Cloud Computing


The Killer App? Cost Savings May Be AI's Biggest Benefit

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KeyBanc Capital Markets' Brent Bracelin and his team write that they met with 18 tech firms from Adobe (ADBE) to Microsoft (MSFT) on a recent AI and cloud-based trip to the West Coast, and one of their main takeaways is that cost savings have become the "killer app" for AI. Bracelin writes that although consumer applications for AI tend to grab headlines, he sees 2018 as the year that it really takes off on the enterprise side. And cost savings are a major way this will happen, as using basic machine algorithms to automate mundane tasks can be a huge boon, freeing up employees for more complex tasks or reducing the headcount needed in a department. That may sound scary from a jobs perspective, but many argue that AI isn't the job killer many fear, and that a human touch will be more valuable with the rise of machine learning. Ultimately, he argues that as corporates adopt AI tools, often embedded in cloud-native applications, they should lead to material cost savings.


Artificial Intelligence, Cloud becoming key technologies: Akash Ambani - ET Telecom

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NEW DELHI: Cloud and Artificial Intelligence (AI) are set to become the bedrocks of communications infrastructure, with both having the potential to grow manifold in the coming years, Akash Ambani, director at Reliance Jio Infocomm, said on Friday. "India's public cloud market is estimated to be $2.6 billion in 2018, and over $4 billion by 2020," Ambani said at the India Digital Open Summit in Mumbai. Research firm Gartner Inc had estimated the public cloud services market in India at $1.81 billion in 2017. Public cloud computing uses cloud computing technologies to support customers that are external to the provider's organisation. Using public cloud services generates the types of economies of scale and sharing of resources that can reduce costs and increase choices of technologies, Gartner said.


2018: The Year of Artificial Intelligence and Insights as a Service {IaaS}

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The digital world is constantly changing--a good thing, especially because new advancements and tools are what propel businesses and entire industries (like healthcare[Office1]) forward. But in order to successfully integrate technology into business operations, you can't only look at what's happening in the world of technology today, you've got to look at what's coming next as well. Here at Futurum, that's our specialty. In this vein, my partner, Daniel Newman, recently brought you five predictions on how blockchain will drive digital transformation and discussed his AI and automation predictions for the future. Today, I'm here to share how 2018 will be the year of AI and insights as a service (IaaS).


Machine Learning Essentials - Level 2

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Shivam Sharma works as a Subject Matter Expert at CloudThat Technologies and has been involved in various large and complex projects with global clients. He has experience in Machine Learning and Microsoft Infrastructure technology stack including Azure Stack, Office 365, EMS, Lync, Exchange, System Center, Windows Servers, designing Active Directory and managing various domain services, including Hyper-V virtualization. Having core training and consulting experience, he is passionate about technology and is involved in delivering training to corporate and individuals on cutting edge technologies. Arzan has 7 years of experience in Microsoft Infrastructure technology stack including setting up Windows servers, designing Active Directory and managing various domain services, including Hyper-V virtualization. As a Cloud Solutions Architect at CloudThat, he is responsible for deploying, supporting and managing client infrastructures on Azure.


IBM Q4 edges expectations, as-a-service run rate now $10.3 billion ZDNet

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SaaS had a major impact on the way companies consume cloud services. This ebook looks at how the as a service trend is spreading and transforming IT jobs. IBM said its as-a-service annual run rate is $10.3 billion exiting the fourth quarter, which was better than expected on earnings and revenue. Big Blue reported a solid quarter relative to expectations and showed growth in its "strategic imperatives" revenue, which includes analytics, cloud and newer businesses. The company reported non-GAAP fourth quarter earnings of $5.18 a share on revenue of $22.5 billion, up 4 percent from a year ago.


Salesforce Einstein AI: An API Tutorial

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Non-Salesforce app developers may be missing out on a hidden gem in the AI world. When developers think of using the cloud for AI, they might think of IBM Watson, Microsoft Azure Cognitive Services, Google Cloud, or Amazon AI. When they hear of Salesforce Einstein, they might automatically assume it's limited to the Salesforce developer specialization. Any app, whether Salesforce-related or not, can leverage the sophisticated AI cloud technologies that Salesforce has acquired. They've entered the AI market with Salesforce Einstein, their own orchestration of AI cloud services.


Oracle to market: We're Cloud 2.0

ZDNet

It's no longer news that Oracle has gotten cloud religion. While not the first to market, Oracle has now been in the cloud market long enough to get within eyeshot of having the full buildout of its global presence and portfolio of services. As one of the established names in the data center, the cloud has turned the tables and placed Oracle in the position of challenger, and not surprisingly, Amazon is dead center in its sights. Oracle's positioning is that its later start to the cloud has provided the chance to learn all the lessons of the first-generation providers. Although not using those exact words, Oracle is framing its platform as Cloud 2.0.


2018 Learning Trends in Cloud Computing Industry

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Democratization of content: In 2018, we will see more professionals outside of the training team contributing in creation of content. The concept of crowd sourcing will evolve in a big way with solution architects, professional services, and support organizations creating the content. Customers and partners will also contribute content as they implement the cloud computing solutions and identify new use cases and design and implementation best practices. This trend will see the role of training organizations evolving. Training teams will act more as a strategy and content curation arm providing tools and templates to the subject matter experts to develop the content and then curating the content.


Serverless Machine Learning with Tensorflow on Google Cloud Platform Coursera

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About this course: This one-week accelerated on-demand course provides participants a a hands-on introduction to designing and building machine learning models on Google Cloud Platform. Through a combination of presentations, demos, and hand-on labs, participants will learn machine learning (ML) and TensorFlow concepts, and develop hands-on skills in developing, evaluating, and productionizing ML models. OBJECTIVES This course teaches participants the following skills: Identify use cases for machine learning Build an ML model using TensorFlow Build scalable, deployable ML models using Cloud ML Know the importance of preprocessing and combining features Incorporate advanced ML concepts into their models Productionize trained ML models PREREQUISITES To get the most of out of this course, participants should have: Completed Google Cloud Fundamentals- Big Data and Machine Learning course OR have equivalent experience Basic proficiency with common query language such as SQL Experience with data modeling, extract, transform, load activities Developing applications using a common programming language such Python Familiarity with Machine Learning and/or statistics Google Account Notes: • You'll need a Google/Gmail account and a credit card or bank account to sign up for the Google Cloud Platform free trial (Google services are currently unavailable in China).


Google Cloud Platform Big Data and Machine Learning Fundamentals Coursera

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About this course: This 1-week accelerated on-demand course introduces participants to the Big Data and Machine Learning capabilities of Google Cloud Platform (GCP). It provides a quick overview of the Google Cloud Platform and a deeper dive of the data processing capabilities. At the end of this course, participants will be able to: • Identify the purpose and value of the key Big Data and Machine Learning products in the Google Cloud Platform • Use CloudSQL and Cloud Dataproc to migrate existing MySQL and Hadoop/Pig/Spark/Hive workloads to Google Cloud Platform • Employ BigQuery and Cloud Datalab to carry out interactive data analysis • Choose between Cloud SQL, BigTable and Datastore • Train and use a neural network using TensorFlow • Choose between different data processing products on the Google Cloud Platform Before enrolling in this course, participants should have roughly one (1) year of experience with one or more of the following: • A common query language such as SQL • Extract, transform, load activities • Data modeling • Machine learning and/or statistics • Programming in Python Google Account Notes: • You'll need a Google/Gmail account and a credit card or bank account to sign up for the Google Cloud Platform free trial (Google services are currently unavailable in China).