Training Overview
Azure Databrick is a cloud-scale platform for data analytics & machine learning. In this one-day training, you'll learn how to use Azure Databricks to explore, prepare, & model data; & integrate Databricks machine learning processes with azure machine learning.
Duration- 1 Day
Prerequisites
Before attending this training, you should have experience using Python to work with data, & some knowledge of machine learning concepts. Before attending this training, complete the following learning path on Microsoft Learn:
- Create machine learning models
Audience Profile
This training is designed for data scientists with experience in Pythion who need to learn how to apply their data science & machine learning skills on Azure Databricks.
Content Outline
Get started with Azure Databricks
- Understand Azure Databricks
- Provision Azure Databricks workspaces & clusters
- Work with notebooks in Azure Databricks
Work with data in Azure Databricks
- Understand data frames
- Query data frames
- Visualize data
Preparing data for machine learning with Azure Databricks
- Understand machine learning concepts
- Perform data cleaning
- Perform feature engineering
- Perform data scaling
- Perform data encoding
Training a machine learning model with Azure Databricks
- Understand Spark ML
- Train & validate a model
- Use other machine learning frameworks
Using MLflow to track experiments in Azure Databricks
- Understand the capabilities of MLflow
- Use MLflow terminology
- Run experiments
Manage machine learning models in Azure Databricks
- Describe considerations for model management
- Register models
- Manage model versioning
Track Azure Databricks experiments in Azure Machine Learning
- Describe Azure Machine Learning
- Run Azure Databricks experiments in Azure Machine Learning
- Log metrics in Azure Machine Learning with MLflow
- Run Azure Machine Learning pipelines on Azure Databricks compute
Deploy Azure Databricks models in Azure Machine Learning
- Describe considerations for model deployment
- Plan for Azure Machine Learning deployment endpoints
- Deploy a model to Azure Machine Learning
- Troubleshoot model deployment
- Understand Azure Databricks
- Provision Azure Databricks workspaces & clusters
- Work with notebooks in Azure Databricks
- Understand data frames
- Query data frames
- Visualize data
- Understand machine learning concepts
- Perform data cleaning
- Perform feature engineering
- Perform data scaling
- Perform data encoding
- Understand Spark ML
- Train & validate a model
- Use other machine learning frameworks
- Understand the capabilities of MLflow
- Use MLflow terminology
- Run experiments
- Describe considerations for model management
- Register models
- Manage model versioning
- Describe Azure Machine Learning
- Run Azure Databricks experiments in Azure Machine Learning
- Log metrics in Azure Machine Learning with MLflow
- Run Azure Machine Learning pipelines on Azure Databricks compute
- Describe considerations for model deployment
- Plan for Azure Machine Learning deployment endpoints
- Deploy a model to Azure Machine Learning
- Troubleshoot model deployment
FAQs
Most recently, Microsoft announced new technology designed to accelerate machine learning algorithms to real-time, which is known as Project Brainwave & it uses programmable processors known as FPGAs & use to run sophisticated & compute-hungry algorithms. Microsoft is also developing industry-specific AI applications
Microsoft replaced this unsatisfactory handwork by humans with a piece of AI-enhanced technology called FastStart.
An AI Platform is a framework that is designed to function more efficiently & intelligently than traditional frameworks. An AI Platform can also provide Data Governance, ensuring the use of best practices by a team of AI scientists & ML engineers
Azure is 4-12% cheaper than AWS, & it also offers some extra properties, which makes it better than AWS. PaaS Capabilities mainly include both Azure & AWS are similar in offering PaaS capabilities for virtual networking, storage, & machines.
To attend the training session, you should have operational Desktops or Laptops with the required specification, along with a good internet connection to access the labs.
We would always recommend you attend the live session to practice & clarify the doubts instantly & get more value from your investment. However, if, due to some contingency, you have to skip the class, Radiant Techlearning will help you with the recorded session of that particular day. However, those recorded sessions are not meant only for personal consumption & NOT for distribution or any commercial use.
Radiant Techlearning has a data center containing the Virtual Training environment for the purpose of participant hand-on-practice.
Participants can easily access these labs over Cloud with the help of a remote desktop connection.
Radiant virtual labs provide you the flexibility to learn from anywhere in the world & in any time zone.
The learners will be enthralled as we engage them the real-world & industry Oriented projects during the training program. These projects will improve your skills & knowledge & you will gain a better experience. These real-time projects will help you a lot in your future tasks & assignments.