Seattle AI Tech Meetup

This is paid online course, follow instructions below to enroll. do NOT rsvp at meetup here. ----------- We are starting the 9th cohort of this live course: 4-week Full Stack Machine Learning with AWS. This course is online live course. You can listen, watch, interact, Q&A with instructors from anywhere around the world. If you miss the live session due to time zone or conflict, you can learn session replay with recordings, course materials any time. The course include: * 4 weeks/ 8 sessions/ 12 hours * 8 lectures / 8 hands-on projects * Live Sessions, Real time interaction * Capstone projects, work with peer students globally * Slack supports to projects and homework * Students project demonstration, add to Github portfolio * Earn certificate upon course completion * Free trial, and scholarship is available Start date: Nov 3rd, 10am PT/1pm ET. Every Tue and Thu. Enrollment: https://learn.xnextcon.com/course/coursedetails/C2020110310 Details: In this course, we will train you to become a Full Stack Machine Learning Engineer, capable of not just training models but also deploying and managing them in production for business value. You will learn machine learning primarily through building 8 production grade services, step by step. You will learn how to build production AI in AWS, how to integrate it with an application, and how to manage it through its lifecycle. You will also build from scratch a custom project (where you will gather data, train and tune a production grade models, build a prediction service, and connect that prediction service to create your own ML application). This course has 8 hands on sessions where you will build, step by step, production grade machine learning services for business applications. You will also work with your peer students as a team on group project.

This is paid online course, follow instructions below to enroll. do NOT rsvp at meetup here. ----------- Start date: Nov 9th, 10am PST (US Pacific Time), Every Mon and Wed (double check your local time) 4 sessions, 8hours total, Monday and Wednesday Enrollment: https://learn.xnextcon.com/course/coursedetails/C2020110910 Are you responsible for building a data platform for your company, or maybe for your customers? Then you must have heard, or even used Apache Kafka - a scalable, distributed and a highly-available open source system used for message brokering, distributed logs, and streaming applications. Apache Kafka is used by successful tech companies such as Linkedin/Foursquare/Cisco, and also by small startups that want to scale up effortlessly as their business grows. Being highly available, Apache Kafka is used across many industries ranging from technology, fintech, financial services, IoT, retails etc. Learn to acquire data from its origin to where it can be transported, modified, analyzed, and finally utilized for business value. At the completion of this course, you will learn to create elegant and robust architectures using Kafka. This is a four-part training on Apache Kafka. The training will offer both theoretical and practical modules. By participating in the training you would be able to: * Learn the features of Apache Kafka * Understand the underlying core concepts * Experience the message brokers with hands-on exercise * Evaluate Kafka as a solution for your technical challenges This training is packed with practical exercises and code labs. * We meet twice a week at AICamp online classroom (powered by zoom) * Practical walkthroughs that present solutions to actual, real-world problems and challenges * A no-nonsense teaching style that cuts through all the cruft and help you master kafka * Build end to end data stream pipeline with everything you learn

This is online AI tech talk event, you can join from anywhere with zoom, Register and attend: https://learn.xnextcon.com/event/eventdetails/W2020111110 Abstract: Welcome to the "Deep Learning in Practice" learning series, presented by Allegro AI. This series is focused on methodologies and tools for machine and deep-learning(ML/DL) projects. In the 3rd session, we will harness the pipeline concept towards manageable high throughput experimentation in ML/DL research. Currently, complex pipelines are found in the field of ML in implementations of automated training and deployment of ML models. However, these pipelines and the code they encapsulate are rarely those that are used in the research stage. Moreover, existing research pipelines tend to be focused on the data preparation stage, and are mostly trivial afterward. This represents several areas where we can do better: * Easily “grow” automated multi-stage workflows from research code with minimal code changes. * Frictionless executions of these pipelines on available resources. * Minimizing re-writes when promoting code from research towards “production” I will address suggestions for improvements in these, with specific examples from simple to intricate workflows in research. In the previous webinar, we established how ensuring reproducibility in ML research enables automation, which in turn unlocks advanced MLOps (such as pipelines). Since these topics will be used for this webinar, it is recommended to refresh your memory with the recorded event. Social networking with speakers, attendees 30mins before/after the event on slack. Join slack by the invitation: https://bit.ly/3gi7bjf . The two channels: #jobs for job posting from speakers, partners, sponsors companies, and you can Q&A with hiring managers right in the channel. #events for events Q&A, mixing and networking with speakers and other peer attendees.