Microsoft Azure DP-100

Excel in Azure Machine Learning with DP-100 Course by Tech Cryptors'

Microsoft Azure DP-100

Tech Cryptors provide unparalleled customer satisfaction and exceptional value for clients. 

Embark on a journey of mastering Azure Machine Learning with Tech Cryptors’ Microsoft Azure DP-100 course. Learn to harness the power of machine learning models, manage Azure ML workspaces, and implement robust data processing pipelines. Gain expertise in creating, training, and evaluating models, enabling you to make informed decisions and drive innovation using Azure’s advanced machine learning capabilities. Our cutting-edge courses are designed to equip aspiring professionals with practical skills and knowledge to excel in the dynamic world of networking.

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Learn Compute Specifications and Model Deployment in Microsoft Azure DP-100

Compute Specification for Training: Determine appropriate compute specifications for training workloads.

Model Deployment Requirements: Describe the necessary requirements for deploying models.

Development Approach Selection: Choose the suitable development approach for building or training models.

Learn to Manage Azure Machine Learning Workspace in Microsoft Azure DP-100

Creating a Workspace: Set up and create an Azure Machine Learning workspace.

Workspace Interaction: Manage the workspace using developer tools.

Git Integration: Configure Git integration for source control.

Data Management in Azure ML Workspace

Azure Storage Resources: Select and utilize Azure Storage resources.

Datastore Registration: Register and maintain datastores.

Data Asset Management: Create and manage data assets.

Compute Targets: Create compute targets for experiments and training.

Environment Selection: Choose the right environment for machine learning use cases.

Attached Compute Resources: Configure attached compute resources, including Apache Spark pools.

Microsoft Azure DP-100
Yes. Tech Cryptors is ISO 9001:2015 CERTIFIED COMPANY under IAF. Also, we are in collaboration with Shastra INDIAN INSTITUTES TECHNOLOGY Madras. CERTIFICATION will have the significance of the above.
We conduct this course in BOTH online and offline modes. You can choose the mode suitable for you, even you can SWITCH modes for a couple of lectures in case of any personal issues and emergencies.
In one batch we allow MAXIMUM 4 students Because we believe that to have a better understanding and to excel in this course PERSONAL ATTENTION is needed.
We have new batches starting every 15 days, But you should register asap for your desired date because our batches are filling fast. Batch timings are also kept AS PER YOUR TIME CONVENIENCE, we don’t have any rigid preassigned slots.
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Compute Utilization Monitoring: Monitor compute utilization during experiments.

Learn about Interactive Data Exploration and Wrangling

Interactive Data Wrangling: Perform interactive data wrangling, including Apache Spark usage.

Data Exploration with Data Assets: Access and explore data using data assets and data stores.

Learn Model Creation with Azure ML Designer

Training Pipeline Creation: Build a training pipeline.

Data Asset Consumption: Consume data assets within the designer.

Custom Code Components: Utilize custom code components within the designer.

Model Evaluation and Responsible AI: Evaluate the model and follow responsible AI guidelines.

Understanding Automated Machine Learning in Microsoft Azure DP-100

Tabular Data Automation: Use automated machine learning for tabular data.

Computer Vision Automation: Utilize automated machine learning for computer vision tasks.

NLP Automation: Apply automated machine learning for natural language processing.

Training Option Selection: Choose training options, including preprocessing and algorithms.

Evaluation and Responsible AI: Evaluate automated machine learning runs responsibly.

Get to know about Custom Model Training with Notebooks in Microsoft Azure DP-100

Compute Instance Development: Develop code using a compute instance.

MLflow for Tracking: Track model training using MLflow.

Model Evaluation: Evaluate a model's performance.

Python SDKv2 Model Training: Train a model using Python SDKv2.

Terminal for Compute Configuration: Use the terminal to configure a compute instance.

Learn about Hyperparameter Tuning with Azur in Microsoft Azure DP-100e ML

Sampling Method Selection: Choose an appropriate sampling method.

Search Space Definition: Define the search space for hyperparameter tuning.

Primary Metric Definition: Specify the primary metric for tuning.

Early Termination Options: Define early termination criteria.

Understanding about Running Model Training Scripts in Microsoft Azure DP-100

Job Run Settings: Configure settings for running model training scripts.

Compute Configuration: Configure compute for job runs.

Data Consumption in Jobs: Consume data from data assets in job runs.

Azure ML Script Execution: Run a script as a job using Azure Machine Learning.

MLflow Metrics Logging: Log metrics from job runs using MLflow.

Troubleshooting with Logs: Use logs to troubleshoot job run errors.

Environment Configuration: Configure environments for job runs.

Parameter Definition: Define parameters for job runs.

Learn to Implement Training Pipelines in Microsoft Azure DP-100

Pipeline Creation: Create a pipeline for orchestrating training processes.

Data Passing in Pipelines: Pass data between steps in a pipeline.

Pipeline Execution and Scheduling: Run and schedule pipelines.

Pipeline Run Monitoring: Monitor the execution of pipeline runs.

Custom Component Creation: Build custom components for pipelines.

Component-Based Pipelines: Design pipelines using component-based architecture.

Learn Model Management in Azure ML in Microsoft Azure DP-100

MLflow Model Output: Understand MLflow model output.

Framework for Model Packaging: Identify suitable frameworks for packaging models.

Responsible AI Assessment: Evaluate models based on responsible AI guidelines.

Learn Model Deployment and MLOps Practices in Microsoft Azure DP-100

Online Deployment Configuration: Configure settings for online model deployment.

Batch Deployment Compute: Set up compute for batch deployment.

Online Model Endpoint Deployment: Deploy models to online endpoints.

Batch Model Endpoint Deployment: Deploy models to batch endpoints.

Testing Online Deployed Services: Test online deployed models.

Batch Endpoint Invocation: Invoke batch endpoints for batch scoring.

Applying MLOps Practices: Implement MLOps practices, including Azure DevOps and GitHub integration.

Automated Retraining: Automate model retraining based on data changes.

Event-Based Retraining: Define triggers for event-based model retraining.

Note For Certification

TECH CRYPTORS is an ISO 9001:2015 CERTIFIED COMPANY, also in COLLABORATION with shaastra, INDIAN INSTITUTE OF TECHNOLOGY, madras. Certification will be with Significance of above for attended TRAINING COURSE. Also, every certificate will have a Unique Certificate Number, which will be helpful WORLDWIDE to verify the validity of every individual certificate using www.techcryptors.com.

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