Professional Machine Learning Engineer Exam Prep Workshop

DreamsPlus offers a comprehensive Professional Machine Learning Engineer Boot Camp in Chennai and online, designed to equip you with hands-on experience and prepare you for the Google Professional Machine Learning Engineer certification. Our expert trainers will guide you through the latest machine learning concepts and best practices to ensure you pass the exam with confidence.

Learning Pathway:

Section 1: Architecting low-code ML solutions 

1.1 Developing ML models by using BigQuery ML. 

• Selecting the appropriate BigQuery ML model (e.g., linear and binary classification, regression, time-series, matrix factorization, boosted trees, autoencoders) based on the business problem.
• Performing feature engineering or feature selection using BigQuery ML.
• Generating predictions using BigQuery ML.

1.2 Building AI solutions by using ML APIs. 

• Developing applications using ML APIs (e.g., Cloud Vision API, Natural Language API, Cloud Speech API, Translation).
• Developing applications using industry-specific APIs (e.g., Document AI API, Retail API).

1.3 Training models by using AutoML. 

• Preparing data for AutoML (e.g., feature selection, data labeling, Tabular Workflows on AutoML).
• Utilizing available data (e.g., tabular, text, speech, images, videos) to train custom models.
• Using AutoML for tabular data.
• Creating forecasting models with AutoML.
• Configuring and troubleshooting trained models.

Section 2: Collaborating within and across teams to manage data and models 

2.1 Exploring and preprocessing organization-wide data (e.g., Cloud Storage, BigQuery, Spanner, Cloud SQL, Apache Spark, Apache Hadoop). Considerations include:

• Organizing various types of data (e.g., tabular, text, speech, images, videos) for optimal training.
• Managing datasets within Vertex AI.
• Performing data preprocessing (e.g., Dataflow, TensorFlow Extended [TFX], BigQuery).
• Creating and managing features in Vertex AI Feature Store.
• Addressing privacy considerations related to data usage and collection (e.g., managing sensitive data such as personally identifiable information [PII] and protected health information [PHI]).

2.2 Model prototyping using Jupyter notebooks. Considerations include:

• Selecting the appropriate Jupyter backend on Google Cloud (e.g., Vertex AI Workbench, notebooks on Dataproc).
• Implementing security best practices in Vertex AI Workbench.
• Utilizing Spark kernels.
• Integrating with code repositories.
• Developing models in Vertex AI Workbench using common frameworks (e.g., TensorFlow, PyTorch, sklearn, Spark, JAX).

2.3 Tracking and running ML experiments. Considerations include:

• Choosing the right Google Cloud environment for development and experimentation (e.g., Vertex AI Experiments, Kubeflow Pipelines, Vertex AI TensorBoard with TensorFlow and PyTorch) based on the framework.

Section 3: Scaling prototypes into ML models 

3.1 Building models. Considerations include:

• Selecting the ML framework and model architecture.
• Applying modeling techniques based on interpretability needs.

3.2 Training models. Considerations include:

• Organizing training data (e.g., tabular, text, speech, images, videos) on Google Cloud (e.g., Cloud Storage, BigQuery).
• Ingesting various file formats (e.g., CSV, JSON, images, Hadoop, databases) for training.
• Training models using different SDKs (e.g., Vertex AI custom training, Kubeflow on Google Kubernetes Engine, AutoML, tabular workflows).
• Employing distributed training to set up robust pipelines.
• Tuning hyperparameters.
• Troubleshooting issues in ML model training.

3.3 Choosing appropriate hardware for training. Considerations include:

• Assessing compute and accelerator options (e.g., CPU, GPU, TPU, edge devices).
• Implementing distributed training with TPUs and GPUs (e.g., Reduction Server on Vertex AI, Horovod).

Section 4: Serving and scaling models 

4.1 Serving models. Considerations include:

• Implementing batch and online inference (e.g., Vertex AI, Dataflow, BigQuery ML, Dataproc).
• Serving models using various frameworks (e.g., PyTorch, XGBoost).
• Managing a model registry.
• Conducting A/B testing for different model versions.

4.2 Scaling online model serving. Considerations include:

• Utilizing Vertex AI Feature Store.
• Managing Vertex AI public and private endpoints.
• Selecting appropriate hardware (e.g., CPU, GPU, TPU, edge).
• Scaling the serving infrastructure based on throughput requirements (e.g., Vertex AI Prediction, containerized serving).
• Optimizing ML models for production in terms of performance, latency, memory, and throughput (e.g., simplification techniques).

Section 5: Automating and orchestrating ML pipelines 

5.1 Developing end-to-end ML pipelines. Considerations include:

• Validating data and models.
• Ensuring consistent data preprocessing between training and serving.
• Hosting third-party ML pipelines on Google Cloud (e.g., MLFlow).
• Identifying necessary components, parameters, triggers, and compute resources (e.g., Cloud Build, Cloud Run).
• Selecting an orchestration framework (e.g., Kubeflow Pipelines, Vertex AI Pipelines, Cloud Composer).
• Implementing hybrid or multicloud strategies.
• Designing systems using TFX components or Kubeflow DSL (e.g., Dataflow).

5.2 Automating model retraining. Considerations include:

• Defining a suitable retraining policy.
• Implementing CI/CD for model deployment (e.g., Cloud Build, Jenkins).

5.3 Tracking and auditing metadata. Considerations include:

• Tracking and comparing model artifacts and versions (e.g., Vertex AI Experiments, Vertex ML Metadata).
• Integrating with model and dataset versioning.
• Managing model and data lineage.

Section 6: Monitoring ML solutions 

6.1 Identifying risks to ML solutions. Considerations include:

• Developing safe machine learning systems (e.g., guarding against inadvertent data or model exploitation, hacking) 
• Complying with Google’s Responsible AI policies (e.g., biases)
• Evaluating the preparedness of ML solutions (e.g., fairness, data bias)
• Explainability of the Vertex AI model (Vertex AI Prediction, for example). 

6.2 Monitoring, testing, and troubleshooting ML solutions. Considerations include:

• Setting up metrics for continuous evaluation (such as Explainable AI and Vertex AI Model Monitoring) 
• Tracking training-serving skew 
• Tracking feature attribution drift 
• Tracking model performance against simpler models, baselines, and across time dimensions 
• Common training and serving errors 

Why Choose DreamsPlus?

• Expert trainers with industry experience
• Comprehensive course material
• Interactive training sessions
• Guaranteed success in Google certification exam