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Global Big Data Conference

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Amazon's 2019 Climate Pledge calls for a commitment to net zero carbon across their businesses by 2040. Since then, the company has reduced the weight of their outbound packaging by 33%, eliminating 915,000 tons of packaging material worldwide, or the equivalent of over 1.5 billion shipping boxes. With less packaging used throughout the supply chain, volume per shipment is reduced and transportation becomes more efficient. The cumulative impact across Amazon's enormous network is a dramatic reduction in carbon emissions. To make this happen, the customer packaging experience team partnered with AWS to build a machine learning solution powered by Amazon SageMaker.


Should you incorporate an AI strategy into your business? - Dynamic Business

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With both government and companies eagerly adopting artificial intelligence (AI) strategies, we explore how AI could also streamline and scale your business. We examine the potential opportunities and risks that come with using AI, and what the future of AI and business looks like. The CSIRO defines AI as "a collection of interrelated technologies used to solve problems autonomously and perform tasks to achieve defined objectives, in some cases without explicit guidance from a human being." Subfields of AI include machine learning, computer vision, human language technologies, robotics, knowledge representation and other scientific fields. For instance, AI is already being used in autonomous emergency breaking (helping reduce 1,137 vehicle-related deaths per year) and in maintaining Sydney Harbour Bridge (using machine-learning and predictive analytics to identify priority locations for maintenance).


Maxis adopts Google Cloud to boost data analytics, AI and machine learning capabilities

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Malaysian operator Maxis has announced it is working with Google Cloud to integrate data analytics into its business, from consumers to enterprise, network, retail channels and employees. The company's digital analytics transformation programme entails transitioning 100 percent of its business intelligence, data analytics and machine learning on-premise workloads to the cloud. Maxis has also established its Big Data and Advanced Analytics and AI Center of Excellence with data scientists and commitment programmes. Maxis is leveraging Artificial Intelligence and Machine Learning (AI/ML) services from Google Cloud, as well as from Google Cloud partners' technology solutions. Google Cloud and Maxis also plan to jointly develop a curated career development programme to build technical knowledge and in-house expertise, and grow the number of Google Certified Data Engineers within the organisation.


Top 5 programming languages for data scientists to learn

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Data science is a field focused on extracting knowledge from data. Put into lay terms, obtaining detailed information applying scientific concepts to large sets of data used to inform high-level decision-making. Take the ongoing COVID-19 global pandemic for example: Government officials are analyzing data sets retrieved from a variety of sources, like contact tracing, infection, mortality rates, and location-based data to determine which areas are impacted and how to best adjust on-going support models to provide help where it is most needed while trying to curb infection rates. Big data, as it is often called, is the collective aggregation of large sets of data culled from multiple digital sources. These swaths of data tend to be rather large in size, variety (types of data), and velocity (the rate at which data is collected).


30 Best Edureka Free Courses, Tutorial & Certification 2020

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Are you looking for the Best Edureka Courses 2020? Edureka is an online technical training platform that offers Big Data, cloud computing, artificial intelligence, and blockchain-based courses. The classes can be attended to at any place and any time as per your choice Use our Android and iOS App to learn on the go. Their engaging learning platform, expert industry practitioners, and support ninjas make sure that you complete the course. Get lifetime accesses to the entire content including quizzes and assignments as the technology upgrades your content gets updated at no cost? Choose from a number of batches as per your convenience if you got something urgent to do, reschedule your batch for a later time. If you want to get started with top Edureka free courses check out the Edureka course catalog from the Edureka site. You will get tons of free courses online Edureka on the Edureka platform.


What Constitutes a Perfect Data Team?

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Data science is the most promising field in near future, with the advancement of technology and statistical models in recent times, a new data wave is knocking at our doors for a complete revolution. It relates to an interdisciplinary field of study that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from many structural and unstructured data. As diverse does this field sounds, its team also has to be diverse enough to carry out tasks efficiently! To understand this in a better way let's follow the pipeline for a data science project. The most important aspect of this job is to Understand the Business Problem at the beginning, in the meeting with clients, a data science professional asks relevant questions, understands and defines objectives for the problem that needs to be tackled.


3 Hurdles to Overcome for AI and Machine Learning - InformationWeek

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Although we are still in the infancy of the AI revolution, there's not much artificial intelligence can't do. From business dilemmas to societal issues, it is being asked to solve thorny problems that lack traditional solutions. Possessing this endless promise, are there any limits to what AI can do? Yes, artificial intelligence and machine learning (ML) do have some distinct limitations. Any organization looking to implement AI needs to understand where these boundaries are drawn so they don't get themselves into trouble thinking artificial intelligence is something it's not.


AutoML: Bridging the skills gap with machine learning

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Is there anything that can stop AI? As the novel Covid-19 pandemic forces the world to put on its brakes, AI technologies like machine learning – AutoML in particular – have been continuing to develop at break-neck speeds at the beginning of the new decade. Following a recent breakthrough by Google scientists at the start of a period of enforced lockdown, AutoML is seeing a wave of new progress in correlation with the explosion of big data, advanced analytics and predictive models. The increasing amount of viable data has meant that AI, machine learning (ML) and data science is undergoing reams of data and training that has served to boost the technology exponentially. AutoML in 2020, can perform data pre-processing, as well as Extraction, Transformation and Loading tasks (ETL).


Leaders versus Laggards in AI: Latest Findings on Generating ROI from AI

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The gap between leaders versus laggards in AI has widened significantly in the last 6 months, even as leaders are investing big time on pilot projects to transform business teams with AI and Deep Learning. In a powerful survey finding, market research firm ESI ThoughtLab has found out APAC region leads (14.1 Billion USD) in average revenue earned through the adoption of AI applications in 2020. North America ($13.9 billion) and EU ($12.7 Billion) have also reported significant revenue growth from AI adoption. Laggards in AI can drive home success with AI investments by developing a culture of learning and sharing knowledge. ESI ThoughtLab reports AI leaders are constantly amplifying their data science talent pool by acquiring AI businesses.


Interpreting the Scope of AI job Market in the US in current times.

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We are living in a time where everything is digital. Disruptive technologies like artificial intelligence (AI) has become central to this transformation. From retail to Fintech and cybersecurity to predictive analytics, tech pundits avow that AI now plays an essential cog in the future of these industries and disciplines. However, through some alarmists argue that AI is stealing jobs through automation and robotics, on the contrary, it has been observed that AI is also adding new job roles every day to the existing employment pool. Researchers have tracked down new job roles, occupations and emerging industries, in the AI landscape that can help us understand the job market better.