Big Data Cloud is doing its part to ensure that we all can contribute for the movement & become legitimate citizens of the world of Big Data & Cloud. Big Data Cloud, is a not-for-profit organization for evangelizing to individuals and organizations about Big Data and Cloud technologies. Founded & Operated by Third Eye Consulting Services & Solutions LLC.
TensorFlow is an open source software library created by Google that is used to implement machine learning and deep learning systems. These two names contain a series of powerful algorithms that share a common challenge—to allow a computer to learn how to automatically spot complex patterns and/or to make best possible decisions.
TensorFlow, at its heart, is a library for dataflow programming. It leverages various optimization techniques to make the calculation of mathematical expressions easier and more performant.
Some of the key features of TensorFlow are:
Efficiently works with mathematical expressions involving multi-dimensional arrays
Good support of deep neural networks and machine learning concepts
GPU/CPU computing where the same code can be executed on both architectures
High scalability of computation across machines and huge data sets
Together, these features make TensorFlow the perfect framework for machine intelligence at a production scale.
In this TensorFlow tutorial, you will learn how you can use simple yet powerful machine learning methods in TensorFlow and how you can use some of its auxiliary libraries to debug, visualize, and tweak the models created with it.
Software developers are quickly adopting Artificial Intelligence (AI) technologies, such as natural language understanding, sentiment analysis, speech recognition, image understanding and machine learning (ML). Across a broad range of industries and sectors, AI-infused software applications and cloud services drive innovative customer experiences, augment human capabilities and transform how we live, work and play. New tools, cloud-hosted APIs and platforms make it even easier to build such applications.
Modern AI applications live at the intersection of cloud computing, data platforms and AI tools. The cloud provides a powerful foundation for elastic compute and storage, while supporting special-purpose hardware such as graphics processing units (GPUs) that accelerate demanding calculations. It also enables connectivity, identity, application monitoring and the Internet of Things (IoT). Data platforms in the cloud can ingest and integrate massive volumes of data, use databases and data lakes to transform and analyze the data, and build real-time data-driven applications. Layered upon these capabilities are AI tools and algorithms that help developers build models from the data for targeted intelligent scenarios, and deploy them in a hosted AI application.
Most technologies touted as “game-changing” are just somewhat modified versions of something already in use. Blockchains are different, and while it will take some time to work out the kinks, they could one day make as much of an impact on the world as the database, smartphone or web browser.
If you’ve heard of bitcoin — and unless the state of the world has forced you to stop following the news, you probably have — you’ve heard of the most widely used implementation of a blockchain. But the technology that makes a blockchain special has a large number of potential uses, and cloud computing companies are starting to make it available to their customers.
“When I (recently) heard that Estonia had basically redone its health care system and put all its national health care records on a blockchain, my ears really picked up,” said Jay Bartot, chief technology officer at Madrona Ventures Labs, who was hired last year by Madrona Venture Group to focus on helping startups explore new technologies.
So, what is the blockchain? Let’s sort it all out.
What exactly are we talking about again?
A blockchain is, well, a chain of blocks. It’s a method for sharing a record of an online transaction in a secure and trustworthy way that allows both parties to have a copy of that record without either party having to maintain that record.
The main chain (black) consists of the longest series of blocks from the genesis block (green) to the current block. (Caption and image courtesy Wikimedia Commons / cc 3.0)
Most digital transactions have a middleman, like a bank that verifies Party A has the funds and has duly transferred them to Party B in a trusted fashion. The blockchain allows you to skip the bank and transfer money, information, or other services directly to another entity over the internet in a method that both parties can trust.
Today at Microsoft Connect(); we introduced Azure Databricks, an exciting new service in preview that brings together the best of the Apache Spark analytics platform and Azure cloud. As a close partnership between Databricks and Microsoft, Azure Databricks brings unique benefits not present in other cloud platforms. This blog post introduces the technology and new capabilities available for data scientists, data engineers, and business decision-makers using the power of Databricks on Azure.
Once you manage data at scale in the cloud, you open up massive possibilities for predictive analytics, AI, and real-time applications. Over the past five years, the platform of choice for building these applications has been Apache Spark: with a massive community at thousands of enterprises worldwide, Spark makes it possible to run powerful analytics algorithms at scale and in real time to drive business insights. Managing and deploying Spark at scale has remained challenging, however, especially for enterprise use cases with large numbers of users and strong security requirements.
Enter Databricks. Founded by the team that started the Spark project in 2013, Databricks provides an end-to-end, managed Apache Spark platform optimized for the cloud. Featuring one-click deployment, autoscaling, and an optimized Databricks Runtime that can improve the performance of Spark jobs in the cloud by 10-100x, Databricks makes it simple and cost-efficient to run large-scale Spark workloads. Moreover, Databricks includes an interactive notebook environment, monitoring tools, and security controls that make it easy to leverage Spark in enterprises with thousands of users.
In Azure Databricks, we have gone one step beyond the base Databricks platform by integrating closely with Azure services through collaboration between Databricks and Microsoft. Azure Databricks features optimized connectors to Azure storage platforms (e.g. Data Lake and Blob Storage) for the fastest possible data access, and one-click management directly from the Azure console. This is the first time that an Apache Spark platform provider has partnered closely with a cloud provider to optimize data analytics workloads from the ground up.
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