Top 10 Skills for Machine Learning Cloud Architects
32mBeginner2023-08-28
Authors

Ben Sullins
Data Geek, Tech Consultant
Free the Data Academy
Data Analytics Training Company
Course details
Check out the Top 10 Skills series, where career experts and LinkedIn Learning instructors walk you through the most important skills for data science professionals today. In this short beginner-friendly course, find out what you need to know to succeed in a rapidly growing field as a machine learning cloud architect, gathering insights along the way from data expert Ben Sullins to build out your toolbox and boost your career.
Whether you’re looking for a new move in data science, or just retooling for your current role, Ben shows you the skill set that can set you apart, working down the list, one skill at a time. Explore the fundamentals of cloud computing platforms, machine learning, deep learning, data processing and storage, data engineering, infrastructure as code (IAC), containerization and orchestration, CI/CD and DevOps, security and compliance, and communicative problem-solving. Discover the importance of preparing for tomorrow by learning impactful, new skills today.
Whether you’re looking for a new move in data science, or just retooling for your current role, Ben shows you the skill set that can set you apart, working down the list, one skill at a time. Explore the fundamentals of cloud computing platforms, machine learning, deep learning, data processing and storage, data engineering, infrastructure as code (IAC), containerization and orchestration, CI/CD and DevOps, security and compliance, and communicative problem-solving. Discover the importance of preparing for tomorrow by learning impactful, new skills today.
Skills covered
Cloud DevelopmentMachine LearningArtificial Intelligence (AI)Cloud ComputingOne-Off
Concepts
Top 10 Skills for Machine Learning Cloud Architects
- Skills for machine learning cloud architects
- Cloud computing platforms
- Machine learning
- Deep learning
- Data processing and storage
- Data engineering
- Infrastructure as code (IAC)
- Containerization and orchestration
- CI CD and DevOps
- Security and compliance
- Communication and problem-solving
- Summary