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Google Professional-Machine-Learning-Engineer
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Google Professional-Machine-Learning-Engineer Questions and Answers
You are working on a prototype of a text classification model in a managed Vertex AI Workbench notebook. You want to quickly experiment with tokenizing text by using a Natural Language Toolkit (NLTK) library. How should you add the library to your Jupyter kernel?
You work with a data engineering team that has developed a pipeline to clean your dataset and save it in a Cloud Storage bucket. You have created an ML model and want to use the data to refresh your model as soon as new data is available. As part of your CI/CD workflow, you want to automatically run a Kubeflow Pipelines training job on Google Kubernetes Engine (GKE). How should you architect this workflow?
You are an ML engineer at a mobile gaming company. A data scientist on your team recently trained a TensorFlow model, and you are responsible for deploying this model into a mobile application. You discover that the inference latency of the current model doesn’t meet production requirements. You need to reduce the inference time by 50%, and you are willing to accept a small decrease in model accuracy in order to reach the latency requirement. Without training a new model, which model optimization technique for reducing latency should you try first?
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A Professional Machine Learning Engineer in the realm of Google Cloud is tasked with designing, building, and deploying scalable machine learning models and systems that leverage Google Cloud's infrastructure and services.
ML Engineers utilize Google Cloud's robust data storage and processing capabilities, such as BigQuery, Dataflow, and Dataproc, to efficiently manage and analyze large and intricate datasets for machine learning tasks.
Throughout the ML model development process, a Machine Learning Engineer is responsible for tasks like data preprocessing, feature engineering, model selection, hyperparameter tuning, training, evaluation, and deployment.
ML Engineers collaborate closely with data scientists, software developers, DevOps engineers, and business stakeholders to understand requirements, integrate machine learning solutions into existing systems, and ensure that ML-based applications meet business objectives.
Machine Learning Engineers need proficiency in programming languages like Python and experience with data platforms such as TensorFlow, PyTorch, Scikit-learn, and Google Cloud's suite of ML services like AutoML and AI Platform.
ML Engineers play a pivotal role in democratizing machine learning by developing reusable components, best practices, and tools that empower teams across the organization to build and deploy ML models efficiently and effectively, thus fostering innovation and driving business growth.
TESTED 21 Nov 2024
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