Tag: Azure

Introduction

Overview of On-Premise and Cloud-Based Data Storage and Analysis Solutions

Understanding Big Data

• Big Data criteria
• Big Data structure
• Working with Big Data

Cloud Solutions

• Azure SQL Database
• Azure Data Warehouse
• Azure Data Factory
• Azure Databricks
• Power BI

Working with Databases

• Data warehouse design
• Dimensional modelling
• Implementation and deployment

Data Models – A Comparison

• SSAS Tabular Data Models
• SSAS Multidimension Models
• Power BI Models

Data Cleansing

• Strategies and tools

Report Models

• Building Power BI tabular models
• Understanding DAX

PowerBI Reports

• Designing Power BI reports

Power BI Architecture

• Workspace generation
• Licensing
• Permissions

Administration

• Administering Azure solutions
• Administering the Power BI Service

Security

• Maintaining a secure Azure architecture
• Azure SQL Database/Data Warehouse, Data Factory and Data Bricks
• Data Masking and Privacy Issues

Duration

14 hours (usually 2 days including breaks)

Requirements

• An understanding of containers (e.g., Docker) and Kubernetes basics
• Experience with the Linux command line

Audience

• Developers
• System Administrators
• DevOps Engineers

Overview

Azure Kubernetes Service (AKS) is a hosted Kubernetes service that simplifies the deployment and management of a Kubernetes cluster in Azure. 

In this instructor-led, live training (online or onsite), participants will learn how to set up and manage a production-scale container environment using Kubernetes on AKS. 

By the end of this training, participants will be able to:

• Configure and manage Kubernetes on AKS.
• Deploy, manage and scale a Kubernetes cluster.
• Deploy containerized (Docker) applications on Azure.
• Migrate an existing Kubernetes environment from on-premise to AKS cloud.
• Integrate Kubernetes with third-party continuous integration (CI) software.
• Ensure high availability and disaster recovery in Kubernetes.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Course Outline

Introduction

• Kubernetes deployment: Azure vs AWS vs on-premise

Overview of Azure Kubernetes Service (AKS) Features and Support

Using the Azure Portal and the Azure CLI (Command Line Interface)

Creating and Uploading a Container Image to the Azure Container Registry

Building A Kubernetes Cluster

Networking Kubernetes Pods

Integrating Kubernetes with Continuous Integration (CI) Tools and Processes

Updating an Application Running in Kubernetes

Integrating Kubernetes with Active Directory

Identity and Security Management in AKS

Advanced Networking

Monitoring a Kubernetes Cluster

Scaling a Kubernetes Cluster

Migrating from On-premise to Azure

Ensuring High Availability and Disaster Recovery in Kubernetes

Troubleshooting

Summary and Conclusion

Duration

28 hours (usually 4 days including breaks)

Requirements

• An understanding of machine learning concepts.
• Knowledge of cloud computing concepts.
• A general understanding of containers (Docker) and orchestration (Kubernetes).
• Some Python programming experience is helpful.
• Experience working with a command line.

Audience

• Data science engineers.
• DevOps engineers interesting in machine learning model deployment.
• Infrastructure engineers interested in machine learning model deployment.
• Software engineers wishing to automate the integration and deployment of machine learning features with their application.

Overview

Kubeflow is a framework for running Machine Learning workloads on Kubernetes. TensorFlow is one of the most popular machine learning libraries. Kubernetes is an orchestration platform for managing containerized applications.

This instructor-led, live training (online or onsite) is aimed at engineers who wish to deploy Machine Learning workloads to Azure cloud.

By the end of this training, participants will be able to:

• Install and configure Kubernetes, Kubeflow and other needed software on Azure.
• Use Azure Kubernetes Service (AKS) to simplify the work of initializing a Kubernetes cluster on Azure.
• Create and deploy a Kubernetes pipeline for automating and managing ML models in production.
• Train and deploy TensorFlow ML models across multiple GPUs and machines running in parallel.
• Leverage other AWS managed services to extend an ML application.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Course Outline

Introduction

• Kubeflow on Azure vs on-premise vs on other public cloud providers

Overview of Kubeflow Features and Architecture

Overview of the Deployment Process

Activating an Azure Account

Preparing and Launching GPU-enabled Virtual Machines

Setting up User Roles and Permissions

Preparing the Build Environment

Selecting a TensorFlow Model and Dataset

Packaging Code and Frameworks into a Docker Image

Setting up a Kubernetes Cluster Using AKS

Staging the Training and Validation Data

Configuring Kubeflow Pipelines

Launching a Training Job.

Visualizing the Training Job in Runtime

Cleaning up After the Job Completes

Troubleshooting

Summary and Conclusion

Master Azure Machine Learning

Have a great intuition of many Azure Machine Learning models

Build an army of powerful Machine Learning models and know how to combine them to solve any problem

Know which Machine Learning model to choose for each type of problem

Make robust Machine Learning models

Requirements

• Basic mathematics level
• Passion for Machine Learning, AI, and Data Science

Description

Are you passionate about Machine Learning and AI? Are you looking to find your first steps into Data Science. This course starts from scratch with Azure Machine Learning and lands in decision trees.

I will walk you through the Azure ML Studio, how to create expirements, how to add datasets, how to add algorithms and predict values.

This course does not cover any coding with R or Python, this will be published in a different course.

Who this course is for:

• Anyone interested in Azure Machine Learning.
• Any people who are not that comfortable with coding but who are interested in Azure Machine Learning and want to apply it easily on datasets.
• Any students in college who want to start a career in Data Science.
• Any data analysts who want to level up in Azure Machine Learning.
• Any people who want to become a Data Scientist.

Course content