The data science process is extensively covered by resources all over the web and known by everyone. A data scientist connects to data, splits it or merges it, cleans it, builds features, trains a model, deploys it to assess performance, and iterates until they’re happy with it. That’s not the end of the story though. Next, you need to try the model on real data and enter the production environment. These two environments are inherently different because the production environment is continuously running – and potentially impacting existing internal or external systems. Data is constantly coming in, being processed and computed into KPIs, and going through models that are retrained frequently. These systems, more often than not, are written in different languages than the data science environment.
Interestingly, the top two answers, "Communication skills" (4.10 on a five-point scale) and "Track record of getting things done" (4.09), aren't usually explicitly quantifiable criteria. They're also things you can get across before even getting an interview using a strong resume or cover letter, respectively. Of course, hard skills are also very important, as we see knowledge of algorithms, data, and frameworks filling out the next two top spots. Once you've picked the right people, you need to ensure they're collaborating effectively, which is why Stack Overflow also asked about favored development practices:
To craft high-performance IoT apps, developers need a federated environment that distributes algorithmic capabilities for execution at IoT network endpoints, also known as “edge devices.” Federation is essential because many IoT edge devices — such as mobile phones — lack sufficient local resources for storing all data and executing all the algorithms needed to do their jobs effectively. Key among the capabilities being federated to the IoT edges are machine learning, deep learning and other cognitive-computing algorithms. These analytic capabilities enable IoT edge devices ... to make decisions and take actions autonomously based on locally acquired sensor data. In particular, these algorithms drive the video recognition, motion detection, natural-language processing, clickstream processing and other real-time pattern-sensing applications upon which IoT apps depend.
“This is high-quality work,” says Evangelos Kontopantelis, a data scientist at the University of Manchester in the United Kingdom who works with primary care databases. He says that dedicating more computational power or more training data to the problem “could have led to even bigger gains.” Several of the risk factors that the machine-learning algorithms identified as the strongest predictors are not included in the ACC/AHA guidelines, such as severe mental illness and taking oral corticosteroids. Meanwhile, none of the algorithms considered diabetes, which is on the ACC/AHA list, to be among the top 10 predictors. Going forward, Weng hopes to include other lifestyle and genetic factors in computer algorithms to further improve their accuracy.
Chatbots are the biggest innovation in customer service ever since businesses created web portals for customers to serve themselves. Email and live chat may have increased the interaction between firms and clients. However, chatbots are available 24 hours a day and will interact with customers in the same way a human would. Since most customer queries do not require human intervention, chatbots save you money by automating your customer service. You can now put an end to automated email replies and unavailable live chat services. ... One of the reasons chatbots may herald the end of apps is that they speak the same language as the user. The language used by apps to interact with customers is frankly, not engaging or friendly.
There are many challenges that ASR engines need to address. For example, recognition accuracy is affected by the quality of the microphone used, and by the level of background noise. Refinements in signal processing and acoustic modelling help to create more noise-robust speech recognition, which is especially important as ASR use cases move from relatively quiet offices and homes to noisier mobile environments. People's accents and speaking styles also vary widely, of course, which is why most ASR systems benefit from the creation of user profiles from supplied training texts, so the decoder can fine-tune its "speaker-independent" acoustic model. People may also use words that are not in the language model or the lexicon, so the software also needs to be able to add "out of vocabulary" words and record their pronunciation.
In short - we don’t have much patience when it comes to bad user experience. As a result, near perfection has become a must to survive in the competitive tech environment. The job of an information architect is to maintain a competitive advantage by making sure things are where they should be, and believe me, it’s not always easy. As you’ll soon discover, there is a lot to think about. So what do we really mean by IA? I’ll begin by explaining, in layman’s terms, what it means. If you scroll down, you’ll find 8 easy principles that highlight some crucial things to think about when designing the IA of a website. Further down, I explain the many ways that good IA will benefit both the user and your bottom line, and finally I share some handy processes to get you started, plus a list useful tools to use when designing the IA of a site.
In today’s world of disruptive technology innovation, needless to say that Lean Principles apply to any field of IT, and as we will see now, Lean Principles also apply to more than just manual processes in IT environment. About Ericsson: Ericsson is a global leader in delivering ICT solutions, carrying over 40% of the world's mobile traffic through its networks. It has customers in over 180 countries and comprehensive industry solutions ranging from Cloud services and Mobile Broadband to Network Design and Optimization. In our service delivery unit IT & Cloud (SDU IT&C), we commenced the Lean Journey with small steps around five years ago. We selected a few important KPIs aligned with the organization’s strategy and initiated lean transformation programs on those areas which helped us by delivering consistently on the following parameters
There is no going back, only forward. We don’t get to pick and choose when technological progress stops or where. People whose jobs are on the chopping block of automation are afraid that the current wave of tech will impoverish them, but they also depend on the next wave of technology to generate the economic growth that is the only way to create sustainable new jobs. I understand that it is far easier to tell millions of newly redundant workers to “retrain for the information age” or to “join the entrepreneurial economy” than to be one of them or to actually do it. And who can say how quickly all that new training will also become worthless? What professions today can be called “computer proof”? ... Compare what a child can do with an iPad in a few minutes to the knowledge and time it took to do basic tasks with a PC just a decade ago. These advances in digital tools mean that less training and retraining are required for those whose jobs are taken by robots.
In a nutshell, a serverless platform needs the application developers to think and write business logic in the form of functions which are invoked when an event is dispatched to the system. Event streams are central to Serverless Architectures especially in AWS’s Lambda implementation. Any interaction with the platform such as an user’s request or mutation of state such as updating an object in the data store generates events, which is streamed into a user defined function for processing the event and accomplishes any domain specific concerns. ... Companies like Netflix, Google, and Facebook have invested significantly in this area during the course of building modern platforms for their consumer facing services. Each of these companies have a proven track record for their quality of service despite running on commodity hardware and network.
Quote for the day: "The question of whether Machines Can Think... is about as relevant as the question of whether Submarines Can Swim." -- Edsger W. Dijkstra