- الصفحة الرئيسية /
- الكتب /
- الكمبيوتر والتكنولوجيا /
- Databases & Big Data /
- Data Processing /
- Machine Learning Platform Engineering: Build ...
0 ratings
منتج #:
176676434
Machine Learning Platform Engineering: Build an internal developer platform for ML and AI systems (From Scratch)
86% من المشترين سيوصون بهذا المنتج لصديق
منتج #:
176676434
IQD 91206
تفاصيل السعر
باستثناء رسوم الشحن والجمارك ( سيتم احتساب رسوم الشحن والجمارك عند إتمام الشراء )
*سيتم استيراد جميع العناصر من أمريكا
0 ratings
اكتب رأيك
متوفر فى المخزون
مستورد من متجر USA
كمية:
اطلب الآن واحصل عليه حول السبت, أكتوبر 10
معاملة آمنة
تعمل يوباي جاهدة لحماية أمنك وخصوصيتك. يضمن نظام أمان الدفع المتقدم لدينا السرية من خلال تشفير معلوماتك أثناء النقل باستخدام بروتوكولات AES (معايير التشفير المتقدمة) وSSL (طبقة المنافذ الآمنة). تفاصيل الدفع الخاصة بك آمنة بنسبة %100 لأننا لا نشارك تفاصيل الدفع الخاصة بك مع بائعين تابعين لجهات خارجية
أفضل شركائنا اللوجستيين
A must-have if you want to learn how to build and deploy ML models from scratch.
شحن
سريع
استرجاع
مجاني*
تغليف آمن
منتجات أصلية %100
الامتثال لمعيار PCI DSS
حاصل على شهادة ISO 27001
Note: Step Down Voltage Transformer required for using electronics products of أمريكا store (110-120). Recommended power converters اشتري الآن.
أبرز ما يلفت الانتباه
Customizable Framework
Build a tailored developer platform suited for your unique machine learning and AI systems, enabling streamlined workflows and integration suitable for specific organizational needs.
Scalability
Facilitate seamless scaling of AI projects from small prototypes to enterprise-level systems, providing flexibility to adapt to growing data and increasing computational demands.
Enhanced Collaboration
Promote teamwork through integrated tools and resources, allowing data scientists, engineers, and stakeholders to collaborate efficiently on machine learning models and projects.
تفاصيل المنتج
| Publisher | Manning Publications |
| Publication date | March 10, 2026 |
| Language | English |
| Print length | 504 pages |
| ISBN-10 | 1633437337 |
| ISBN-13 | 978-1633437333 |
| Item Weight | 13.7 ounces (388.4 grams) |
| Dimensions | 7.38 x 1.26 x 9.25 inches (18.7 x 3.2 x 23.5 cm) |
من يجب أن يشتري؟
-
ML Engineers
Ideal for machine learning engineers seeking to design and manage custom internal platforms for ML and AI projects.
-
Data Scientists
Great for data scientists who want to efficiently deploy models and manage workflows without deep infrastructure knowledge.
-
DevOps Teams
Useful for DevOps teams aiming to integrate ML resources into existing CI/CD pipelines and improve deployment processes.
-
Beginners in ML
Not suitable for beginners who lack foundational knowledge in machine learning, as it requires advanced technical skills.
وصف المنتج
Machine Learning Platform Engineering: Build an internal developer platform for ML and AI systems (From Scratch)
هل لديك أي استفسار؟ تحدث معنا
أسئلة العملاء & الإجابات
-
سؤال:
What is a Machine Learning Platform, and why is it important?
إجابه: A Machine Learning Platform is a software solution that enables developers and data scientists to build, deploy, and manage machine learning models efficiently. Its importance lies in providing a unified environment that simplifies the complexities of data management, model training, and deployment processes. Organizations leverage these platforms to accelerate their AI initiatives, enhance collaboration among teams, and ensure smoother integration with existing systems. For instance, a financial institution might use a Machine Learning Platform to create predictive models for risk assessment. -
سؤال:
How can I build an internal developer platform for ML and AI systems?
