Data and AI (Azure Databricks) from Udemy

Unlock the transformative potential of Azure Databricks with this comprehensive course on Data Analytics, Machine Learning, Deep Learning, and Generative AI. Designed for professionals and enthusiasts, this course equips you with cutting-edge skills to harness the power of Apache Spark and seamlessly integrate it with Azure Databricks for data-driven solutions.

Azure Databricks, a unified analytics platform, is revolutionizing how businesses analyze data and deploy machine learning at scale. This course dives deep into its robust features, helping you process large datasets with Apache Spark and extract meaningful insights in real time.

You will explore the full spectrum of machine learning and deep learning applications, from predictive modeling to advanced neural networks. Learn to train, optimize, and deploy models effectively while mastering Databricks’ MLflow for seamless model management and tracking.

But we don’t stop there. The course also delves into Generative AI, the forefront of AI innovation. Understand how to leverage generative models to create text, images, and synthetic data for diverse use cases. From foundational concepts to advanced implementations, you'll learn to build intelligent systems that think, learn, and generate.

Key highlights of the course:

Comprehensive introduction to Azure Databricks and its architecture.
Real-time data analytics with Apache Spark.
Building scalable machine learning pipelines.
Deep learning fundamentals and applications in Azure.
Harnessing Generative AI for innovative solutions.
Hands-on projects and real-world scenarios for practical understanding.

Whether you're a data engineer, machine learning practitioner, or an AI enthusiast, this course is your gateway to mastering modern data science and artificial intelligence with Azure Databricks. Join now and elevate your career to the next level.

What's inside

Learning objectives

Data analytics with apache spark in azure databricks
Machine learning with azure databricks
Deep learning with azure databricks

Generative ai with azure databricks
Implementation of complex concepts like rag, hyperparameter tuning, model serving with azure databricks

Data analytics with apache spark in azure databricks
Machine learning with azure databricks
Deep learning with azure databricks
Generative ai with azure databricks
Implementation of complex concepts like rag, hyperparameter tuning, model serving with azure databricks

Syllabus

Introduction

Course Introduction

Important Concepts

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Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Provides hands-on labs using Azure Databricks, which allows learners to gain practical experience with this unified analytics platform

Covers generative AI, which is a cutting-edge field with applications in creating text, images, and synthetic data for various use cases

Explores the integration of Microsoft Fabric with Azure Databricks, which may be useful for those working within the Microsoft ecosystem

Requires learners to deploy a Databricks workspace and compute cluster, which may require an Azure subscription and incur costs

Teaches MLflow for model management and tracking, which is an open-source platform to streamline machine learning development

Includes labs on building agents with external APIs, which may require learners to create accounts and manage API keys

Reviews summary

Practical data and ai on azure databricks

According to learners, this course provides a solid and comprehensive introduction to Data and AI concepts specifically using Azure Databricks. Students frequently praise the numerous hands-on labs and demos as being very practical and helpful for understanding complex topics. The course covers a wide range of subjects, including Spark data analytics, Machine Learning, Deep Learning, and especially cutting-edge Generative AI techniques like RAG and agents. While some sections on foundational concepts might feel brief, the course is considered highly relevant for professionals looking to implement AI solutions on the Azure platform.

Highly relevant skills for industry application.

"This course is highly relevant for anyone working with Azure and Databricks in a professional capacity."

"The skills learned are directly applicable to real-world data and AI projects."

"Great course to upgrade skills for industry requirements."

"Helped me understand how to leverage Databricks for enterprise AI solutions."

Strong focus on modern Generative AI concepts.

"The sections on Generative AI, especially RAG and Agents, were very timely and relevant."

"Learned a lot about implementing Azure OpenAI and RAG using Databricks."

"The Generative AI part is definitely a highlight, covering prompt engineering and embeddings."

"Explaining complex concepts like RAG and agents practically is a major plus."

Covers a broad range of relevant Data & AI topics.

"This course covers a lot of ground, from Spark to ML/DL and Generative AI."

"I appreciate the breadth of topics covered, including modern GenAI techniques like RAG."

"It provides a great overview of the capabilities of Azure Databricks across data science workflows."

"From basic analytics to complex Generative AI agents, it touches upon key areas."

Frequent praise for practical, hands-on labs.

"The labs are really hands-on and provide practical experience."

"The hands-on sections with Databricks were incredibly valuable and helped solidify my understanding."

"I really liked the hands-on labs part which made me understand everything very clearly."

"There are a lot of practical examples and labs which help put theory into practice."

Azure platform changes require constant updates.

"As with any cloud course, keeping up with Azure Databricks updates is crucial; ensure content stays current."

"The pace of change in Azure/AI means some labs might need slight adjustments over time."

"Requires occasional updates to keep pace with platform evolution."

Some topics might feel too brief or introductory.

"Some foundational concepts were covered a bit quickly, assuming prior knowledge."

"While comprehensive, the depth on certain ML/DL algorithms could be expanded."

