Understand and learn the skills needed to use modern tools in Microsoft Azure. This book discusses how to practically apply these tools in the industry, and help drive the transformation of organizations into a knowledge and data-driven entity. It provides an end-to-end understanding of data science life cycle and the techniques to efficiently productionize workloads.
The book starts with an introduction to data science and discusses the statistical techniques data scientists should know. You''ll then move on to machine learning in Azure where you will review the basics of data preparation and engineering, along with Azure ML service and automated machine learning. You''ll also explore Azure Databricks and learn how to deploy, create and manage the same. In the final chapters you''ll go through machine learning operations in Azure followed by the practical implementation of artificial intelligence through machine learning.
Table of Contents
Part I - Introduction to Data Science and its rise to prominence
Chapter 1 Data Science in the modern enterprise
What is Data Science
The Data Scientists' tools and lingo
Ethics and ethical AI
Significance of Data Science in organizations
Case Studies of applied Data Science
Chapter 2 Most important Statistical Tehniques in Data Science
Top Statistical Tehniques Data Scientists need to know
Supervised Learning
Unsupervised Learning
Regression/Classification/ Forecasting
Bayesian method
Time series analysis
Linear regression
Sampling methods
Reinforcement Learning
Part 2 - Machine Learning in Microsoft Azure
Chapter 3 Basics of data preparation and data engineering Ingesting disparate data sources
Preparing data for analysis
Data Exploration
Feature Engineering
Chapter 4 Introducing Azure Machine Learning AzureML- DataStores/ Datasets
Azure ML Compute/CLusters/inference/batch-realtime
Azure ML Service- Training and Building
Azure ML Service- Deploying
Azure ML Service- Pipelines
Azure ML Studio/Designer
Azure Automated Machine Learning (AutoML)
Hyperparameter Training
Azure ML- Security
Case Study
Part 3 - Azure Databricks
Chapter 5 Spark and Big Data
Spark and Hadoop
What is Big Data?
Why Spark is the platform of choice for Big Data
Challenges with Big Data
Chapter 6 Azure Databricks Basics
What is Azure Databricks
Azure Databricks from the Data Engineers' perspective
Azure Databricks from the Data Scientists' perspective
Chapter 7 Azure Databricks
Deploying the Azure Databricks workspace
Creating and Managing Clusters
Creating and managing users and groups
Managing Databricks Notebooks
Using Databricks Notebooks
DBFS
Connecting to ADLS
Sample Notebook(s)
Part 4 - Operationalizing Data Science
Chapter 8 Machine Learning Operations
Operationalization concepts and DevOps
MLOps in Azure
MLFlow in Azure Databricks
Git
Chapter 9 Practical ML
Introducing use cases in the different industries
Democratizing AI through ML
