DP-100T01 Designing and Implementing a Data Science Solution on Azure

طبّق علم البيانات وتعلّم الآلة على Azure. خلال أربعة أيام تستخدم مساحة عمل Azure Machine Learning وأدوات المطوّرين

4 أياممتوسط22 وحدةDP-100T01Microsoft Certified: Azure Data Scientist Associate

نظرة عامة

طبّق علم البيانات وتعلّم الآلة على Azure. خلال أربعة أيام تستخدم مساحة عمل Azure Machine Learning وأدوات المطوّرين، وتدرّب النماذج بـ AutoML وبمهام الأوامر، وتتابعها بـ MLflow، وتضبط المعاملات الفائقة، وتبني خطوط المعالجة، وتراجع لوحة Responsible AI، وتنشر النماذج على نقاط نهاية مُدارة عبر الإنترنت، استعداداً لامتحان DP-100.

المنهاج

المحتوى التقني أدناه معروضٌ باللغة الإنجليزية كما ورد في منهاج الدورة، حفاظاً على دقّة المصطلحات.

22 وحدة

Create an Azure Machine Learning workspace
Identify Azure Machine Learning resources
Identify Azure Machine Learning assets
Train models in the workspace

Explore the studio
Explore the Python SDK
Explore the CLI

Understand URIs
Create a datastore
Create a data asset

Choose the appropriate compute target
Create and use a compute instance
Create and use a compute cluster

Understand environments
Explore and use curated environments
Create and use custom environments

Preprocess data and configure featurization
Run an Automated Machine Learning experiment
Evaluate and compare models

Configure MLflow for model tracking in notebooks
Train and track models in notebooks

Convert a notebook to a script
Run a script as a command job
Use parameters in a command job

Track metrics with MLflow
View metrics and evaluate models

Define a search space
Configure a sampling method
Configure early termination
Use a sweep job for hyperparameter tuning

Create components
Create a pipeline
Run a pipeline job

Log models with MLflow
Understand the MLflow model format
Register an MLflow model

Understand Responsible AI
Create the Responsible AI dashboard
Evaluate the Responsible AI dashboard

Explore managed online endpoints
Deploy your MLflow model to a managed online endpoint
Deploy a model to a managed online endpoint
Test managed online endpoints

Understand and create batch endpoints
Deploy your MLflow model to a batch endpoint
Deploy a custom model to a batch endpoint
Invoke and troubleshoot batch endpoints

What is Azure AI Foundry?
How does Azure AI Foundry work
When to use Azure AI Foundry

Explore the language models in the model catalog
Deploy a model to an endpoint
Improve the performance of a language model

Understand the development lifecycle of a large language model (LLM) app
Understand core components and explore flow types
Explore connections and runtimes
Explore variants and monitoring options

Understand how to ground your language model
Make your data searchable
Build an agent with prompt flow

Understand when to fine-tune a language model
Prepare your data to fine-tune a chat completion model
Explore fine-tuning language models in Azure AI Studio

Assess the model performance
Manually evaluate the performance of a model
Assess the performance of your generative AI apps

Plan a responsible generative AI solution
Identify potential harms
Measure potential harms
Mitigate potential harms
Operate a responsible generative AI solution

لمن هذه الدورة

علماء البيانات الذين يستخدمون Python ويريدون تدريب النماذج ونشرها على Azure.

قبل أن تبدأ

Creating cloud resources in Microsoft Azure.
Using Python to explore and visualize data.
Training and validating machine learning models using common frameworks like Scikit-Learn, PyTorch, and TensorFlow.
Working with containers
AI-900T00: Microsoft Azure AI Fundamentals is recommended, or the equivalent experience.

الشهادة

الشهادة التي تؤهّلك لها

بيانات Microsoft وPower BI

Microsoft Certified: Azure Data Scientist Associate

DP-100

دورة واحدة

بيانات Microsoft وPower BI

يومان

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