What we're about
Upcoming events (4)
Free online AI tech event, Join from anywhere with zoom, Register and attend: https://learn.xnextcon.com/event/eventdetails/W2021020110 Agenda: * pre-event networking (15mins) * community updates, jobs/interns/talents announcements. (5mins) * tech talk, code labs and Q&A (100mins) Abstract: This is 2hour introduction to Product AI on Google Cloud Platform. I will cover the basic components in Google’s latest AI service, AI Platform (Unified). I will walk the audience through two end-2-end production pipeline notebook, showing how AI Platform (Unified) is integrated into enterprise level production. We will take a look at integrating AutoML, custom training for Tensorflow jobs, deployment to cloud instances, serving binaries, custom pre- and post- processing, auto-scaling, containers and debugging deployed models. The two end-to-end pipelines we will discuss: * AutoML Image Classification for online prediction * Custom Training Raw Bytes (image) Classification For each pipeline, we will deep dive to: * Step by step sequences. * Parameter choices. * CSV and JSONL dataset (input) and prediction (output) formats. * GPU and CPU compute and container selection. * Single device, multi-device and multi-instance distributed training. * Instance scaling for prediction * Opinionated tips and best practices for integration.
Free online AI workshop, you can join from anywhere with zoom, Register and attend here: https://www.aicamp.ai/event/eventdetails/W2021020913 Agenda: * pre-event networking (15mins) * community updates, jobs/interns/talents announcements. (5mins) * tech talk, code labs and Q&A (45mins) Abstract: Open source benefits can actually be very difficult to realize, even for heavy users of open source. Many enterprise companies are finding that their open source vendors feel similar to old fashioned "licensed" software providers. Licensing of open source projects often determines what you pay open source vendors downstream and whether you become "locked-in" or not. Having a simple strategy related to open source is key. During this webinar, we will cover: - The major types of open source licenses and their pros and cons - Other important factors in evaluating an open source offering such as governance and ecosystem - How to create a good open source strategy
Paid online live course (using zoom), follow instructions below to enroll ----------- 4-weeks AI course: ML for Developers with Scikit-Learn (Cohort 4) Start date: Feb 13th，10AM~2PM PST, Every Saturday. Enrollment: https://learn.xnextcon.com/course/coursedetails/C2021021310 ** 40% off promotion price will end on 1/27. In the AICamp online classroom (powered by Zoom), we will meet once a week and you will interact with instructors and other classmates, listen the lectures, discuss problems; After class, you will work on projects, homework, and get support on private group on slack. The course include: * 4 weeks/ 8 sessions/ 16 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 Details: In this course you will learn the fundamentals of machine learning including intuitions, important theoretical aspects and how to use machine learning algorithms to solve problems. Students will learn about the foundational underpinnings of machine learning as well as how to put that knowledge to the test with practical exercises. The course takes projects focused approach to teach you machine learning by building machine learning models and 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 these models work under the hood while writing code. The course balances learning theory, coding exercises, and working on projects. Students who take this course will be able to: * Identify and frame problems that can be solved by machine learning * Choose the right techniques to the problems * Understand key machine learning concepts and how algorithms / models work * Build and training various models with scikit-learn * Troubleshoot and improve models
Register and attend here: https://www.aicamp.ai/event/eventdetails/W2021022510 (Free online AI tech talk event, you can join from anywhere with zoom) Agenda: * pre-event networking (20mins) * community updates, jobs/interns/talents announcements. (5mins) * tech talk and Q&A (45mins) Abstract: COVID-19 has completely altered human behavior - the way that we shop, research, consume and act - causing big shifts in patterns of data. These shifting patterns mean that some AI models, which were previously working fine, are now no longer predicting with the same accuracy. This creates some big challenges for data scientists and engineering teams on how to detect which models have been affected and how to get these AI applications up and running in a seamless way to continue generating business value. In this session we will take a deep dive into the methodologies that exist for concept drift remediation. We will discuss how to automatically monitor models to detect drift, how to harness automated tools for adjusting models, how to utilize online models that adapt to shifting data, and how to set up alerts and tracking error rates for ongoing monitoring. The session will include a live demo and a real customer use case.