In this course you will learn to:

1. Design a governance solution.
2. Design a compute solution.
3. Design an application architecture.
4. Design storage, non-relational and relational.
5. Design data integration solutions.
6. Design authentication, authorization, and identity solutions.
7. Design network solutions.
8. Design backup and disaster recovery solutions.
9. Design monitoring solutions.
10. Design migration solutions.

This course is designed for data scientists with experience of Pythion who need to learn how to apply their data science and machine learning skills on Azure Databricks

Job role: Data Scientist

Day 1

Module 1: Introduction to Azure Databricks

In this module, you will learn how to provision an Azure Databricks workspace and cluster, and use them to work with data.

Lessons

• Getting Started with Azure Databricks
• Working with Data in Azure Databricks

Lab : Getting Started with Azure Databricks

Lab : Working with Data in Azure Databricks

After completing this module, you will be able to:

• Provision an Azure Databricks workspace and cluster
• Use Azure Databricks to work with data

Module 2: Training and Evaluating Machine Learning Models

In this module, you will learn how to use Azure Databricks to prepare data for modeling, and train and validate a machine learning model.

Lessons

• Preparing Data for Machine Learning
• Training a Machine Learning Model

Lab : Training a Machine Learning Model

Lab : Preparing Data for Machine Learning

After completing this module, you will be able to use Azure Databricks to:

• Prepare data for modeling
• Train and validate a machine learning model

Module 3: Managing Experiments and Models

In In this module, you will learn how to use MLflow to track experiments running in Azure Databricks, and how to manage machine learning models.

Lessons

• Using MLflow to Track Experiments
• Managing Models

Lab : Using MLflow to Track Experiments

Lab : Managing Models

After completing this module, you will be able to:

• Use MLflow to track experiments
• Manage models

Module 4: Integrating Azure Databricks and Azure Machine Learning

In this module, you will learn how to integrate Azure Databricks with Azure Machine Learning

Lessons

• Tracking Experiments with Azure Machine Learning
• Deploying Models

Lab : Deploying Models in Azure Machine Learning

Lab : Running Experiments in Azure Machine Learning

After completing this module, you will be able to:

• Run Azure Machine Learning experiments on Azure Databricks compute
• Deploy models trained on Azure Databricks to Azure Machine Learning

Before attending this course, you should have experience of using Python to work with data, and some knowledge of machine learning concepts. Before attending this course, complete the following learning path on Microsoft Learn:

Create machine learning models

• Operating System – macOS, Windows 7 or above
• Putty software to setup SSH connection

Shivam Sharma – Co-founder, TechScalable

Shivam is an author, cloud architect, speaker, and Co-Founder at TechScalable. Being passionate about ever-evolving technology he works on Azure, GCP, Machine Learning & Blockchain. He is also a Microsoft Certified Trainer.

Shivam architects’ solutions on Cloud as well on-premises using a wide array of platforms/technologies. Having core training and consulting experience, he is involved in delivering Azure and Machine Learning training to corporates like BCG, Microsoft, Intuit, RedHat, VMWare, HCL, GE, Applied Materials, Dell, Infosys, IBM, Schneider, L&T, TCS, Capgemini, Mercedes-Benz, Oracle, HP, Wipro, Colt, Cipla, LinkedIn, Mindtree.

He is certified in below:

• Microsoft Certified: Azure Administrator Associate (AZ-104)
• Microsoft Certified: Azure Solutions Architect Expert
• Microsoft Certified Trainer (MCT)
• Microsoft Certified: Azure Data Scientist Associate
• Microsoft Certified: Azure Developer Associate
• Microsoft Certified: Azure DevOps Engineer Expert
• Microsoft Certified: Azure Security Engineer Associate
• Microsoft Certified: Azure AI Engineer Associate
• Microsoft Certified: Azure Fundamentals
• MCSA: Machine Learning – Certified 2018
• MCSE: Cloud Platform and Infrastructure — Certified 2018
• MCSE: Cloud Platform and Infrastructure — Certified 2016
• Google Cloud Certified – Associate Cloud Engineer
  

It will be purely Hands-on and Case study driven training program.

In case the batch is cancelled, the amount would be credited back to the payee’s account in 5 working days.