"Felt like some sections were more of an overview than a deep dive."

"Could use more in-depth coverage on certain Spark optimization techniques."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Data and AI (Azure Databricks) with these activities:

Review Apache Spark Fundamentals

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Solidify your understanding of Apache Spark fundamentals to better grasp its implementation within Azure Databricks.

Browse courses on Apache Spark

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Review the core concepts of Spark architecture.
Practice writing basic Spark applications using RDDs and DataFrames.
Familiarize yourself with Spark SQL syntax.

Review 'Learning Spark, 2nd Edition'

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Gain a deeper understanding of Apache Spark concepts and best practices.

View Learning Spark: Lightning-Fast Data Analytics on Amazon

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Read the chapters relevant to data analytics and machine learning.
Work through the code examples provided in the book.
Compare the book's examples with the Azure Databricks implementation.

Review 'Deep Learning with Python'

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Enhance your understanding of deep learning concepts and their practical implementation.

View Deep Learning with Python, Second Edition on Amazon

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Read the chapters on convolutional neural networks and recurrent neural networks.
Implement the code examples using Keras in Azure Databricks.
Experiment with different hyperparameters and architectures.

Four other activities

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Experiment with Prompt Engineering Techniques

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Improve your prompt engineering skills by experimenting with different techniques and analyzing their impact on the output of Generative AI models.

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Explore different prompt engineering techniques, such as chain-of-thought prompting.
Experiment with these techniques using Azure OpenAI.
Analyze the results and document your findings.

Build a Data Pipeline with Delta Live Tables

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Apply your knowledge of Delta Live Tables to build a complete data pipeline, reinforcing your understanding of data ingestion, transformation, and storage.

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Design a data pipeline using the Medallion Architecture.
Implement the pipeline using Delta Live Tables in Azure Databricks.
Monitor the pipeline's performance and troubleshoot any issues.

Create a Blog Post on Generative AI Use Cases

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Solidify your understanding of Generative AI by researching and writing about its various applications in different industries.

Show steps

Research different use cases of Generative AI.
Choose a specific use case and write a detailed blog post about it.
Include code examples and visualizations to illustrate your points.

Develop a Model Serving Endpoint

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Master model serving by deploying a machine learning model to a production endpoint using Azure Databricks Model Serving.

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Train a machine learning model using Azure Databricks.
Register the model with MLflow.
Deploy the model to a Model Serving endpoint.
Test the endpoint with sample data.

Career center

Learners who complete Data and AI (Azure Databricks) will develop knowledge and skills that may be useful to these careers:

Generative AI Engineer

Generative AI Engineers are responsible for designing, building, and deploying generative AI models. This course offers a specialized focus on Generative AI within Azure Databricks, perfectly aligning with the career interests for this role. The course content covers building generative models to create diverse content, equipping learners with the tools to innovate across various applications. The practical labs, including chatbot development and the implementation of Retrieval Augmented Generation, will enable you to translate theoretical concepts into real-world applications for Generative AI.

See salaries and explore the career path for Generative AI Engineer

Machine Learning Engineer

The machine learning engineer focuses on developing, deploying, and maintaining machine learning models. This course directly aligns with the responsibilities of a machine learning engineer by providing in-depth knowledge of machine learning and deep learning using Azure Databricks. You will learn to train, optimize, and deploy models effectively, and master MLflow for model management and tracking. The course's coverage of hyperparameter tuning and AutoML also significantly enhances the ability to build high-performance machine learning pipelines. By learning these skills, you can effectively implement machine learning solutions at scale.

See salaries and explore the career path for Machine Learning Engineer

AI Developer

An artificial intelligence developer designs and implements AI-powered applications. This course is tailored for aspiring AI developers, offering a deep dive into generative AI and its applications using Azure Databricks. You will learn to leverage generative models to create text, images, and synthetic data, enabling the development of intelligent systems. The hands-on labs, including building a RAG Chatbot and interacting with Agents, provide practical experience in developing AI solutions. This background allows an AI developer to build innovative and intelligent applications.

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Data Engineer

A data engineer designs, builds, and maintains data pipelines and infrastructure. This course is valuable for aspiring data engineers since it covers Azure Databricks, a vital platform for processing large datasets with Apache Spark. You will gain hands-on experience deploying Databricks workspaces and compute clusters, which helps build a foundation for managing data infrastructure. Furthermore, understanding Delta Lake and the Medallion Architecture, taught in this course, allows a data engineer to design robust and scalable data solutions. This is especially useful for those looking to work with big data and real-time analytics.

See salaries and explore the career path for Data Engineer

Big Data Architect

The big data architect designs and oversees the implementation of big data solutions. This course is beneficial for the big data architect as it delves into Apache Spark and Azure Databricks, technologies central to big data processing. You will learn how to process large datasets and extract meaningful insights in real-time. Furthermore, the course covers Delta Lake and the Medallion Architecture, which are crucial for designing scalable and efficient big data solutions. The skills acquired in data analytics and machine learning also enable you to integrate advanced analytics into big data architectures.

