Implementing a Data Science Solution

Designing and Implementing a Data Science Solution on Azure

DP-100: Designing and Implementing a Data Science Solution on Azure, expertise in applying data science and machine learning to implement and run machine learning workloads on Azure. Your responsibilities for this role include: Designing and creating a suitable working environment for data science workloads, Exploring data, Training machine learning models, Implementing pipelines, Running jobs to prepare for production, Managing, deploying, and monitoring scalable machine learning solutions.

1. Design a machine learning solution

Determine the appropriate compute specifications for a training workload
Describe model deployment requirements
Select which development approach to use to build or train a model

2. Manage an Azure Machine Learning workspace

Create an Azure Machine Learning workspace
Manage a workspace by using developer tools for workspace interaction
Set up Git integration for source control
Create and manage registries

3. Manage data in an Azure Machine Learning workspace

Select Azure Storage resources
Register and maintain datastores
Create and manage data assets

4. Manage compute for experiments in Azure Machine Learning

Create compute targets for experiments and training
Select an environment for a machine learning use case
Configure attached compute resources, including Apache Spark pools
Monitor compute utilization

5. Explore data by using data assets and data stores

Access and wrangle data during interactive development
Wrangle interactive data with Apache Spark

6. Create models by using the Azure Machine Learning designer

Create a training pipeline
Consume data assets from the designer
Use custom code components in designer
Evaluate the model, including responsible AI guidelines

7. Use automated machine learning to explore optimal models

Use automated machine learning for tabular data
Use automated machine learning for computer vision
Use automated machine learning for natural language processing
Select and understand training options, including preprocessing and algorithms
Evaluate an automated machine learning run, including responsible AI guidelines

8. Use notebooks for custom model training

Develop code by using a compute instance
Track model training by using MLflow
Evaluate a model
Train a model by using Python SDKv2
Use the terminal to configure a compute instance

9. Tune hyperparameters with Azure Machine Learning

Select a sampling method
Define the search space
Define the primary metric
Define early termination options

10. Run model training scripts

Configure job run settings for a script
Configure compute for a job run
Consume data from a data asset in a job
Run a script as a job by using Azure Machine Learning
Use MLflow to log metrics from a job run
Use logs to troubleshoot job run errors
Configure an environment for a job run
Define parameters for a job

11. Implement training pipelines

Create a pipeline
Pass data between steps in a pipeline
Run and schedule a pipeline
Monitor pipeline runs
Create custom components
Use component-based pipelines

12. Manage models in Azure Machine Learning

Describe MLflow model output
Identify an appropriate framework to package a model
Assess a model by using responsible AI guidelines

13. Deploy a model

Configure settings for online deployment
Configure compute for a batch deployment
Deploy a model to an online endpoint
Deploy a model to a batch endpoint
Test an online deployed service
Invoke the batch endpoint to start a batch scoring job

14. Apply machine learning operations (MLOps) practices

Trigger an Azure Machine Learning job, including from Azure DevOps or GitHub
Automate model retraining based on new data additions or data changes
Define event-based retraining triggers

Fees Structure :	8900 INR / 108 USD
Total No of Class :	61 Video Class
Class Duration :	28:30 Working Hours
Download Feature :	Download Avalable
Technical Support :	Call / Whatsapp : +91 8680961847
Working Hours :	Monday to Firday 9 AM to 6 PM
Payment Mode :	Credit Card / Debit Card / NetBanking / Wallet (Gpay/Phonepay/Paytm/WhatsApp Pay)

Brochure Buy Now Sample Demo

Fees Structure :	10000 INR / 120 USD
Class Duration :	30 Days
Class Recording :	Live Class Recording available
Class Time :	Monday to Firday 1.5 hours per day / Weekend 3 Hours per day
Technical Support :	Call / Whatsapp : +91 8680961847
Working Hours :	Monday to Firday 9 AM to 6 PM
Payment Mode :	Credit Card / Debit Card / NetBanking / Wallet (Gpay/Phonepay/Paytm/WhatsApp Pay)

Download Brochure Pay Online