What’s Included?
High-Video
AI Mentor
Access for Tablet & Phone
Prerequisites
- Basic familiarity with cloud computing concepts (e.g., VMs, storage)
- Subscription to the certification
- Laptop with a modern browser and stable internet
- No prior professional cloud certification is required
- Willingness to engage with code-based and low-code cloud lab environments
- Basic understanding of Python programming (for custom model labs)
- General knowledge of machine learning fundamentals
Skills You’ll Gain
- AI Model deployment on Cloud Platforms (GCP, Azure)
- Containerization for AI/ML (via tools like NIM)
- Cloud cost optimization for training/inference
- Prompt engineering for API-driven models
- Building interactive web apps for ML models
- Automated Machine Learning (AutoML) workflows
- Managing the MLOps lifecycle
- Scaling inference with optimized microservices
- Collaborative data science using cloud notebooks
- API integration for running third-party models
Self Study Materials Included
Videos
Engaging visual content to enhance understanding and learning experience.
Tools You’ll Master
DataRobot AI Cloud
Google Cloud AI Tools
Google Cloud ML Tools
Hugging Face Spaces
Kaggle Notebooks
Microsoft Azure Free Tier
NVIDIA NIM
OpenAI Playground
Replicate
Streamlit Community Cloud
What You’ll Learn
Design an end-to-end MLOps pipeline using cloud-native services.
Containerize and deploy models for highly scalable production environments.
Optimize cloud expenditure for AI training and inference workloads.
Build interactive web interfaces for sharing ML model predictions.
Leverage automated ML (AutoML) platforms for rapid solution development.
Test and refine generative AI prompts using specialized developer sandboxes.
Utilize optimized microservices for high-performance model serving (NVIDIA NIM).
Integrate third-party, state-of-the-art models via simplified APIs (Replicate).
Collaborate on data science projects using powerful cloud notebooks.
Master core AI services across at least two major cloud providers (GCP, Azure).
Frequently Asked Questions
Data scientists, cloud developers, ML engineers, and IT architects who need to design and manage AI solutions at scale on public cloud infrastructure.
A basic familiarity with machine learning concepts and some coding (Python) is helpful, but the course is structured to guide you through cloud setup.
Most motivated learners complete the course and hands-on labs within 4–6 weeks of dedicated study.
The tools list leverages free tiers and community versions (Azure Free, Kaggle, Hugging Face Spaces) to minimize costs. Any optional paid services will be clearly flagged.
Yes, this certification validates practical skills in using industry-standard cloud and AI tools, making it valuable globally for professional roles.
Absolutely. All labs and case studies focus on practical scenarios like deploying an image classifier or a generative AI service.
No, you will start with foundational cloud concepts specific to AI and build up to advanced deployment and management skills.
Yes, proficiency in reading and modifying Python code is required for the model training and API integration labs