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4.2
103 reviews

Google Certified Professional Machine Learning Engineer

Master ML Algorithms, Data Modeling, TensorFlow & Google Cloud AI/ML Services. 137 Questions, Answers with Explanations
Instructor
Deepak Dubey
7,502 Students enrolled
  • Description
  • Curriculum
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  • Translate business challenges into ML use cases

  • Choose the optimal solution (ML vs non-ML, custom vs pre-packaged)

  • Define how the model output should solve the business problem

  • Identify data sources (available vs ideal)

  • Define ML problems (problem type, outcome of predictions, input and output formats)

  • Define business success criteria (alignment of ML metrics, key results)

  • Identify risks to ML solutions (assess business impact, ML solution readiness, data readiness)

  • Design reliable, scalable, and available ML solutions

  • Choose appropriate ML services and components

  • Design data exploration/analysis, feature engineering, logging/management, automation, orchestration, monitoring, and serving strategies

  • Evaluate Google Cloud hardware options (CPU, GPU, TPU, edge devices)

  • Design architectures that comply with security concerns across sectors

  • Explore data (visualization, statistical fundamentals, data quality, data constraints)

  • Build data pipelines (organize and optimize datasets, handle missing data and outliers, prevent data leakage)

  • Create input features (ensure data pre-processing consistency, encode structured data, manage feature selection, handle class imbalance, use transformations)

  • Build models (choose framework, interpretability, transfer learning, data augmentation, semi-supervised learning, manage overfitting/underfitting)

  • Train models (ingest various file types, manage training environments, tune hyperparameters, track training metrics)

  • Test models (conduct unit tests, compare model performance, leverage Vertex AI for model explainability)

  • Scale model training and serving (distribute training, scale prediction service)

  • Design and implement training pipelines (identify components, manage orchestration framework, devise hybrid or multicloud strategies, use TFX components)

  • Implement serving pipelines (manage serving options, test for target performance, configure schedules)

  • Track and audit metadata (organize and track experiments, manage model/dataset versioning, understand model/dataset lineage)

  • Monitor and troubleshoot ML solutions (measure performance, log strategies, establish continuous evaluation metrics)

  • Tune performance for training and serving in production (optimize input pipeline, employ simplification techniques)

How long do I have access to the course materials?
You can view and review the lecture materials indefinitely, like an on-demand channel.
Can I take my courses with me wherever I go?
Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!
4.2
103 reviews
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Course details
Video 17 hours
Certificate of Completion

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