- 1: What is the Professional Machine Learning Engineer Exam Prep Workshop?The workshop is a comprehensive training program designed to prepare professionals for the Google Professional Machine Learning Engineer certification exam. It covers key concepts, tools, and practices necessary for machine learning engineering and focuses on hands-on experience using platforms like Vertex AI, BigQuery, and AutoML.
- 2: Who should attend this workshop?This workshop is ideal for aspiring machine learning engineers, data scientists, and professionals looking to enhance their skills and gain certification in machine learning engineering. A basic understanding of machine learning, programming, and cloud computing is recommended, along with familiarity with Python and Google Cloud.
- 3: What topics will be covered in the workshop?The workshop covers a broad range of topics, including developing ML models using BigQuery ML, building AI solutions with ML APIs, training models with AutoML, managing and preprocessing data, prototyping models with Jupyter notebooks, training and scaling models, automating ML pipelines, and monitoring ML solutions.
- 4: How is the workshop structured?The workshop spans two intensive days, focusing on exam preparation. It combines theoretical explanations with practical exercises, group discussions, and real-world projects. Participants will gain hands-on experience with tools like Vertex AI, TensorFlow, PyTorch, and various Google Cloud services to tackle machine learning challenges.
- 5: Do I need prior experience in machine learning to attend?While prior knowledge of machine learning is not mandatory, a basic understanding of the field is recommended. Familiarity with Python and cloud platforms like Google Cloud will also be beneficial to make the most of the workshop.
- 6: Will the workshop help me pass the Google Professional Machine Learning Engineer exam?Yes, the workshop is specifically designed to help you prepare for and pass the Google Professional Machine Learning Engineer exam. You will learn the most important concepts and best practices, and receive guidance from expert trainers with real-world experience to ensure you are fully ready for the certification exam.
- 7: Will I get any certification after completing the workshop?Yes, upon completing the workshop, you will receive a certificate from DreamsPlus. This certificate will validate your preparedness for the Google Professional Machine Learning Engineer exam and demonstrate your advanced skills in machine learning engineering.
- 8: Can I attend this workshop online?Yes, the workshop is available both in Chennai and online. This allows you to join the session from anywhere, providing flexibility while still gaining the full experience of the workshop.
- 9: What is the duration of the workshop?The workshop lasts for two days, with each day dedicated to different aspects of machine learning engineering and exam preparation. The training is designed to be intensive, ensuring you gain a comprehensive understanding of the topics covered.
- 10: How can I register for the workshop?You can register for the workshop by visiting our website or by contacting us directly for assistance. For more details or any inquiries, feel free to reach out to us at support@dreamsplus.in or call us at +91 95000 56713.
Google Cloud
Professional Machine Learning Engineer Certification
"Prepare for Google Professional Machine Learning Engineer certification with DreamsPlus' exam prep workshop in Chennai and online.…
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Advanced Professional Machine Learning Engineer Certification Course
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
FAQs for Professional Machine Learning Engineer Certification
What Will You Learn?
- 2-day intensive exam prep workshop
- Expert trainers with real-world experience
- Comprehensive course material
- Interactive sessions and group discussions
- Practice exams and assessments
Course Curriculum
Course Highlights
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Review machine learning fundamentals
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Focus on exam objectives and question types
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Practice with real-world scenarios and case studies
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Get tips and strategies for passing the exam
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