As demands for AI applications grow, we've seen a lot of effort put by companies to build their Machine Learning Engineering (MLE) tools tailored for their needs. There are just so many challenges faced by industries in regards to having a well-designed environment for their Machine Learning (ML) lifecycle: building, deploying, and managing ML models in production. This post will cover two papers, explaining MLE practices from two of the leading tech companies: Google and Microsoft. Adding a little bit of context, this article is part of a graduate-level course at Columbia University: COMS6998 Practical Deep Learning System Performance taught by Prof. Parijat Dube who also works at IBM New York as Research Staff Member. The first section will present a paper from Google and will touch on the building part of an ML lifecycle.

This article is part of "AI education", a series of posts that review and explore educational content on data science and machine learning. If I wanted to learn deep learning with Python again, I would probably start with PyTorch, an open-source library developed by Facebook's AI Research Lab that is powerful, easy to learn, and very versatile. When it comes to training material, however, PyTorch lags behind TensorFlow, Google's flagship deep learning library. There are fewer books on PyTorch than TensorFlow, and even fewer online courses. Among them is Deep Learning with PyTorch by Eli Stevens, Luca Antiga, and Thomas Viehmann, three engineers who have contributed to the project and have extensive experience developing deep learning solutions.

What are the opportunities and threats faced by the vendors in the international market?

NVIDIA expanded the NVIDIA Jetson AI at the Edge platform with an entry-level developer kit priced at just $59, opening the potential of AI and robotics to a new generation of students, educators and hobbyists. The Jetson Nano 2GB Developer Kit is designed for teaching and learning AI by creating hands-on projects in such areas as robotics and intelligent IoT. To support the effort, NVIDIA also announced the availability of free online training and AI-certification programs, which will supplement the many open-source projects, how-tos and videos contributed by thousands of developers in the vibrant Jetson community. "While today's students and engineers are programming computers, in the near future they'll be interacting with, and imparting AI to, robots," said Deepu Talla, vice president and general manager of Edge Computing at NVIDIA. "The new Jetson Nano is the ultimate starter AI computer that allows hands-on learning and experimentation at an incredibly affordable price."

One of the more challenging real-world problems in computational intelligence is to learn from non-stationary streaming data, also known as concept drift. Perhaps even a more challenging version of this scenario is when -- following a small set of initial labeled data -- the data stream consists of unlabeled data only. Such a scenario is typically referred to as learning in initially labeled nonstationary environment, or simply as extreme verification latency (EVL). Because of the very challenging nature of the problem, very few algorithms have been proposed in the literature up to date. This work is a very first effort to provide a review of some of the existing algorithms (important/prominent) in this field to the research community. More specifically, this paper is a comprehensive survey and comparative analysis of some of the EVL algorithms to point out the weaknesses and strengths of different approaches from three different perspectives: classification accuracy, computational complexity and parameter sensitivity using several synthetic and real world datasets.

A communication enabled indoor intelligent robots (IRs) service framework is proposed, where nonorthogonal multiple access (NOMA) technique is adopted to enable highly reliable communications. In cooperation with the ultramodern indoor channel model recently proposed by the International Telecommunication Union (ITU), the Lego modeling method is proposed, which can deterministically describe the indoor layout and channel state in order to construct the radio map. The investigated radio map is invoked as a virtual environment to train the reinforcement learning agent, which can save training time and hardware costs. Build on the proposed communication model, motions of IRs who need to reach designated mission destinations and their corresponding down-link power allocation policy are jointly optimized to maximize the mission efficiency and communication reliability of IRs. In an effort to solve this optimization problem, a novel reinforcement learning approach named deep transfer deterministic policy gradient (DT-DPG) algorithm is proposed. Our simulation results demonstrate that 1) With the aid of NOMA techniques, the communication reliability of IRs is effectively improved; 2) The radio map is qualified to be a virtual training environment, and its statistical channel state information improves training efficiency by about 30%; 3) The proposed DT-DPG algorithm is superior to the conventional deep deterministic policy gradient (DDPG) algorithm in terms of optimization performance, training time, and anti-local optimum ability. Xianbin Wang is with Department of Electrical and Computer Engineering, Western University, London, ON N6A5B9, Canada (email: xianbin.wang@uwo.ca). The explosive development of robotics and artificial intelligence technologies have changed, are changing and will continue to transform human lives. In recent years, intelligent robots (IRs) are proven competent to provide a variety of services, such as security monitoring, sanitation, and travel guides [1]. New various services offered by IRs require a large amount of communication, computation and data resources, which are not necessarily provided locally [2].

This course has two main sections: one focused on effective business coaching, and the second focused on developing a successful business case. Two separate courses that already have more than 2000 students registered together. The section on The Key Stages of Coaching will involve learners in the process of discovery, goal setting, action planning, and follow-up that distinguishes coaching from other development methods. After completing the second section, you will be able to build an effective business case. You will understand what makes a business case, how to prepare one and how to design business cases to persuade decision makers.

Think back to the first time you heard the phrase "neural networks" or "neural nets" -- perhaps it's right now -- and try to remember what your first impression was. As an Applied Math and Economics major with a newfound interest in data science and machine learning, I remember thinking that whatever neural networks are, they must be extremely important, really cool, and very complicated. I also remember thinking that a true understanding of neural networks must be on the other side of a thick wall of prerequisite knowledge including neuroscience and graduate mathematics. Through taking a machine learning course with Professor Samuel Watson at Brown, I have learned that three of the previous four statements are true in most cases -- neural nets are extremely important, really cool, and they can be very complicated depending on the architecture of the model. But most importantly, I learned that understanding neural networks requires minimal prerequisite knowledge as long as the information is presented in a logical and digestable way.

Growth is fundamental to our personal and professional lives. It challenges us to become better people and make the most of every day. The idea of developing new soft and hard skills, although overwhelming at first, is much more manageable and achievable with the right structure and guidance. Those looking to embrace growth as we close out 2020, and head into a new calendar year, will want to check out this roundup of eLearning bundles on sale for Black Friday. Everything in this roundup is an additional 70% off for a limited time, which means now is a great time to pick up more skills and hit the ground running in 2021.

Computer Learning in Automated Manufacturing Processes (CLAMPS) demonstrates the integration of predictive machine learning to drive improvements and minimise variability within a composites manufacturing process. A collaborative project between the Centre for Modelling & Simulation (CFMS) and the National Composites Centre (NCC), highlights the digitalisation and automation steps necessary to ensure consistently high-quality parts, ultimately reducing costs. Together, CFMS and NCC have brought together a combination of traditional virtual manufacturing simulation and artificial intelligence technology to detect and control defect formation.