BaDshaH
10-08-2023, 02:15 PM
https://i.postimg.cc/RhhVtV4G/Screen-Shot-2023-05-04-at-2-10-07-PM.png
Released 10/2023
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 25h 55m | Size: 4.32 GB
Become a Machine Learning Engineer. Level-up your programming skills with MLOps
What you'll learn
Master Python fundamentals, MLOps principles, and data management to build and deploy ML models in production environments.
Utilize Amazon Sagemaker / AWS, Azure, MLflow, and Hugging Face for end-to-end ML solutions, pipeline creation, and API development.
Fine-tune and deploy Large Language Models (LLMs) and containerized models using the ONNX format with Hugging Face.
Design a full MLOps pipeline with MLflow, managing projects, models, and tracking system features.
Skills you'll gain
Data Management
Devops
MLOps
Machine Learning
Github
Python Programming
Data Analysis
Microsoft Azure
Big Data
Amazon Web Services (Amazon AWS)
Cloud Computing
Rust Programming
This comprehensive course series is perfect for individuals with programming knowledge such as software developers, data scientists, and researchers. You'll acquire critical MLOps skills, including the use of Python and Rust, utilizing GitHub Copilot to enhance productivity, and leveraging platforms like Amazon SageMaker, Azure ML, and MLflow. You'll also learn how to fine-tune Large Language Models (LLMs) using Hugging Face and understand the deployment of sustainable and efficient binary embedded models in the ONNX format, setting you up for success in the ever-evolving field of MLOps
Through this series, you will begin to learn skills for various career paths
1. Data Science - Analyze and interpret complex data sets, develop ML models, implement data management, and drive data-driven decision making.
2. Machine Learning Engineering - Design, build, and deploy ML models and systems to solve real-world problems.
3. Cloud ML Solutions Architect - Leverage cloud platforms like AWS and Azure to architect and manage ML solutions in a scalable, cost-effective manner.
4. Artificial Intelligence (AI) Product Management - Bridge the gap between business, engineering, and data science teams to deliver impactful AI/ML products.
Applied Learning Project
Explore and practice your MLOps skills with hands-on practice exercises and Github repositories.
1. Building a Python script to automate data preprocessing and feature extraction for machine learning models.
2. Developing a real-world ML/AI solution using AI pair programming and GitHub Copilot, showcasing your ability to collaborate with AI.
4. Creating web applications and command-line tools for ML model interaction using Gradio, Hugging Face, and the Click framework.
3. Implementing GPU-accelerated ML tasks using Rust for improved performance and efficiency.
4. Training, optimizing, and deploying ML models on Amazon SageMaker and Azure ML for cloud-based MLOps.
5. Designing a full MLOps pipeline with MLflow, managing projects, models, and tracking system features.
6. Fine-tuning and deploying Large Language Models (LLMs) and containerized models using the ONNX format with Hugging Face. Creating interactive demos to effectively showcase your work and advancements.
Homepage
https://www.coursera.org/specializations/mlops-machine-learning-duke
https://i.postimg.cc/vmLsvJBf/url.png
Download From Rapidgator
https://rapidgator.net/file/7ac82bac24a8ba9bcbd054869800015c
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Download From Nitroflare
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Released 10/2023
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 25h 55m | Size: 4.32 GB
Become a Machine Learning Engineer. Level-up your programming skills with MLOps
What you'll learn
Master Python fundamentals, MLOps principles, and data management to build and deploy ML models in production environments.
Utilize Amazon Sagemaker / AWS, Azure, MLflow, and Hugging Face for end-to-end ML solutions, pipeline creation, and API development.
Fine-tune and deploy Large Language Models (LLMs) and containerized models using the ONNX format with Hugging Face.
Design a full MLOps pipeline with MLflow, managing projects, models, and tracking system features.
Skills you'll gain
Data Management
Devops
MLOps
Machine Learning
Github
Python Programming
Data Analysis
Microsoft Azure
Big Data
Amazon Web Services (Amazon AWS)
Cloud Computing
Rust Programming
This comprehensive course series is perfect for individuals with programming knowledge such as software developers, data scientists, and researchers. You'll acquire critical MLOps skills, including the use of Python and Rust, utilizing GitHub Copilot to enhance productivity, and leveraging platforms like Amazon SageMaker, Azure ML, and MLflow. You'll also learn how to fine-tune Large Language Models (LLMs) using Hugging Face and understand the deployment of sustainable and efficient binary embedded models in the ONNX format, setting you up for success in the ever-evolving field of MLOps
Through this series, you will begin to learn skills for various career paths
1. Data Science - Analyze and interpret complex data sets, develop ML models, implement data management, and drive data-driven decision making.
2. Machine Learning Engineering - Design, build, and deploy ML models and systems to solve real-world problems.
3. Cloud ML Solutions Architect - Leverage cloud platforms like AWS and Azure to architect and manage ML solutions in a scalable, cost-effective manner.
4. Artificial Intelligence (AI) Product Management - Bridge the gap between business, engineering, and data science teams to deliver impactful AI/ML products.
Applied Learning Project
Explore and practice your MLOps skills with hands-on practice exercises and Github repositories.
1. Building a Python script to automate data preprocessing and feature extraction for machine learning models.
2. Developing a real-world ML/AI solution using AI pair programming and GitHub Copilot, showcasing your ability to collaborate with AI.
4. Creating web applications and command-line tools for ML model interaction using Gradio, Hugging Face, and the Click framework.
3. Implementing GPU-accelerated ML tasks using Rust for improved performance and efficiency.
4. Training, optimizing, and deploying ML models on Amazon SageMaker and Azure ML for cloud-based MLOps.
5. Designing a full MLOps pipeline with MLflow, managing projects, models, and tracking system features.
6. Fine-tuning and deploying Large Language Models (LLMs) and containerized models using the ONNX format with Hugging Face. Creating interactive demos to effectively showcase your work and advancements.
Homepage
https://www.coursera.org/specializations/mlops-machine-learning-duke
https://i.postimg.cc/vmLsvJBf/url.png
Download From Rapidgator
https://rapidgator.net/file/7ac82bac24a8ba9bcbd054869800015c
https://rapidgator.net/file/e5a1056d7d6abede6e85b07e3b7b0f9e
https://rapidgator.net/file/657a329e70c123da0a760f57034b7abb
https://rapidgator.net/file/56c962a4dd1a44bf752501c534f2d82b
https://rapidgator.net/file/bcce369d352a6a27ec7668b5e02c0c2f
Download From Nitroflare
https://nitroflare.com/view/9AED323F9E9892F
https://nitroflare.com/view/2B01B7D9ABAA91D
https://nitroflare.com/view/FCD557A9EF7CE7D
https://nitroflare.com/view/14EAE65A8DD4355
https://nitroflare.com/view/CB2DF97770A47E0
Download From DDownload
https://ddownload.com/zga1vazx9fdh
https://ddownload.com/jifsyy7sfyqa
https://ddownload.com/lt64iogyznhs
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