In the second course of the Practical Data Science Specialization, you will learn to automate a natural language processing task by building an end-to-end machine learning pipeline using Hugging Face’s highly-optimized implementation of the state-of-the-art BERT algorithm with Amazon SageMaker Pipelines. Your pipeline will first transform the dataset into BERT-readable features and store the features in the Amazon SageMaker Feature Store. It will then fine-tune a text classification model to the dataset using a Hugging Face pre-trained model, which has learned to understand the human language from millions of Wikipedia documents. Finally, your pipeline will evaluate the model’s accuracy and only deploy the model if the accuracy exceeds a given threshold.
Dieser Kurs ist Teil der Spezialisierung Spezialisierung Practical Data Science
von
Spezialisierung Practical Data Sciencedeeplearning.aiÜber diesen Kurs
Working knowledge of ML & Python, familiarity with Jupyter notebook & stat, completion of the Deep Learning & AWS Cloud Technical Essentials courses
Was Sie lernen werden
Store and manage machine learning features using a feature store
Debug, profile, tune and evaluate models while tracking data lineage and model artifacts
Kompetenzen, die Sie erwerben
- ML Pipelines and MLOps
- Model Training and Deployment with BERT
- Model Debugging and Evaluation
- Feature engineering and feature store
- Artifact and lineage tracking
Working knowledge of ML & Python, familiarity with Jupyter notebook & stat, completion of the Deep Learning & AWS Cloud Technical Essentials courses
von
deeplearning.ai
DeepLearning.AI is an education technology company that develops a global community of AI talent.
Amazon Web Services
Since 2006, Amazon Web Services has been the world’s most comprehensive and broadly adopted cloud platform. AWS offers over 90 fully featured services for compute, storage, networking, database, analytics, application services, deployment, management, developer, mobile, Internet of Things (IoT), Artificial Intelligence, security, hybrid and enterprise applications, from 44 Availability Zones across 16 geographic regions. AWS services are trusted by millions of active customers around the world — including the fastest-growing startups, largest enterprises, and leading government agencies — to power their infrastructure, make them more agile, and lower costs.
Lehrplan - Was Sie in diesem Kurs lernen werden
Week 1: Feature Engineering and Feature Store
Transform a raw text dataset into machine learning features and store features in a feature store.
Week 2: Train, Debug, and Profile a Machine Learning Model
Fine-tune, debug, and profile a pre-trained BERT model.
Week 3: Deploy End-To-End Machine Learning pipelines
Orchestrate ML workflows and track model lineage and artifacts in an end-to-end machine learning pipeline.
Bewertungen
Top-Bewertungen von BUILD, TRAIN, AND DEPLOY ML PIPELINES USING BERT
It is one of course with the exact content required for an working professional who is already working with AWS and want to leverage the benefits of sagemaker for their ML deployment tasks
Week 3 lab gave twice error 'Failed' and 3rd time it went without an issue. This was quite frustrating. Overall, good class. Thx.
Simple to learn but there are lot of takeaways which helps any data scientist or a machine learning engineer!
Über den Spezialisierung Practical Data Science
Development environments might not have the exact requirements as production environments. Moving data science and machine learning projects from idea to production requires state-of-the-art skills. You need to architect and implement your projects for scale and operational efficiency. Data science is an interdisciplinary field that combines domain knowledge with mathematics, statistics, data visualization, and programming skills.
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