Democratizing Machine Learning with the Power of Cloud
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The document discusses the benefits of using Azure Machine Learning (AzureML) for democratizing machine learning, highlighting features such as reduced complexity, no coding requirement, built-in algorithms, and cloud power. It emphasizes easy deployment and collaboration, as well as support for R and Python. The document also hints at a demo featuring the Iris flower dataset.
Democratizing Machine Learning with the Power of Cloud
- 1. Democratizing Machine Learning with the Power of Cloud
- 2. Haritha Thilakarathne Software Engineer – Data Science & Analytics Tech One Global – Enadoc Dev Center http://haritha.me
- 15. Why AzureML? • Reduces Complexity. • No coding! Seriously?? • Top class machine learning algorithms inbuilt. • Power of cloud. • Easy deployment with RESTful API. • Easy collaboration. • R & Python support • Vowpal Wabbit
- 26. You heard hell a lot on AzureML that you couldn’t believe, and now it’s time for a DEMO Caution : Unexpected things may occur during a demonstration :)
- 28. Iris Flower Dataset Iris setosaIris versicolor Iris virginica
Editor's Notes
- #4: Google traffic – A use of big data, data analysis, data visualization
- #6: Data science is multidisciplinary Data Science acts as the middle core
- #7: Machine learning is a technique of data science that helps computers learn from existing data in order to forecast future behaviors, outcomes, and trends.
- #8: needed a huge amount of processing power and storage. Thus, businesses seeking to use the so-called learning systems for tasks like predictive analytics had to shell out major bucks for hardware and software.
- #10: Cortana Intelligence is a powerful solution to transform your data into intelligent action from Microsoft.
- #11: A fully managed cloud service that enables you to easily build, deploy, and share predictive analytics solutions.
- #14: Cross Industry Standard Process for Data Mining – CRISP-DM Azure machine learning process. Starts with defining the objective.
- #15: http://download.microsoft.com/download/A/6/1/A613E11E-8F9C-424A-B99D-65344785C288/microsoft-machine-learning-algorithm-cheat-sheet-v6.pdf
- #17: https://docs.microsoft.com/en-us/azure/machine-learning/machine-learning-data-science-vm-do-ten-things
- #26: Price is nearly 5000$
- #27: Demo would be building a multiclass classification for Iris dataset.
- #28: Dataset is grabbed from UCI machine learning repository.
- #29: These are the three classes that need to be predicted
- #30: With machine learning, you can’t get 100% accuracy. If you getting close to 100% it should surely be overfitting.
- #33: Q&A