Professional Machine Learning Engineer
Best Google Professional Machine Learning Engineer Training Institute in Coimbatore.
Nux Software Training & Certification Solutions provides the best Google Professional Machine Learning Engineer training courses programs in Coimbatore. Nux Software Training & Certification Solutions in Coimbatore offers great and sophisticated training programs that will improve your performance and provide you with hands-on experience. Expert trainers in our sector have a wide range of abilities and experience in their graded areas. The environment at the Training Center is ideal for professional, individual, corporate, live project, and industrial training. The lab’s infrastructure is cutting-edge and well-managed, and you can use it from anywhere at any time. The training center employs international specialist teachers who have extensive knowledge and real-world industrial experience. Our training programs incorporate a variety of cutting-edge learning methods and delivery models. We understand your needs and will provide you with 100% satisfaction.
Global Knowledge listed the Google Cloud Professional Google Professional Machine Learning Engineer certification as one of the highest paying IT qualifications.
This program will teach you the skills you need to develop your career as a Google Professional Machine Learning Engineer and will recommend training to help you prepare for the industry-recognized Google Cloud Professional Google Professional Machine Learning Engineer certification.
You will be able to implement solution parts such as infrastructure components such as networks, systems, and application services, as well as acquire real-world experience through a variety of hands-on Qwiklabs projects.
After successfully completing this program, you will receive a certificate of completion to share with your professional network and potential employers.
To become Google Cloud certified and demonstrate your proficiency in cloud architecture and Google Cloud Platform, as well as design, develop, and manage solutions to drive business objectives, you must register for and pass the official Google Cloud certification exam. More information on how to register, as well as extra resources to help you prepare, can be found at Nux software Training & Certification Solutions.
Course Syllabus
Google Professional Machine Learning Engineer Syllabus
Framing ML problems
- 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)
- Problem type (e.g., classification, regression, clustering)
- Outcome of model predictions
- Input (features) and predicted output format
- Alignment of ML success metrics to the business problem
- Key results
- Determining when a model is deemed unsuccessful
- 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)
Architecting ML solutions
- 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
- Evaluation of compute and accelerator options (e.g., CPU, GPU, TPU, edge devices)
- 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])
- Visualization
- Statistical fundamentals at scale
- Evaluation of data quality and feasibility
- Establishing data constraints (e.g., TFDV)
- Organizing and optimizing training datasets
- Data validation
- Handling missing data
- Handling outliers
- Data leakage
- Ensuring consistent data pre-processing between training and serving
- Encoding structured data types
- Feature selection
- Class imbalance
- Feature crosses
- Transformations (TensorFlow Transform)
Developing ML models
- 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
- 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
- Unit tests for model training and serving
- Model performance against baselines, simpler models, and across the time dimension
- Model explainability on Vertex AI
- Distributed training
- Scaling prediction service (e.g., Vertex AI Prediction, containerized serving)
- Identification of components, parameters, triggers, and compute needs (e.g., Cloud Build, Cloud Run)
- Orchestration framework (e.g., Kubeflow Pipelines/Vertex AI Pipelines, Cloud Composer/Apache Airflow)
- Hybrid or multicloud strategies
- System design with TFX components/Kubeflow DSL
- Serving (online, batch, caching)
- Google Cloud serving options
- Testing for target performance
- Configuring trigger and pipeline schedules
- Organizing and tracking experiments and pipeline runs
- Hooking into model and dataset versioning
- Model/dataset lineage
Monitoring, optimizing, and maintaining ML solutions
- 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
- Optimization and simplification of input pipeline for training
- Simplification techniques