Amazon Elastic Map Reduce:the demos

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Demo #1: starting an EMR cluster + Spot fleet Demo #2: processing S3 data with Apache Hive Demo #3: recommendation with Apache Mahout Demo #4: spam classification with Apache Spark

Amazon Elastic Map Reduce:
the demos
Julien Simon, AI Evangelist, EMEA
@julsimon

What to expect
• Demo #1: starting an EMR cluster + Spot fleet
• Demo #2: processing S3 data with Apache Hive
• Demo #3: recommendation with Apache Mahout
• Demo #4: spam classification with Apache Spark
• Q&A

© 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Resources
https://aws.amazon.com/emr/
https://aws.amazon.com/blogs/bigdata
https://aws.amazon.com/whitepapers/
Two Decades of Recommender Systems at Amazon.com
https://www.computer.org/csdl/mags/ic/2017/03/mic2017030012.html
https://mahout.apache.org/
https://mahout.apache.org/users/recommender/recommender-documentation.html
https://spark.apache.org/mllib/
Code samples: https://github.com/juliensimon/aws/tree/master/ML
https://medium.com/@julsimon

Thank you!
Julien Simon, AI Evangelist, EMEA
@julsimon

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Presented by Vladimir Iglovikov: - https://www.linkedin.com/in/iglovikov/ - https://x.com/viglovikov - https://www.instagram.com/ternaus/ This presentation delves into the journey of Albumentations.ai, a highly successful open-source library for data augmentation. Created out of a necessity for superior performance in Kaggle competitions, Albumentations has grown to become a widely used tool among data scientists and machine learning practitioners. This case study covers various aspects, including: People: The contributors and community that have supported Albumentations. Metrics: The success indicators such as downloads, daily active users, GitHub stars, and financial contributions. Challenges: The hurdles in monetizing open-source projects and measuring user engagement. Development Practices: Best practices for creating, maintaining, and scaling open-source libraries, including code hygiene, CI/CD, and fast iteration. Community Building: Strategies for making adoption easy, iterating quickly, and fostering a vibrant, engaged community. Marketing: Both online and offline marketing tactics, focusing on real, impactful interactions and collaborations. Mental Health: Maintaining balance and not feeling pressured by user demands. Key insights include the importance of automation, making the adoption process seamless, and leveraging offline interactions for marketing. The presentation also emphasizes the need for continuous small improvements and building a friendly, inclusive community that contributes to the project's growth. Vladimir Iglovikov brings his extensive experience as a Kaggle Grandmaster, ex-Staff ML Engineer at Lyft, sharing valuable lessons and practical advice for anyone looking to enhance the adoption of their open-source projects. Explore more about Albumentations and join the community at: GitHub: https://github.com/albumentations-team/albumentations Website: https://albumentations.ai/ LinkedIn: https://www.linkedin.com/company/100504475 Twitter: https://x.com/albumentations

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Alan Dix

Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024 https://alandix.com/academic/papers/synergy2024-epistemic/ As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.

Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdf

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Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack. While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack. I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:

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DianaGray10

Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI. UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities. Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes. What will you get from this session? 1. Insights into integrating generative AI. 2. Understanding how this integration enhances test automation within the UiPath platform 3. Practical demonstrations 4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath Topics covered: What is generative AI Test Automation with generative AI and Open AI. UiPath integration with generative AI Speaker: Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP

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Amazon Elastic Map Reduce:the demos

1. Amazon Elastic Map Reduce: the demos Julien Simon, AI Evangelist, EMEA @julsimon

2. What to expect • Demo #1: starting an EMR cluster + Spot fleet • Demo #2: processing S3 data with Apache Hive • Demo #3: recommendation with Apache Mahout • Demo #4: spam classification with Apache Spark • Q&A

3. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved. Resources https://aws.amazon.com/emr/ https://aws.amazon.com/blogs/bigdata https://aws.amazon.com/whitepapers/ Two Decades of Recommender Systems at Amazon.com https://www.computer.org/csdl/mags/ic/2017/03/mic2017030012.html https://mahout.apache.org/ https://mahout.apache.org/users/recommender/recommender-documentation.html https://spark.apache.org/mllib/ Code samples: https://github.com/juliensimon/aws/tree/master/ML https://medium.com/@julsimon

4. Thank you! Julien Simon, AI Evangelist, EMEA @julsimon

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