Online Classroom Batches Preferred
Weekdays Regular
(Class 1Hr - 1:30Hrs) / Per Session
Weekdays Regular
(Class 1Hr - 1:30Hrs) / Per Session
Weekend Regular
(Class 3hr - 3:30Hrs) / Per Session
Weekend Fasttrack
(Class 4:30Hr - 5:00Hrs) / Per Session
No Interest Financing start at ₹ 5000 / month
Top Skills You Will Gain
- Azure Cognitive Services
- Creating Bots, Enhancing your bots with LUI
- Deployment and containers
- Integrate Cognitive
- Services with bots and Agent
- Monitoring, analytics and best practices
- Image classification
- Enhancing bots with QnA Maker
Microsoft Azure AI Engineer Associate Certification Course Key Features 100% Money Back Guarantee
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Build 3+ Projects
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Microsoft Azure AI Engineer Associate Certification Course Curriculam
Trainers Profile
Trainers are certified professionals with 12+ years of experience in their respective domains as well as they are currently working with Top MNCs. As all Trainers from Microsoft Azure AI Engineer Associate Certification are respective domain working professionals so they are having many live projects, trainers will use these projects during training sessions.
Pre-requisites
Candidates for the Azure AI Engineer Associate certification should have subject matter expertise using cognitive services, machine learning, and knowledge mining to architect and implement Microsoft AI solutions involving natural language processing, speech, computer vision, and conversational AI.
Syllabus of Microsoft Azure AI Engineer Associate Certification Online Course Download syllabus
- 1. Recommend Cognitive Services APIs to meet business requirements
- 2. Choosing a Microsoft cognitive services technology
- 3. Microsoft Azure Cognitive Services: The Big Picture
- 4. Microsoft Azure Cognitive Services: Custom Vision API
- 5. Precision versus Recall
- 6. automated-ml#precision-recall-chart
- 7. Performance measures in Azure ML: Accuracy, Precision, Recall and F1 Score
- 1. Bot Service Compliance
- 1. Machine learning at scale learning-at-scale
- 2. Designing an Intelligent Edge in Microsoft Azure (By Jared Rhodes)
- 3. Azure IoT reference architecture
- 4. Azure IoT reference architecture (Detailed PDF)
- 5. Choosing a real-time message ingestion technology in Azure
- 1. Azure Databricks
- 1. Build a real-time recommendation API on Azure
- 2. storage required to store logging, bot state data, and Cognitive Services output
- 3. Choose the right data store
- 4. Warehouse
- 5. Azure Data Lake Analytics
- 6. Azure Data Lake Store
- 7. Cosmos DB Global Distribution:
- 8. Install the Azure IoT Edge runtime on Debian-based Linux system
- 9. Deploy Azure IoT Edge modules from the Azure portal
- 10. Creating an image recognition solution with Azure IoT Edge and Azure Cognitive Services
- 11. IoT edge built-in modules
- 12. Stream Analytics
- 13. Another option for stream analytics is Apache Spark in Azure Databrick or HDInsigt, or Apache Storm in HDInsight
- 14. Apache Storm
- 15. Anomaly detection using machine learning in Azure Stream Analytics
- 16. Built-in ML based Anomaly Detection
- 1. Real-Time ML based Anomaly Detection in Azure Stream Analytics (same as above but demo with Raspberry Pi)
- 2. Anomaly Generator (Git)
- 3. Performing sentiment analysis by using Azure Stream Analytics and Azure Machine Learning Studio
- 4. Real-time analytics on IoT Edge with Azure Stream Analytics
- 5. Azure Stream Analytics now available on IoT Edge
- 1. What is Azure Machine Learning
- 2. Predict automobile price with the visual interface
- 3. Create and manage Azure Machine Learning service workspaces
- 4. AI Pipelines
- 5. Azure Machine Learning Pipeline
- 6. AML Samples:
- 7. Get Started with Azure Machine Learning
- 8. How to use Notebooks with Azure Machine Learning workspace
- 9. Choose a Compute target
- 10. Deploy a model to an Azure Kubernetes Service cluster
- 11. Deploy a model using a custom Docker image
- 12. Azure Data Factory
- 13. Data Science Virtual Machine
- 14. Data Science Virtual Machine
- 15. AI Data Science Virtual Machine
- 16. Learning Virtual Machine
- 1. Cognitives Services Directory
- 2. Classify images with the Microsoft Custom Vision Service
- 3. Speech Service Documentation
- 4. Bing Web Search API Documentation
- 5. What is Custom Decision Service?
