• Upload

linkedin - Microsoft Machine Learning Operations (MLOps) Engineer Associate

39
0
2026-10-23 00:05:27
1
Coin
2
1
https://www.linkedin.com/learning/microsoft-machine-learning-operations-mlops-engineer-associate-ai-300-cert-prep-by-microsoft-press linkedin - Microsoft Machine Learning Operations (MLOps) Engineer Associate (AI-300) Cert Prep by Microsoft Press 2026-10
AI-300
lmt831 Send message
Video playlist
（1/76）
01. Course welcome
01:40
01. Module 1 Design and implement MLOps infrastructure
00:26
02. Learning objectives
00:18
03. Create and manage a workspace
19:41
04. Create and manage datastores
16:15
05. Create and manage compute targets
16:22
06. Configure identity and access management for workspaces
19:10
01. Learning objectives
00:16
02. Create and manage data assets
16:17
03. Create and manage environments
10:41
04. Create and manage components
10:08
01 Learning objectives
00:21
02. Configure GitHub integration with machine learning
11:43
03. Deploy Machine Learning workspaces and resources
09:35
04. Restrict network access to machine learning workspaces
12:19
05. Manage source control for machine learning projects
07:17
01. Module 2 Manage the machine learning model lifecycle
00:23
02 Learning objectives
00:25
03. Configure experiment tracking
07:23
04. Use automated machine learning to explore optimal models
15:30
05. Use notebooks for experimentation and exploration
13:23
06. Automate hyperparameter tuning
11:33
07. Run model training scripts
11:50
08. Manage distributed training for large models
08:37
09. Implement training pipelines
06:29
10. Compare model performance across jobs in Azure ML
11:44
01.Learning objectives
00:23
02. Package feature retrieval specs with model artifacts
08:00
03. Register an MLflow model
09:35
04. Evaluate a model by using responsible AI principles
07:19
05. Manage model lifecycle
06:51
01 Learning objectives
00:22
02. Deploy models with Azure machine learning endpoints
06:04
03. Troubleshoot model endpoints
06:00
04. Implement progressive rollout and safe rollback strategies
09:04
01. Learning objectives
00:20
02. Detect and analyze data drift
09:33
03. Monitor performance metrics of models
07:08
04. Configure retraining or alert triggers
05:50
01. Module 3 Design and implement GenAIOps infrastructure
00:23
02 Learning objectives
00:27
03. Create and configure Foundry resources
12:52
04. Configure identity and access management
07:26
05. Secure Microsoft Foundry networks and private access
08:09
06. Deploy infrastructure using Bicep templates and Azure CLI
08:20
01 . Learning objectives
00:25
02. Deploy foundation models
06:03
03. Select the right Azure AI Foundry models
06:58
04. Version models and plan production deployments
05:29
05. Configure provisioned throughput units
05:02
01. Learning objectives
00:22
02. Version Prompts with Prompty, GitHub, and Azure AI Foundry
05:37
03. Create prompt variants and compare performance
04:17
01. Module 4 Implement GenAI quality and observability
00:23
02 . Learning objectives
00:28
03. Create test datasets and data mapping for model evaluation
08:26
04. Implement AI quality metrics
04:49
05. Configure risk and safety evaluations
08:42
06. Set up automated evaluation workflows
05:16
01. Learning objectives
00:25
02. Examine continuous monitoring in Microsoft Foundry
08:01
03. Monitor performance metrics
06:29
04. Track and optimize cost metrics
09:24
05. Configure detailed logging, tracing, and debugging
06:34
01. Module 5 Optimize GenAI systems and model performance
00:21
02. Learning objectives
00:31
03. Optimize RAG retrieval
05:56
04. Select and fine-tune embedding models
05:50
05. Implement and optimize hybrid search
04:37
06. Evaluate and improve RAG systems
07:16
01 . Learning objectives
00:25
02. Design and implement advanced fine-tuning methods
06:08
03. Create and manage synthetic data for fine-tuning
07:46
04. Monitor and optimize fine-tuned model performance
05:56
05. Manage a fine-tuned model
06:13
01. Next steps and summary
00:26

Udemy - The Complete Full-Stack Web Development Bootcamp part1
lmt831
9276 2

NeetCode.io - System Design for Beginners
lmt831
9397 2

Udemy - Excel for Accounting & Finance part1
lmt831
28 0

Udemy – AI Engineer Core Track: LLM Engineering, RAG, QLoRA, Agents part1
lmt831
8727 3

Goodbye, Microsoft Copilot
ExplorerExec
95.8K 20

Current status of Microsoft's human verification
喜爱科技的老孙
76.3K 11

Hello Interview - System Design in a Hurry
lmt831
8191 0

Udemy - Unreal Engine 5 C++ The Ultimate Game Developer Course part1
lmt831
8877 1

Udemy - Angular - The Complete Guide (2025 Edition) part1
lmt831
9058 0

Udemy - Excel for Accounting & Finance part2
lmt831
19 0

【Original English Audio with Chinese & English Subtitles】MIT How to AI (Almost) Anything, Spring 2025
常青藤名校课
10.5K 3

Why does your AI resume go unanswered? It's not theory you lack—it's Agent engineering skills
码农小蟹
182.8K 33

An AI career test that silences programmers across the internet: in the AI era, the most endangered profession might just be programmers.
码农小蟹
929.9K 543

NeetCode - Algorithms & Data Structures for Beginners
lmt831
21.3K 2

[AI Trainer] From Zero Basics to Practical Combat - A Must-Watch for Beginners
小鹿线-老张
144.2K 98

How to Become an AI Engineer? What Are the Three Core Technologies You Must Master?
AI知学长
1.5M 416

Breaking: Microsoft Cloud Services experience global outage!
赛博马克CyberMark
132.3K 28

HR Efficiency Practice: Building an Exclusive Employee Growth Coach with Amazon Quick
亚马逊云科技
27.3K 0

Udemy - Ultimate System Administrator Course part7
lmt831
33 0

Entering the AI track, rewriting the career ceiling for programmers! A video reveals the 4 key stages of transitioning to AI!!!
程序员小蟹
70.8K 75
Expand
秋日萌宠动物园！
Customer Service
Top
赛事库 课堂 2021拜年纪