Google Professional Machine Learning Engineer Certification

Google Professional Machine Learning Engineer Certification
  • Category: IT & Software
  • Store: Udemy
  • Students: 376
  • Rating: 0 / 5
  • Duration: 3 Hours
  • Discounted Price: 0$
  • Price: 49.99$
  • Added Date: 1 month ago
  • Expired Date: Expired
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Description

A Professional Machine Learning Engineer designs, builds, and productionizes ML models to solve business challenges using Google Cloud technologies and knowledge of proven ML models and techniques. The ML Engineer considers responsible AI throughout the ML development process, and collaborates closely with other job roles to ensure long-term success of models. The ML Engineer should be proficient in all aspects of model architecture, data pipeline interaction, and metrics interpretation. The ML Engineer needs familiarity with foundational concepts of application development, infrastructure management, data engineering, and data governance. Through an understanding of training, retraining, deploying, scheduling, monitoring, and improving models, the ML Engineer designs and creates scalable solutions for optimal performance.


The Professional Machine Learning Engineer exam assesses your ability to:


Frame ML problems

Architect ML solutions

Design data preparation and processing systems

Develop ML models

Automate & orchestrate ML pipelines

Monitor, optimize, and maintain ML solutions


Section 1: Framing ML problems

1.1 Translating business challenges into ML use cases. Considerations include:


Choosing the best solution (ML vs. non-ML, custom vs. pre-packaged [e.g., AutoML, Vision API]) based on the business requirements

Defining how the model output should be used to solve the business problem

Deciding how incorrect results should be handled

Identifying data sources (available vs. ideal)

1.2 Defining ML problems. Considerations include:


Problem type (e.g., classification, regression, clustering)

Outcome of model predictions

Input (features) and predicted output format

1.3 Defining business success criteria. Considerations include:


Alignment of ML success metrics to the business problem

Key results

Determining when a model is deemed unsuccessful

1.4 Identifying risks to feasibility of ML solutions. Considerations include:


Assessing and communicating business impact

Assessing ML solution readiness

Assessing data readiness and potential limitations

Aligning with Google's Responsible AI practices (e.g., different biases)

Section 2: Architecting ML solutions

2.1 Designing reliable, scalable, and highly available ML solutions. Considerations include:


Choosing appropriate ML services for the use case (e.g., Cloud Build, Kubeflow)

Component types (e.g., data collection, data management)

Exploration/analysis

Feature engineering

Logging/management

Automation

Orchestration

Monitoring

Serving

2.2 Choosing appropriate Google Cloud hardware components. Considerations include:


Evaluation of compute and accelerator options (e.g., CPU, GPU, TPU, edge devices)

2.3 Designing architecture that complies with security concerns across sectors/industries. Considerations include:


Building secure ML systems (e.g., protecting against unintentional exploitation of data/model, hacking)

Privacy implications of data usage and/or collection (e.g., handling sensitive data such as Personally Identifiable Information [PII] and Protected Health Information [PHI])

Section 3: Designing data preparation and processing systems

3.1 Exploring data (EDA). Considerations include:


Visualization

Statistical fundamentals at scale

Evaluation of data quality and feasibility

Establishing data constraints (e.g., TFDV)

3.2 Building data pipelines. Considerations include:


Organizing and optimizing training datasets

Data validation

Handling missing data

Handling outliers

Data leakage

3.3 Creating input features (feature engineering). Considerations include:


Ensuring consistent data pre-processing between training and serving

Encoding structured data types

Feature selection

Class imbalance

Feature crosses

Transformations (TensorFlow Transform)

Section 4: Developing ML models

4.1 Building models. Considerations include:


Choice of framework and model

Modeling techniques given interpretability requirements

Transfer learning

Data augmentation

Semi-supervised learning

Model generalization and strategies to handle overfitting and underfitting

4.2 Training models. Considerations include:


Ingestion of various file types into training (e.g., CSV, JSON, IMG, parquet or databases, Hadoop/Spark)

Training a model as a job in different environments

Hyperparameter tuning

Tracking metrics during training

Retraining/redeployment evaluation

4.3 Testing models. Considerations include:


Unit tests for model training and serving

Model performance against baselines, simpler models, and across the time dimension

Model explainability on AI Platform

4.4 Scaling model training and serving. Considerations include:


Distributed training

Scaling prediction service (e.g., AI Platform Prediction, containerized serving)

Section 5: Automating and orchestrating ML pipelines

5.1 Designing and implementing training pipelines. Considerations include:


Identification of components, parameters, triggers, and compute needs (e.g., Cloud Build, Cloud Run)

Orchestration framework (e.g., Kubeflow Pipelines/AI Platform Pipelines, Cloud Composer/Apache Airflow)

Hybrid or multi-cloud strategies

System design with TFX components/Kubeflow DSL

5.2 Implementing serving pipelines. Considerations include:


Serving (online, batch, caching)

Google Cloud serving options

Testing for target performance

Configuring trigger and pipeline schedules

5.3 Tracking and auditing metadata. Considerations include:


Organizing and tracking experiments and pipeline runs

Hooking into model and dataset versioning

Model/dataset lineage

Section 6: Monitoring, optimizing, and maintaining ML solutions

6.1 Monitoring and troubleshooting ML solutions. Considerations include:


Performance and business quality of ML model predictions

Logging strategies

Establishing continuous evaluation metrics (e.g., evaluation of drift or bias)

Understanding Google Cloud permissions model

Identification of appropriate retraining policy

Common training and serving errors (TensorFlow)

ML model failure and resulting biases

6.2 Tuning performance of ML solutions for training and serving in production. Considerations include:


Optimization and simplification of input pipeline for training

Simplification techniques


best practice Tests for Google Professional Machine Learning Engineer Certification 2021

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