Course Description
A cloud-scale platform for data analytics & machine learning is called Azure Databricks. You will learn how to explore, prepare, and model data using Azure Databricks in this one-day course, as well as how to integrate Databrick's machine learning processes with Azure Machine Learning.
Prerequisites
You should have some experience using Python to work with data and some understanding of machine learning concepts before enrolling in this course. Complete the following learning path on Microsoft Learn before enrolling in this course:
- Create machine learning models
- Audience Profile (heading 3)
- 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
- 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
- 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
- Explain Azure Machine Learning
- Run experiments with Azure Databricks in Azure Machine Learning.
- Azure Machine Learning with MLflow log metrics
- Use 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 work 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 & offers some extra properties that make it better than AWS. PaaS Capabilities mainly include both Azure & AWS are similar in providing PaaS capabilities for virtual networking, storage, & machines.
To attend the training session, you should have operational Desktops or Laptops with the required specification & 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.
- A: Radiant Techlearning has a data center containing a Virtual Training environment for participant h&-on-practice.
- Participants can easily access these labs over Cloud with the help of a remote desktop connection.
- Radiant virtual labs allow you to learn from anywhere & 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 & give you a better experience. These real-time projects will help you greatly in future tasks & assignments.