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Machine Learning with PyCarent + MLflow

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PyCaret is an open source, low-code machine learning library in Python that allows you to go from preparing your data to deploying your model within minutes in your choice of environment. This talk is a practical demo using PyCaret in your existing workflows and supercharges your data science team's productivity.

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Machine Learning with PyCaret + MLflow Apr 27, 2021
About me • Data Scientist with 10+ years of experience in solving business problems using data. • Background in Finance, Economics, Analytics, and Data Science. • Worked in Healthcare, Education, Consulting, and Fintech. • Member of CPA, CMA, Canada and ACMA, UK, CGMA • Lived and worked in Asia, Middle East, East Africa, North America. Currently based in Toronto, Canada https://www.linkedin.com/in/profile-moez/ https://twitter.com/moezpycaretorg1 https://moez-62905.medium.com/ moez@pycaret.org
Important Links • Official : https://www.pycaret.org • Docs: https://pycaret.readthedocs.io/en/latest/ • PyCaret GitHub : https://www.github.com/pycaret/pycaret • LinkedIn : https://www.linkedin.com/company/pycaret • YouTube : https://www.youtube.com/channel/UCxA1YTYJ9BEeo50lxyI_B3g • Medium : https://moez-62905.medium.com/ • Today’s Presentation and Demo : https://www.github.com/pycaret/pycaret-demo-mlflow Follow hashtag #pycaret on LinkedIn and Twitter
Join our community! https://join.slack.com/t/pycaret/shared_invite/zt-p7aaexnl-EqdTfZ9U~mF0CwNcltffHg
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Let’s get started
Machine Learning Life Cycle
What is PyCaret? PyCaret is an open source, low-code machine learning library and end-to-end model management tool created to help you automate your machine learning workflows in Python. It is commonly used for rapid prototyping and deployment of ML pipelines. EASY TO USE PRODUCTIVITY TOOL BUSINESS READY
Impact of PyCaret 0 20 40 60 80 100 120 140 160 180 Data Preparation Model Training Model Selection Model Evaluation Cumulative Lines of Code Comparison scikit-learn PyCaret Cumulative lines of code Machine Learning Workflow
Machine Learning use-case supported: Classification Regression Clustering Anomaly Detection Association Rule Mining Natural Language Processing SUPERVISED UNSUPERVISED Time Series
Record and query experiments: code, data, config, and results Prepare Data, Train Model, Tune Hyperparameters, Evaluate Performance Train Model Tune Model Analyze Model Deploy Model
Thank you Questions?

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