Q/A: Suggestion for C++ Programmer in Azure AI/ML?

Q: How a C++ programmer should choose his Azure-AIML journey. I mean what areas they should focus more?

Can you please make some recommendations?

Above was Q/A from @Amritanshu Mishra at https://www.skool.com/k21academy/qa-with-atul-today-are-you-joining?p=8a37903e

A:

You can focus as AI/ML Engineer / Developer, Architect (if 6+ years exp), or AI Consultant.

Here is how we see you learning; For a C++ programmer looking to start a journey in Azure AI and Machine Learning, focusing on areas that leverage your existing programming skills and build a foundational understanding of AI/ML can be a strategic approach.

Here’s a recommended path:

1. Data Fundamentals and ML Basics

- Focus Areas : Start by building a strong foundation in data science and machine learning principles. Key areas include data manipulation, statistics, and the basics of supervised and unsupervised learning.

- Recommended Resources : Azure’s Data Fundamentals (DP-900) or AI Fundamentals (AI-900) certifications are beginner-friendly and will help you understand essential concepts and Azure’s tools for AI and data.

2. Integrating AI and ML with C++

- Focus Areas : Since many AI/ML applications in Azure use Python, you might find it helpful to learn basic Python to work more comfortably within the ecosystem. However, you can use your C++ skills with Azure AI services by integrating model endpoints with C++ applications.

- Recommended Approach : Explore Azure’s REST APIs for Cognitive Services (like Azure Computer Vision , Language , and Speech), which can be easily accessed from C++ code, allowing you to build AI features into your applications without needing to switch languages.

Note: In AI-900 & AI-102 labs, we are doing labs using Python but we also have code for C++

3. Azure Machine Learning (AML)

- Focus Areas : AML is Azure’s managed machine learning service for model training, deployment, and MLOps. Understanding AML will allow you to focus on creating, managing, and deploying ML models with Azure resources.

- Recommended Skills : Learn how to use Azure Machine Learning SDK (primarily in Python, but integration with C++ is possible through REST APIs) and AML Workspaces, Pipelines, and Experiments. This knowledge will enable you to deploy models as web services and integrate them into C++ applications.

4. Computer Vision and Image Processing

- Focus Areas : If you’re interested in vision-based applications, Azure offers Computer Vision and Custom Vision APIs. They’re suitable for facial recognition, object detection, and image classification tasks.

- Recommended Skills : Learn how to set up and interact with these services, using them with C++ applications via REST APIs. Familiarity with image processing libraries in C++ (like OpenCV) will also be beneficial.

5. Natural Language Processing (NLP)

- Focus Areas : For C++ programmers with an interest in language-based AI applications, Azure offers Language Understanding (LUIS) and Text Analytics for sentiment analysis, language detection, and more.

- Recommended Skills : Experiment with Azure’s NLP APIs and focus on integrating these models with REST APIs. This can extend your C++ applications to perform sophisticated language processing tasks without needing in-depth NLP expertise.

6. MLOps and Model Deployment

- Focus Areas : Deploying and managing ML models is essential for production applications. MLOps on Azure involves continuous integration, deployment, and monitoring of ML models.

- Recommended Skills : Focus on learning Azure DevOps , Azure Machine Learning Pipelines , and containerization with Docker. C++ developers can benefit from Azure's MLOps tools by using REST APIs to deploy models into production environments.

7. Expand Cloud and Data Engineering Knowledge

- Focus Areas : Understanding how data flows within the Azure ecosystem and how to optimize resource usage is crucial.

- Recommended Skills : Familiarize yourself with Azure Data Lake , Azure Synapse Analytics , and Data Factory for data pipelines. This will provide a strong base if you intend to work on AI/ML projects with large datasets.

Suggested Certifications:

- Azure AI Fundamentals (AI-900) for a beginner’s perspective.

- Azure AI Engineer Associate (AI-102) if you want to go in-depth with deploying AI solutions.

- Azure Machine Learning Specialty (DP-100) for a practical understanding of ML development and deployment.

Do let us know if you have any follow up question.