What we're about

"Learn by Practicing". Join us to learn and practice AI, Machine learning, Deep learning and Data Science technology together with like-minded developers.

Our goal is to congregate with AI enthusiasts from all over Sao Paulo to learn and practice AI tech, through tech talks, workshops, code labs, hackathons, tech conferences, etc.. we regularly invite tech leads from innovated companies, successful startups to share latest in AI, practical experiences and best practices. We also invite speakers from all of the world to speak in-person or online and learn AI together with developers from all over the world

If you’d like to speak at future meetups, co-promote your meetup or inquire about partnership opportunities, please feel free to reach out to us.

===Tentative Agenda =====
5:30pm - 5:50pm, Snack & social
5:50pm - 6:00pm, Intro/announcement
6:00pm - 7:00pm, Tech Talk 1 and Q&A
7:00pm - 8:00pm, Tech Talk 2 and Q&A
8:00pm - 8:30pm, Mingle

**Learn applied AI tech online with 100,000+ developers globally, via webinars, live online courses, bootcamps: https://learn.xnextcon.com
** AI Developer Conference (Seattle, San Francisco, New York, Beijing): http://www.xnextcon.com

Upcoming events (5)

Online AI tech talk: Deep Learning Performance

Needs a location

RSVP on meetup is turned off, make sure to register here: https://learn.xnextcon.com/event/eventdetails/W20060110 Start date/time: 1st June, 10 AM PST (GMT-7), double check your local time. Description: "Watching paint dry is faster than training my deep learning model.” “If only I had ten more GPUs, I could train my model in time.” “I want to run my model on a cheap smartphone, but it’s probably too heavy and slow.” If this sounds like you, then you might like this talk. Exploring the landscape of training and inference, we cover a myriad of tricks that step-by-step improve the efficiency of most deep learning pipelines, reduce wasted hardware cycles, and make them cost-effective. We identify and fix inefficiencies across different parts of the pipeline, including data preparation, reading and augmentation, training, and inference. With a data-driven approach and easy-to-replicate TensorFlow examples, finely tune the knobs of your deep learning pipeline to get the best out of your hardware. And with the money you save, demand a raise!

Google online talk #5: Builds feature pipelines with Apache Beam by eBay

RSVP on meetup is turned off, make sure to register and attend the event here: https://learn.xnextcon.com/event/eventdetails/W20060310 Start date: 3 June, 10am PST (US pacific time, GMT-7), double check your local time. Description: Welcome to the session 5 of the Beam Learning Months! In this session we will learn how eBay builds feature pipelines with Apache Beam. To unify feature extraction and selection in online and offline, to speedup E2E iteration for model training, evaluation and serving, to support different types (streaming, runtime, batch) of features, etc. eBay leverages Apache Beam for their streaming feature SDK as a foundation to integrate with Kafka, Hadoop, Flink, Airflow and others in eBay. Don't forget to sign up other sessions of the series (every Wednesday): * Session 6: Jun 10th, Distributed Processing for Machine Learning Production Pipelines with Tensorflow. Past sessions: * Session 1: May 6th, Interactive Introduction to Apache Beam * Session 2: May 13th, Best practices to a production-ready pipeline * Session 3: May 20th, Introduction to the Spark Runner * Session 4: May 27th, The Best of Both Worlds: Unlocking the Power of Apache Beam with Apache Flink * Session 5: Jun 3rd, Feature Powered by Apache Beam – Beyond Lambda

3-week Online AI course: Practical Python for Machine Learning

This is paid online course, please read the instructions below to pay and enroll. price starting from $79 for limited time. Join hundreds of developers from all around the world to learn and practice AI, machine learning with python. This course is online live course. You can listen, watch, interact, Q&A with instructors from anywhere around the world. You work with peer devs on projects. If you miss the live session due to time zone or conflict, you can learn by watching session replay any time and live support on slack. Start date: 9 June, 10am PT (US pacific time, check your local time zone). * 3 Weeks / 6 Sessions / 12 hours * 6 lectures / 6 coding exercises * Live Sessions, Real time interaction * Capstone project, Peer students collaboration * Slack supports to projects and homework Enrollment: https://learn.xnextcon.com/course/coursedetails/C20060910 Details: This course covers the key Python skills you will need so you can start using Python for machine learning. The course is ideal for: * Those with some previous coding experience who wants to add Python to their repertoire or level up their basic Python skills. * Aspiring programmers who are learning their first programming language In this course you will learn the fundamentals of Python primarily through a series of coding exercises guided by the instructor. Students will learn about the foundational underpinnings of Python as well as how to put that knowledge to the test with practical exercises. The course takes project-focused approach to teach you Python by building projects. The instructor will walk you through a series of curated projects, and explain the key concepts as they arise. Students will learn the theory and how they work under the hood while writing code

Google online talk #6: Distributed Processing for ML Pipelines with Tensorflow

RSVP on meetup is turned off, make sure to register and attend the event here: https://learn.xnextcon.com/event/eventdetails/W20061010 Start date: 10 June, 10am PST (US pacific time, GMT-7), double check your local time. Description: Welcome to the session 6 of the Beam Learning Months! Production ML workloads often require very large compute and system resources, which leads to the application of distributed processing on clusters. On premises or cloud-based infrastructure cost requires maximum efficient use of resources. This makes distributed processing pipeline frameworks such as Apache Flink ideal for ML workloads. In addition, production ML must address issues of modern software methodology, as well as issues unique to ML. Different types of ML have different requirements, often driven by the different data lifecycles and sources of ground truth. Implementations often suffer from limitations in modularity, scalability, and extensibility. In this talk, we discuss production ML applications and review TensorFlow Extended (TFX), Flink, Apache Beam, and Google experience with ML in production. Don't forget to sign up other sessions of the series (every Wednesday): * Session 1: May 6th, Interactive Introduction to Apache Beam * Session 2: May 13th, Best practices to a production-ready pipeline * Session 3: May 20th, Introduction to the Spark Runner * Session 4: May 27th, The Best of Both Worlds: Unlocking the Power of Apache Beam with Apache Flink * Session 5: Jun 3rd, Feature Powered by Apache Beam – Beyond Lambda

Past events (75)

Photos (37)