Google Certified Professional Machine Learning Engineer
Become Google Certified Professional Machine Learning Engineer
Google Certified Professional Machine Learning Engineer
A Professional Machine Learning Engineer builds, evaluates, productionizes, and optimizes AI solutions by using Google Cloud capabilities and knowledge of conventional ML approaches. The ML Engineer handles large, complex datasets and creates repeatable, reusable code. The ML Engineer designs and operationalizes generative AI solutions based on foundational models. The ML Engineer considers responsible AI practices, and collaborates closely with other job roles to ensure the long-term success of AI-based applications. The ML Engineer has strong programming skills and experience with data platforms and distributed data processing tools.
The ML Engineer is proficient in the areas of model architecture, data and ML pipeline creation, generative AI, and metrics interpretation. The ML Engineer is familiar with foundational concepts of MLOps, application development, infrastructure management, data engineering, and data governance. The ML Engineer enables teams across the organization to use AI solutions. By training, retraining, deploying, scheduling, monitoring, and improving models, the ML Engineer designs and creates scalable, performant solutions.
Note: The exam does not directly assess coding skill. If you have a minimum proficiency in Python and Cloud SQL, you should be able to interpret any questions with code snippets.
What you’ll learn
- Framing ML problems
- Architecting ML solutions
- Designing data preparation and processing systems
- Developing ML models
- Automating and orchestrating ML pipelines
- Monitoring, optimizing, and maintaining ML solutions
- Learn the skills needed to be successful in a machine learning engineering role
- Prepare for the Google Cloud Professional Machine Learning Engineer certification exam
- Understand how to design, build, productionalize ML models to solve business challenges using Google Cloud technologies
- Understand the purpose of the Professional Machine Learning Engineer certification and its relationship to other Google Cloud certifications
Advance your career with in-demand skills
- Receive professional-level training from Google Cloud
- Demonstrate your technical proficiency
- Earn an employer-recognized certificate from Google Cloud
- Prepare for an industry certification exam
Earn a career certificate
Add this credential to your LinkedIn profile, resume, or CV
Share it on social media and in your performance review
Professional Certificate – 8 course series
87% of Google Cloud certified users feel more confident in their cloud skills. This program provides the skills you need to advance your career and provides training to support your preparation for the industry-recognized Google Cloud Professional Machine Learning Engineer certification.
Here’s what you have to do
1) Complete the Preparing for Google Cloud Machine Learning Engineer Professional Certificate
2) Review other recommended resources for the Google Cloud Professional Machine Learning Engineer exam
3) Review the Professional Machine Learning Engineer exam guide
4) Complete Professional Machine Learning Engineer sample questions
5) Register for the Google Cloud certification exam (remotely or at a test center)
Applied Learning Project
This professional certificate incorporates hands-on labs using Qwiklabs platform.These hands on components will let you apply the skills you learn. Projects incorporate Google Cloud Platform products used within Qwiklabs. You will gain practical hands-on experience with the concepts explained throughout the modules.
Applied Learning Project
This specialization incorporates hands-on labs using Google’s Qwiklabs platform.
These hands on components will let you apply the skills you learn in the video lectures. Projects will incorporate topics such as Google Cloud Platform products, which are used and configured within Qwiklabs. You can expect to gain practical hands-on experience with the concepts explained throughout the modules.
Requirements
- Some prior experience with Google Cloud and Machine Learning will help. Also if you are already certified with Google Professional Data Engineer that will help you greatly.
Description
- Translate business challenges into ML use cases
- Choose the optimal solution (ML vs non-ML, custom vs pre-packaged)
- Define how the model output should solve the business problem
- Identify data sources (available vs ideal)
- Define ML problems (problem type, outcome of predictions, input and output formats)
- Define business success criteria (alignment of ML metrics, key results)
- Identify risks to ML solutions (assess business impact, ML solution readiness, data readiness)
- Design reliable, scalable, and available ML solutions
- Choose appropriate ML services and components
- Design data exploration/analysis, feature engineering, logging/management, automation, orchestration, monitoring, and serving strategies
- Evaluate Google Cloud hardware options (CPU, GPU, TPU, edge devices)
- Design architectures that comply with security concerns across sectors
- Explore data (visualization, statistical fundamentals, data quality, data constraints)
- Build data pipelines (organize and optimize datasets, handle missing data and outliers, prevent data leakage)
- Create input features (ensure data pre-processing consistency, encode structured data, manage feature selection, handle class imbalance, use transformations)
- Build models (choose framework, interpretability, transfer learning, data augmentation, semi-supervised learning, manage overfitting/underfitting)
- Train models (ingest various file types, manage training environments, tune hyperparameters, track training metrics)
- Test models (conduct unit tests, compare model performance, leverage Vertex AI for model explainability)
- Scale model training and serving (distribute training, scale prediction service)
- Design and implement training pipelines (identify components, manage orchestration framework, devise hybrid or multicloud strategies, use TFX components)
- Implement serving pipelines (manage serving options, test for target performance, configure schedules)
- Track and audit metadata (organize and track experiments, manage model/dataset versioning, understand model/dataset lineage)
- Monitor and troubleshoot ML solutions (measure performance, log strategies, establish continuous evaluation metrics)
- Tune performance for training and serving in production (optimize input pipeline, employ simplification techniques)
Who this course is for:
- Anyone wishing to get Google Cloud Certified Professional Machine Learning Engineer
CERTIFICATION FEE: NOT INCLUSIVE
COURSE DURATION: 1 Month