Running AI End to End in the Cloud - Vrushabh
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Running AI End to End in the Cloud - Vrushabh

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Questions and Answers

What is the AI pipeline?

  • A journey that begins with data collection and ends with model training and inference (correct)
  • A journey that begins with model training and ends with data collection and inference
  • A journey that begins with inference and ends with data collection and model training
  • A journey that begins with model training and ends with data collection and inference

    • True or false: Intel offers tools to help speed up the AI workflow?

    True

    What is needed to achieve efficient AI performance?

  • System tuning
  • Hyperparameters optimization
  • Model compression techniques
  • All of the above (correct)

    • What is Hyperthreading used for?

    <p>To increase parallelism</p> Signup and view all the answers

    True or false: Data ingestion and feature engineering is the majority of the time spent in the AI pipeline?

    <p>True</p> Signup and view all the answers

    What is an example of an optimization tool for tuning parameters?

    <p>SigOpt</p> Signup and view all the answers

    What is the majority of the AI pipeline?

    <p>Feature engineering</p> Signup and view all the answers

    What is the main benefit of using optimized software and frameworks on CPU-based cloud instances?

    <p>Reduced execution time</p> Signup and view all the answers

    True or false: OneDNL is a library optimized for deep learning?

    <p>True</p> Signup and view all the answers

    What is the most important thing to consider when working with AI?

    <p>Looking at the problem holistically</p> Signup and view all the answers

    What type of performance improvement can be achieved by using Intel Distribution for Modin?

    <p>15x over the baseline result</p> Signup and view all the answers

    What is a consequence of deploying workflows on the cloud?

    <p>Lower total cost of ownership</p> Signup and view all the answers

    What is required to achieve performant and efficient AI in the cloud?

    <p>A strategy that focuses on data and AI</p> Signup and view all the answers

    True or false: OneDNL is available on cloud instances?

    <p>True</p> Signup and view all the answers

    What is the purpose of model compression techniques?

    <p>To reduce execution time</p> Signup and view all the answers

    What is the purpose of CPU Power Scaling Governors?

    <p>To scale the CPU frequency up or down to save power or improve performance</p> Signup and view all the answers

    What tools does Intel offer to help speed up the AI workflow?

    <p>Software optimizations and acceleration tools</p> Signup and view all the answers

    What is the main focus of the text?

    <p>Efficient AI</p> Signup and view all the answers

    What is an example of a system level parameter?

    <p>CPU Turbo Boost Technology</p> Signup and view all the answers

    True or false: Special training or exploration is required to use OneDNL and OneDAL?

    <p>False</p> Signup and view all the answers

    What is OneDNN?

    <p>An optimized deep learning library</p> Signup and view all the answers

    What is OneDAL?

    <p>An optimized data analytics library</p> Signup and view all the answers

    What is OneDNL?

    <p>A library that seamlessly powers optimizations of the deep learning components</p> Signup and view all the answers

    What techniques does OneDNL use to optimize deep learning?

    <p>SIMD block execution, vectorization, prefetching, and parallelization</p> Signup and view all the answers

    Where are the OneDNN and OneDAL libraries available?

    <p>On cloud instances</p> Signup and view all the answers

    Study Notes

    • The AI pipeline is a journey that begins with data collection and ends with model training and inference.
    • The majority of the time is spent in data ingestion and feature engineering, while machine learning is only 2% of the time.
    • It is important to look at the AI problem holistically, rather than focus on just the model training or prediction.
    • Achieving performant and efficient AI in the cloud requires a coherent and unified optimization strategy, consisting of data+AI SW acceleration.
    • Intel offers a suite of software optimizations and acceleration tools to help speed up the AI workflow.
    • These include optimizations for the data analytics and optimization tools, as well as the frameworks and libraries used in the AI pipeline.
    • By using these tools, the AI workflow can be streamlined from data ingestion to deployment at scale.
    • This can help reduce costs and increase performance.
    • OneDNN and oneDAL are optimized machine learning libraries that help speed up big data analysis.
    • OneDNN is optimized for deep learning and oneDAL is optimized for data analytics.
    • OneDNL is a library that seamlessly powers optimizations of the deep learning components and frameworks like PyTorch, TensorFlow, and OpenVINO.
    • OneDNL has been optimized with SIMD block execution, vectorization, prefetching, and parallelization across all threads over minibatches.
    • OneDAL is also optimized for deep learning and data analytics.
    • Both libraries are available on cloud instances and are freely available.
    • They are quality libraries that are easy to use and have no special training or exploration required.

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    Description

    Test your knowledge on optimizing AI workflow with Intel's suite of software optimizations and acceleration tools, including OneDNN and oneDAL libraries, which are designed to speed up big data analysis and deep learning. Explore the key concepts in streamlining the AI workflow from data ingestion to deployment at scale.

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