[Ulasan] Data Science on AWS (Chris Fregly) Diringkaskan.

[Ulasan] Data Science on AWS (Chris Fregly) Diringkaskan.
9Natree Malaysian
[Ulasan] Data Science on AWS (Chris Fregly) Diringkaskan.

Oct 18 2026 | 00:11:53

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Episode • October 18, 2026 • 00:11:53

Show Notes

Data Science on AWS (Chris Fregly)

- Amazon USA Store: https://www.amazon.com/dp/1492079391?tag=9natree-20
- Amazon Worldwide Store: https://global.buys.trade/Data-Science-on-AWS-Chris-Fregly.html

- Apple Books: https://books.apple.com/us/audiobook/data-science-on-aws-leveraging-aws-for-advanced-data/id1753901899?itsct=books_box_link&itscg=30200&ls=1&at=1001l3bAw&ct=9natree

- eBay: https://www.ebay.com/sch/i.html?_nkw=Data+Science+on+AWS+Chris+Fregly+&mkcid=1&mkrid=711-53200-19255-0&siteid=0&campid=5339060787&customid=9natree&toolid=10001&mkevt=1

- Shopee: https://asia.buys.trade/shopee_Data-Science-on-AWS-Chris-Fregly.html
- Lazada: https://asia.buys.trade/lazada_Data-Science-on-AWS-Chris-Fregly.html
- Tiktok: https://asia.buys.trade/tiktok_Data-Science-on-AWS-Chris-Fregly.html

- Baca selanjutnya: https://malaysian.9natree.com/read/1492079391/

#AmazonSageMaker #SaluranMLOpsberterusan #BERTpemprosesanbahasasemulajadi #AmazonKinesisdanKafkaterurus #PengurusanidentitidanaksesAWS #DataScienceonAWS

Data Science on AWS: Implementing End-to-End, Continuous AI and Machine Learning Pipelines ialah buku teknikal oleh Chris Fregly dan Antje Barth, diterbitkan oleh O’Reilly pada tahun 2021. Buku ini berada dalam genre kejuruteraan data, pembelajaran mesin, dan MLOps berasaskan awan. Tujuan utamanya bukan sekadar mengajar cara melatih model, tetapi menerangkan bagaimana sebuah pasukan membawa projek AI daripada data mentah hingga menjadi perkhidmatan yang boleh digunakan dan diselenggara dalam Amazon Web Services, atau AWS. Pendekatannya menumpukan ekosistem AWS, khususnya Amazon SageMaker, bersama perkhidmatan penyimpanan, pemprosesan data, penstriman, keselamatan, dan automasi. Pembaca dibawa melihat rantaian kerja yang saling bergantung, termasuk pengambilan data, penerokaan, penyediaan ciri, latihan, penilaian, pelaksanaan model, serta pemantauan dan pengemaskinian berterusan. Contoh seperti pemprosesan bahasa semula jadi berasaskan BERT, penglihatan komputer, pengesanan penipuan, dan aplikasi perbualan membantu menunjukkan bahawa keputusan seni bina berubah mengikut masalah. Ia ialah rujukan yang padat dan berorientasikan pelaksanaan, bukan pengenalan santai kepada AI.

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