إجابه: Building an internal developer platform for ML and AI systems involves several key steps: defining user requirements, choosing the right technologies, and establishing infrastructure. Key considerations include selecting scalable cloud services, data storage solutions, and appropriate tooling for coders and data scientists. Creating such a platform allows teams to foster innovation, reduce timing in project completions, and streamline onboarding of new developers. A tech startup might implement this to facilitate rapid iterations of machine learning models for their products. -
سؤال:
What are the common challenges in Machine Learning Platform Engineering?
إجابه: Challenges in Machine Learning Platform Engineering include managing diverse data sources, ensuring model reproducibility, and scaling infrastructure. These obstacles arise from the complexities of varying data formats, the need for consistent testing environments, and computational resource demands. Addressing these challenges usually involves investing in robust data management solutions and adopting DevOps practices. For example, a healthcare provider may face hurdles in integrating patient data from multiple systems for their ML applications. -
سؤال:
What kind of skills do I need to develop a Machine Learning Platform?
إجابه: Developing a Machine Learning Platform requires a combination of skills in software engineering, data science, and cloud technologies. Proficiency in programming languages such as Python or R, along with an understanding of machine learning algorithms and cloud infrastructure management, is crucial. Additionally, familiarity with DevOps concepts can enhance deployment and maintenance processes. A company looking to create predictive analytics tools might benefit from assembling a diverse team with these skill sets. -
سؤال:
How does Machine Learning Platform help in collaboration among teams?
إجابه: Machine Learning Platforms foster collaboration by providing a centralized hub where data scientists, engineers, and stakeholders can work together on projects. These platforms typically feature tools for version control, shared workspaces, and real-time collaboration features. This collaborative spirit streamlines communication and aligns project goals across departments. For example, a retail company could use the platform for marketing and IT teams to jointly develop customer segmentation models. -
سؤال:
How can I measure the performance of my Machine Learning models?
إجابه: Measuring the performance of Machine Learning models is typically done using key metrics such as accuracy, precision, recall, and F1 score. Depending on the application, you might also consider AUC-ROC curves and confusion matrices to gain deeper insights. Regularly assessing model performance allows for continual improvement and fine-tuning. For example, a financial tech firm may focus on precision to minimize false positives in fraud detection models. -
سؤال:
What role does data play in Machine Learning Platform Engineering?
إجابه: Data is the backbone of Machine Learning Platform Engineering; it fuels model training and impacts the effectiveness of AI systems. High-quality, well-structured data enables accurate model outputs, while poor data can lead to misleading results. Therefore, establishing strong data governance practices is essential. A healthcare startup, for instance, must ensure the accuracy and privacy of patient data to build reliable diagnostics tools using AI. -
سؤال:
What are the advantages of building an internal developer platform versus using a third-party solution?
إجابه: Building an internal developer platform offers advantages such as tailored functionalities, optimized workflows, and improved control over data and processes. Unlike third-party solutions, internal platforms can be customized to meet specific organizational needs, security requirements, and compliance regulations. Companies may find this beneficial for integrating proprietary algorithms. For example, a tech firm focusing on niche AI applications might prefer building its solution to ensure it aligns with its innovative vision. -
سؤال:
Where can I buy Machine Learning Platform Engineering Build An Internal Developer Platform For ML And AI Systems From Scratch in Iraq?
إجابه: You can purchase 'Machine Learning Platform Engineering Build An Internal Developer Platform For ML And AI Systems From Scratch' from Ubuy in Iraq. Ubuy is known for its extensive selection of tech-related books and resources that cater to developers and data science professionals, ensuring you find the right materials to help you build effective internal platforms for your ML and AI needs.
Data Processing Editorial Review
لم يتم العثور على مراجعات تحريرية
مراجعات العملاء وتقييماتهم
1 تقييمات العملاء
-
5 نجمة
100%
-
4 نجمة
0%
-
3 نجمة
0%
-
2 نجمة
0%
-
1 نجمة
0%
أضف تقييم لهذا المنتج
شارك أفكارك مع عملاء آخرين
منصة موثوقة وثقة كاملة للمشتري
JK
Jasmin
مشتري موثّق
“Great products and very good service: very easy and very fast international delivery.”
