For all the talk about data fueling digital transformation, it would seem the world remains in a strange limbo where a few companies embrace this concept. Many continue to be slow followers, while others are not yet familiar with the idea. A new report from Gartner, titled Applied Infonomics: Seven Steps to Monetize Available Information Assets (Nov 2018) shows top performers are more than twice as likely as typical performers to have monetized information assets, and eight times more likely to have done so than trailing organizations. From my experience over the past 20 years in analytics and data management, the main challenge for those companies in the middle and those that are laggards is that they generally lack the vision of what could be done differently from their traditional information processes. They are waiting for vendors to bring their best practices, use cases and ideas.
The Azure Machine Learning service speeds up the process of identifying useful algorithms and machine learning pipelines, which automates model selection and tuning. This can cut development time from days to hours, said Bharat Sandhu, director of product marketing, big data and analytics at Microsoft. It also provides DevOps capabilities, via integrated CI/CD tooling, to enable experiment tracking and manage machine learning models deployed in the cloud and on the edge, said Venky Veeraraghavan, group program manager for Microsoft Azure, in a blog post. The Azure Machine Learning service supports popular open source frameworks, including PyTorch, TensorFlow and scikit-learn, so developers and data scientists can use familiar tools. Developers can use Visual Studio Code, Visual Studio, PyCharm, Azure Databricks notebooks or Jupyter notebooks to build apps that use the service.
The portion of marketers using AI to connect with customers is growing, a new survey shows, even though few are satisfied with the ability to balance personalization tools with privacy. In 2018, 29 percent of marketers used AI, according to the fifth edition of the Salesforce State of Marketing report, which surveyed more than 4,100 marketing leaders worldwide. By comparison, just 20 percent of marketers used AI in 2017. The 2018 AI adoption rate was higher, at 40 percent, among "high-performing" marketers -- those who said they are completely satisfied with their overall marketing performance and the outcomes of their marketing investments. Also: Can humans get a handle on AI?
It's no secret that data scientists continue to be among the most sought-after professionals in all of IT. As organizations continue to look for ways to gain value and insights from their data, these are the people they frequently turn to in order to make sense of all the information pouring into their systems from a growing number of sources. The good news for companies desperate to find these needed skill sets is that data science is becoming "democratized," which will help bridge the talent gap. Five factors are democratizing data science and putting this critical capability into the hands of more professionals, potentially alleviating the crippling talent shortage, according to a report released today from consulting firm Deloitte. Some estimates show that data scientists spend about 80 percent of their time on repetitive and tedious tasks -- data preparation, feature engineering and selection, and algorithm selection and evaluation -- that can be fully or partially automated.
When you think of "data science" and "machine learning", do the two terms blur together, like Currier and Ives or Sturm and Drang? If so, you've come to the right place. This article will clarify some important and often-overlooked distinctions between the two to help you better focus your learning and hiring. Machine learning has seen much hype from journalists who are not always careful with their terminology. In popular discourse, it has taken on a wide swath of meanings and implications well beyond its scope to practitioners.
We've all heard the dire predictions about robots coming to steal our jobs. As technologies such as machine learning, AI and automation advance by the day, workplaces everywhere are being transformed; naturally, some people fear that they will become redundant -- depending on their job, some may be right. In this age of AI and ML, ambivalence is ripe; but something ironic has emerged: when it comes to advancing these cutting-edge technologies, the lack of human skills and knowledge is slowing innovation down. In a new survey by Cloudera, the software firm, exploring the benefits and roadblocks of ML adoption across Europe, 51% of business leaders said that the skills shortage was holding them back from implementation. According to Cloudera, companies are eager to use ML -- it's second only to analytics as the key investment priority for businesses; ahead of other disciplines like IoT, artificial intelligence and data science.
If you have been tempted to solve the numerous IQ tests that have plagued the social platforms, then you are not alone. Of late, emotional intelligence has been quantified for centuries through psychological tests and examinations that test your intelligence, personality, vocational interests, attitude, achievement, aptitude, and observational powers. It was never so easy to capture emotional intelligence as it is now. Human behaviour can be predicted by understanding and calculating one's emotions. AI startups have developed the technology where facial emotions and micro-expressions that lasted for seconds are studied and examined to establish patterns, then evaluated to find out what provokes these emotions, to conclusively determine underlying behaviour traits.
This article was originally published on TechRepublic. Aerial imagery: Photos taken from the air, often with UAVs in smart farming. Used to assist farmers to determine the condition of a field. It is the integrated internal and external networking of farming operations as a result of the emergence of smart technology in agriculture. Agro-chemicals: Chemicals used in agriculture, which include fertilizers, herbicides, and pesticides.
This is part two of this series, find part one here - How to build a data science project from scratch. After scraping or getting the data, there are many steps to accomplish before applying a machine learning model. You need to visualize each of the variables to see distributions, find the outliers, and understand why there are such outliers. What can you do with missing values in certain features? What would be the best way to convert categorical features into numerical ones?
Organisations are increasingly adopting cloud-based applications to reduce costs and give them the scalability and agility to transform in an increasingly fast-evolving business landscape. Through doing so they are generating a large amount of data that could provide actionable insights, but few are implementing data analytics strategically. The most innovative companies in the world, including Facebook, Amazon, Netflix and Google, are not only underpinned by the cloud, but recognise that their whole business model is based upon data. Indeed, Netflix said in 2016 that its cloud-based recommendation engine, powered by user data, is worth more than $1 billion annually. Closer to home, Ocado has disrupted the saturated supermarket industry by placing cloud data and automation at the core of its proposition, and even productised much of its technology stack for sale to other retailers.