The course is part of this learning path
This short course covers some additional topics you should review before taking the Google Cloud Digital Leader certification exam.
You’ll get a quick introduction to the following GCP services:
- AI/ML options
- Compliance Reports Manager
- Container Registry
- Database Migration
- Anyone preparing to take the Google Cloud Digital Leader exam
- Complete all other courses in the Cloud Digital Leader Learning Path
AI and Machine Learning Services
If you're going to take the Cloud Digital Leader Exam, then you need to be familiar with the various AI and machine learning products that Google offers. I recommend that you spend a little time familiarizing yourself with the name and general use cases for each.
- You should know that AutoML is used to training models with minimal effort and expertise.
- You should know that Form Parser is used to extract text and spatial structures from documents.
- You should know that Natural Language is used to reveal the structure and meaning of unstructured text.
- You should know that the Recommendations AI will generate highly personalized product recommendations.
- That Translation is used to dynamically translate between languages.
- That Vertex AI is used to build, deploy, and scale more effective AI models.
- And Vision OCR is used to extract text from documents.
Now, the exam is not going to expect you to have really, deep knowledge about each of these products But it might ask you to do something like, to pick the right product for solving a business problem.
While we are on the subject of Machine Learning, you also should also be familiar with Cloud TPUs.
A Cloud TPU (or Tensor Processing Unit) is a custom-designed ASIC (or Application-specific integrated circuit) that is used for ML workloads.
You’ve probably heard of using graphic cards or GPUs for training models. GPUs can provide significant performance boosts. But ultimately, they are designed for accelerating graphics operations, not machine learning. Cloud TPUs were designed from the ground up with ML workloads in mind. This results in faster training times and lower costs.
Daniel began his career as a Software Engineer, focusing mostly on web and mobile development. After twenty years of dealing with insufficient training and fragmented documentation, he decided to use his extensive experience to help the next generation of engineers.
Daniel has spent his most recent years designing and running technical classes for both Amazon and Microsoft. Today at Cloud Academy, he is working on building out an extensive Google Cloud training library.
When he isn’t working or tinkering in his home lab, Daniel enjoys BBQing, target shooting, and watching classic movies.