See salaries and explore the career path for Big Data Architect

Data Scientist

The data scientist analyzes data to extract meaningful insights and build predictive models. This course prepares you to become a data scientist by providing a comprehensive understanding of data analytics, machine learning, and deep learning with Azure Databricks. The course emphasizes real-time data analytics with Apache Spark, which is crucial for processing and analyzing large datasets. Furthermore, the skills acquired in building machine learning pipelines and applying deep learning techniques directly contribute to the data scientist's ability to develop data-driven solutions and predictive models.

See salaries and explore the career path for Data Scientist

Machine Learning Operations Engineer

A machine learning operations engineer focuses on deploying and managing machine learning models in production environments. This course is directly relevant, offering comprehensive training in machine learning and deep learning using Azure Databricks. This course helps build a foundation in deploying and scaling machine learning models with MLflow. Furthermore, the hands-on labs on hyperparameter tuning and model serving provide practical experience in optimizing and deploying models efficiently. These skills are essential for ensuring the reliable and scalable operation of machine learning systems.

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Cloud Solutions Architect

A cloud solutions architect designs and implements cloud-based solutions. This course is beneficial for cloud solutions architects because it provides a comprehensive understanding of Azure Databricks, a key component in modern cloud data platforms. This course helps build a foundation in deploying Databricks workspaces and integrating them with other Azure services. The skills learned in data analytics, machine learning, and AI using Azure Databricks enable the architect to design scalable and efficient cloud solutions for data processing and analysis.

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Data Science Manager

The data science manager leads a team of data scientists and oversees data-related projects. This course is useful for data science managers since it provides a comprehensive overview of data analytics, machine learning, and AI with Azure Databricks. This course empowers you to understand the technologies and methodologies used by your team, facilitating better project management and strategic decision-making. The course's coverage of model management with MLflow and hyperparameter tuning also helps you guide your team in building high-performance models.

See salaries and explore the career path for Data Science Manager

AI Consultant

An artificial intelligence consultant advises organizations on AI strategies and implementations. This course is valuable because it provides a broad understanding of AI technologies, including machine learning, deep learning, and generative AI within the Azure Databricks environment. This course helps build a foundation in building scalable machine learning pipelines and harnessing generative AI for innovative solutions. The consultant can leverage this knowledge to guide clients on leveraging Azure Databricks for their AI initiatives.

See salaries and explore the career path for AI Consultant

AI Product Manager

An artificial intelligence product manager is responsible for the strategy, roadmap, and feature definition of AI products. This course is valuable because it provides a solid understanding of AI technologies, including machine learning, deep learning, and generative AI using Azure Databricks. The course helps build a foundation in building scalable machine learning pipelines and leveraging generative AI for innovative solutions. The AI product manager can use this knowledge to inform product decisions and drive the development of AI-powered features.

See salaries and explore the career path for AI Product Manager

Data Analyst

The data analyst collects, cleans, and analyzes data to provide insights and support decision-making. This course may be useful because it covers data analytics with Apache Spark in Azure Databricks, essential tools for modern data analysis. This course helps build a foundation for real-time data analytics and processing large datasets. Furthermore, understanding data lakehouses and the Medallion Architecture allows the data analyst to organize and manage data effectively. The course's hands-on labs are particularly valuable for gaining practical experience in data analysis.

See salaries and explore the career path for Data Analyst

Analytics Engineer

Analytics engineers transform raw data into usable formats for analysis. This course is helpful because it provides skills in data analytics with Apache Spark and Azure Databricks, essential tools for modern data transformation. This course helps build a foundation in data lakehouses and the Medallion Architecture. Such a knowledge base allows the analytics engineer to design efficient data pipelines. The course's hands-on labs are invaluable for gaining practical experience in data transformation and preparation.

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Business Intelligence Analyst

A business intelligence analyst analyzes data to identify trends and insights that inform business decisions. This course may be useful for business intelligence analysts by providing skills in data analytics with Apache Spark in Azure Databricks. Understanding how to process and analyze large datasets helps to extract valuable insights. Additionally, the knowledge of machine learning techniques helps to develop predictive models for forecasting and decision-making. This course provides tools to enhance the analyst's ability to deliver data-driven recommendations.

See salaries and explore the career path for Business Intelligence Analyst

AI Research Scientist

An artificial intelligence research scientist conducts research to advance the field of AI. This course may be useful because it provides a solid foundation in machine learning, deep learning, and generative AI using Azure Databricks. The course exposes you to the latest advancements in generative AI and provides hands-on experience with building AI agents and RAG systems. While a research scientist typically requires an advanced degree, the practical skills and knowledge gained in this course can support research projects and experimentation with AI techniques.

See salaries and explore the career path for AI Research Scientist

Data and AI (Azure Databricks)

What's inside

Learning objectives

Syllabus

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Reviews summary

Practical data and ai on azure databricks

Activities

Career center

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