Learn Cloud Computing: Google Cloud Certified Professional Machine Learning Engineer
Last updated Jun 2024
Course Overview
The Google Cloud Certified Professional Machine Learning
Engineer course is designed to equip professionals with the skills needed to
design, build, and manage machine learning (ML) models on Google Cloud Platform
(GCP). The course covers a range of topics including ML pipeline design, data
preprocessing, model training, deployment, and maintenance.
Exam Topics
- Framing
ML Problems: Understanding and defining ML use cases.
- Data
Preparation and Processing: Cleaning and preprocessing data for ML.
- Model
Training and Optimization: Building and tuning ML models.
- ML
Pipeline Automation: Implementing and managing ML workflows.
- Model
Deployment and Monitoring: Deploying models to production and
monitoring their performance.
- Responsible
AI: Ensuring fairness, accountability, and transparency in ML models.
Benefits of Taking This Course
- Industry Recognition: Earn a prestigious certification from Google Cloud.
- In-depth Knowledge: Gain comprehensive skills in ML on GCP.
- Career Advancement: Improve qualifications for roles in data science and ML engineering.
- Hands-on Experience: Engage in practical labs and real-world scenarios.
Target Audience
- Machine Learning Engineers: Professionals focused on developing and deploying ML models.
- Data Scientists: Those who analyze data and build predictive models.
- Software Engineers: Developers looking to specialize in ML.
- IT Professionals: Individuals involved in managing and optimizing ML solutions on GCP.
Exam Details
- Format: Multiple choice and multiple select questions.
- Duration: 2 hours.
- Cost: $200 USD.
- Languages: English.
- Delivery Method: Online proctored or on-site at testing centers.
Key Skills Gained
- ML Pipeline Design: Designing and implementing end-to-end ML pipelines.
- Data Management: Preprocessing and managing datasets for ML.
- Model Training: Training and optimizing ML models using GCP tools.
- Deployment: Deploying ML models to production environments.
- Monitoring and Maintenance: Monitoring model performance and retraining as needed.
- Ethical AI: Applying principles of responsible AI in ML projects.
Prerequisites
- Experience: 3+ years of industry experience, including 1+ year with GCP.
- Technical Knowledge: Familiarity with machine learning concepts and GCP services.
- Hands-on Practice: Practical experience with ML projects and tools on GCP.
Included in this course
Perfectly curated Tests designed to help candidate prepare for the Certification Exam.
Topic-wise Content Distribution
Free Tests1
1. Free Test 1
Practice Tests4
1. Practice Test 1
2. Practice Test 2
3. Practice Test 3
4. Practice Test 4
Section Tests6
1. Monitor ML solutions
2. Automate and orchestrate ML pipelines
3. Architect low-code ML solutions
4. Scale prototypes into ML models
5. Serve and scale models
6. Collaborate within and across teams to manage data and models
Final Tests1
1. Final Test 1
What our students say about us
Frequently Asked Questions
Q: What is the format of the exam?
A: The exam consists of multiple choice and multiple select questions.
Q: How much time should I dedicate to studying?
A: A few months of dedicated study and hands-on practice are recommended.
Q: Are there any prerequisites for the exam?
A: It is recommended to have 3+ years of industry experience, including 1+ year with GCP.
Q: What resources are available for preparation?
A: Google Cloud offers official training, hands-on labs, practice exams, and extensive documentation.
Q: What skills will I gain from this course?
A: Skills in ML pipeline design, data management, model training, deployment, monitoring, and ethical AI.