9 September 2026 · عبر Trustpilot
AG
Anke
مشتري موثّق
“Wonderful online shopping experience, smooth transaction from the start. Payment method works conveniently and delivery is unexpectedly fast and reliable. You go the extra mile for service. What makes this even more amazing, you deliver to Namibia. I will remain a happy Ubuy customer and will increase my purchases for sure! Thank you!”
10 September 2026 · عبر Trustpilot
H
Hazel
مشتري موثّق
“Very easy to find the products what you need, and so fast delivery, that’s why I highly recommended to others costumers to used ubuy.”
10 September 2026 · عبر Trustpilot
O
Opaleye
مشتري موثّق
“I received exactly what I ordered I was skeptical about your site because that was my first time to order. But the order came timely and neatly packaged. I was not disappointed. Thank you.”
11 September 2026 · عبر Trustpilot
AC
Adele
مشتري موثّق
“Easy to find and order what you want on the website. Delivery is quick to the UK”
8 September 2026 · عبر Trustpilot
الخروج الآمن
شحن عالمي
إسترجاع سهل
منتجات أصلية
تاريخ سعر المنتج
معلومات مهمة
- القيود: بالنسبة للمنتجات التي يتم شحنها دولياً، يُرجى ملاحظة أن أي ضمان من الشركة المصنعة قد لا يكون صالحاً؛ قد لا تتوفر خيارات خدمة الشركة المصنعة؛ قد لا تكون أدلة المنتج والتعليمات وتحذيرات السلامة مكتوبة بلغة بلد المقصد؛ قد لا يتم تصميم المنتجات (والمواد المصاحبة لها) وفقاً لمعايير بلد الوجهة والمواصفات ومتطلبات الملصقات؛ وقد لا تتوافق المنتجات مع الجهد الكهربي المستخدم في بلد الوجهة والمعايير الكهربائية الأخرى (تتطلب استخدام محوّل كهربي أو جهاز تحويل إذا كان ذلك مناسباً). المستلم مسؤول عن ضمان إمكانية استيراد المنتج بشكل قانوني إلى بلد الوجهة. عند الطلب من يوباي أو الشركات التابعة لها، يكون المستلم هو المستورد المسجل ويجب أن يلتزم بجميع القوانين واللوائح الخاصة ببلد الوجهة.
- ليست كل المنتجات المدرجة على يوباي معروضة للبيع، لأن يوباي هو محرك بحث عالمي. المنتجات تخضع للوائح التصدير / التجارة.
IQD 91206
اطلب الآن واحصل عليه حول السبت, أكتوبر 10
هذا المنتج غير ممنوع في بلدي. (الرجاء الضغط على الرابط أعلاه إذا لم يكن هذا المنتج ممنوعاً في بلدك ، لذلك سيقوم فريقنا بمراجعته والسماح به.)
كمية:
معاملة آمنة
نوفر لك مدفوعات مشفّرة، وحماية متكاملة للمشتري، مع الالتزام بمعايير PCI DSS وشهادة ISO 27001:2022 لضمان أعلى مستويات الأمان في كل عملية شراء.
أفضل شركائنا اللوجستيين
أسرع توصيل عبر الحدود
المميزات والفوائد
- Learn to design and operate a production-ready machine learning platform.
- Hands-on projects such as image classifiers and recommendation systems.
- Master MLOps fundamentals like orchestration and deployment.
- Use real open-source tools like Kubeflow and MLflow.
- Streamline workflows and enhance collaboration between teams.
- Achieve automation and improve model deployment efficiency.
ضمان Ubuy
تسوّق بثقة مع منتجات أصلية %100، ومدفوعات آمنة متوافقة مع معيار PCI DSS، وحماية بيانات معتمدة وفق ISO 27001، وشحن دولي سريع، وإرجاع مجاني*، وتغليف آمن لكل طلب.