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Google Professional Machine Learning Engineer Associate GCP

Il corso "Google ML Engineers" sviluppato da First Consulting è un focus dedicato ai ruoli di System Engineering , Architects , Developers ed ulteriori sulle soluzioni  "end to end" di ML (Machine Learning) gestibili tramite i Google Services dedicati. 
La docenza prevede il rilascio della knowledge tramite iterazioni progressive " Step By Step" per la definizione e la creazione di ambiti e soluzioni di ML , tramite un percorso operativo ed esemplificazioni di alcuni ML Uses case sulla piattaforma GCP 


min 10 gg piano formativo personalizzabile


Attestato di partecipazione First Consulting


Path di Certificazione GCP





Section 1: Framing ML problems

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)

 Defining ML problems. Considerations include:

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

  • Outcome of model predictions

  • Input (features) and predicted output format

 Defining business success criteria. Considerations include:

  • Alignment of ML success metrics to the business problem

  • Key results

  • Determining when a model is deemed unsuccessful

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

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

Choosing appropriate Google Cloud hardware components. Considerations include:

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

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

Exploring data (EDA). Considerations include:

  • Statistical fundamentals at scale

  • Evaluation of data quality and feasibility

  • Establishing data constraints (e.g., TFDV)

Building data pipelines. Considerations include:

  • Organizing and optimizing training datasets

  • Data validation

  • Handling missing data

  • Handling outliers

  • Data leakage

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

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

 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

 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

 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

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 multicloud strategies

  • System design with TFX components/Kubeflow DSL

 Implementing serving pipelines. Considerations include:

  • Serving (online, batch, caching)

  • Google Cloud serving options

  • Testing for target performance

  • Configuring trigger and pipeline schedules

 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

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

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

  • Optimization and simplification of input pipeline for training

  • Simplification techniques




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