to non-tech organizations?

    Resposta: Absolutely, MLOps can benefit any organization looking to implement data-driven decision making, regardless of the industry. For example, a manufacturing firm can utilize MLOps to monitor and analyze production data, enabling real-time adjustments to improve efficiency and reduce waste.
  • Pergunta: What tools are commonly used in MLOps?

    Resposta: Common tools in MLOps include MLflow for model management, Kubeflow for orchestration, Jenkins for CI/CD pipelines, and Prometheus for monitoring. These tools help streamline workflows and provide visibility into model performance. For example, an e-commerce platform may leverage these tools to maintain high availability and quickly adapt to changing customer trends.
  • Pergunta: How can MLOps enhance model performance?

    Resposta: MLOps enhances model performance through continuous monitoring and automated retraining based on performance metrics. This ensures that models adapt to new data trends and operational challenges. An example is a social media platform using MLOps to analyze user engagement data and refine recommendation algorithms to keep audiences engaged.
  • Pergunta: What is the importance of monitoring in MLOps?

    Resposta: Monitoring is crucial in MLOps as it helps track model performance and identify issues such as model drift. Effective monitoring ensures timely intervention before performance dips significantly. For instance, a financial institution relies on real-time monitoring to assure that risk assessment models are accurately reflecting market conditions.
  • Pergunta: Where can I buy MLOps Engineering at Scale in Angola?

    Resposta: You can buy MLOps Engineering at Scale through Ubuy, a reliable e-commerce platform that offers a wide range of products, including books and resources related to machine learning and MLOps. Ubuy provides a user-friendly experience and helps you find the resources you need to enhance your understanding of MLOps.

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Prós

  • Comprehensive MLOps concepts covered
  • Practical case studies included
  • Strong community and support
  • Well-structured learning path
  • High-quality resource materials

Contras

  • Occasional outdated examples

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AOA 61105

Encomende já e receba por volta de Sábado, Outubro 10

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Recursos e benefícios

  • Learn how to rapidly create flexible and scalable machine learning systems without taking on the costly overhead of physical hardware.
  • Master how to deploy model training pipelines as a service endpoint, monitor and manage your pipeline’s life cycle and measure performance improvements.
  • No cloud experience is required to understand and implement the concepts mentioned in the book.

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