- 1. Building a bot
- 2. Bot Framework Emulator
- 3. Data sources for QnA Maker content
- 4. Best practices of a QnA Maker knowledge base
- 5. LUIS (Language Understanding Intelligent Services)
- 6. Entity types and their purposes in LUIS
- 7. Composite entity
- 8. List entity
- 9. Enterprise-grade conversational bot
- 1. What are field-programmable gate arrays (FPGA)
- 2. Inside the Microsoft FPGA-based configurable cloud
- 3. Hyperscale hardware: ML at scale on top of Azure + FPGA
- 4. GPUs vs CPUs for deployment of deep learning models by Fidan Boylu Uz
- 4. Reference architecture: Machine Learning model training with AKS
- 5. Deploy Deep Learning CNN on Kubernetes Cluster with GPUs – AML version
- 6. Deploying Deep Learning Models on Kubernetes with GPUs
- 7. Design Data for Governance, compliance, integrity & security
- 1. IoT Hub
- 2. AML Services
- 3. AML Sample
- 1. Azure IoT reference architecture
- 2.Add authentication to your bot via Azure Bot Service
- 1. Overview of alerts in Microsoft Azure
- 2. Monitor your Azure Machine Learning models with Application Insights
- 3. Add telemetry to your bot
- 4. Bot analytics
- 1. Other useful materials
- 2. Developing AI Models in Microsoft Azure
- 3. Managing Microsoft Azure AI Solutions
- 4. Microsoft Azure Developer: Creating and Integrating AI with Azure Services
- 5. AI-100: Designing and Implementing an Azure AI Solution — Study Guide
- 6. MLOps Workshop
- 7. Microsoft Cloud Workshop library
- 8. Creating an image recognition solution with Azure IoT Edge and Azure Cognitive Services
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Microsoft Azure AI Engineer Associate Certification Training Objectives
- Candidates for the Azure AI Engineer Associate certification should have subject matter expertise using cognitive services, machine learning, and knowledge mining to architect and implement Microsoft AI solutions involving natural language processing, speech, computer vision, and conversational AI.
- Azure AI Engineers translate the vision from solution architects and work with data scientists, data engineers, IoT specialists, and software developers to build complete end-to-end solutions.
- Analyse solution requirements
- Design AI solutions
- Implement and monitor AI solutions
- Visual perception
- Speech Recognition
- Decision-Making
- Translation between the languages
- Design and Implement Azure AI solutions
- IBM Watson usage skill
- Knowledge mining to Architect
- Cognitive services
- Handling Azure resources
- Experienced programmer who wish to develop and implement the solution in Azure environment
- Candidate should be familiar with Python or R
- Non-programmer with computer skill
- IT programming knowledge is an added advantage
- Analysing requirements for AI solutions
- Recommending the appropriate tools and technologies
- Designing and implementing AI solutions that meet scalability
- Performance requirements
- Aspirants should have a piece of knowledge in developing python applications
- Machine learning skill is an added advantage
- One-year experience in Azure application tools
- Minimum experience with Azure Environme
- AI is the fastest growing technology. Many top tech-giants investing a lot in this technology. The huge advantage of this course is to handling data generation. We are generating a lot of data on a daily basis.
- Because of all these advantages of this course, top companies like Amazon, Google, J.P Morgan, IBM, Netflix, Facebook and Twitter recruiting AI engineers
- Higher salaries
- Huge job openings across the globe
- Flexible platform
- Agile technologies
- Portable
- Open-source
- EXAM AI-100 WILL BE REPLACED WITH EXAM AI-102 ON FEBRUARY 23, 2021.
- You will still be able to earn this certification by passing AI-100 until it retires on June 30, 2021.
Exam & Certification
- Participate and Complete One batch of Microsoft Azure AI Engineer Associate Certification Training Course
- Successful completion and evaluation of any one of the given projects
- Complete 85% of the Microsoft Azure AI Engineer Associate Certification Certification course
- Successful completion and evaluation of any one of the given projects
- Oracle Certified Associate (OCA)
- Oracle Certified Professional (OCP)
- Oracle Certified Expert (OCE)
- Oracle Certified Master (OCM)
- Learn About the Certification Paths.
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- Solve Sample Tests that would help you to Increase the Speed needed for attempting the exam and also helps for Agile Thinking.
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Software Testing, CapgeminiMicrosoft Azure AI Engineer Associate Certification Course FAQ's
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