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Democratizing Machine Learning with the Power of Cloud
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.
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Democratizing Machine Learning with the Power of Cloud
- 1. Democratizing Machine Learning with thePower of Cloud
- 2. Haritha Thilakarathne Software Engineer– Data Science & Analytics Tech One Global – Enadoc Dev Center http://haritha.me
- 15. Why AzureML? • ReducesComplexity. • 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 hella 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 IrissetosaIris versicolor Iris virginica
- 33.
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