Nand Kishor is the Product Manager of House of Bots. After finishing his studies in computer science, he ideated & re-launched Real Estate Business Intelligence Tool, where he created one of the leading Business Intelligence Tool for property price analysis in 2012. He also writes, research and sharing knowledge about Artificial Intelligence (AI), Machine Learning (ML), Data Science, Big Data, Python Language etc... ...Full Bio
Nand Kishor is the Product Manager of House of Bots. After finishing his studies in computer science, he ideated & re-launched Real Estate Business Intelligence Tool, where he created one of the leading Business Intelligence Tool for property price analysis in 2012. He also writes, research and sharing knowledge about Artificial Intelligence (AI), Machine Learning (ML), Data Science, Big Data, Python Language etc...
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Google, Microsoft, And Amazon Place Bets On AI In The Enterprise
- Strategy: Leverage Google leadership expertise in AI and Deep Learning (the company has over 7000 AI projects underway in-house, and over one million AI users globally) to provide the most advanced development tools and highest performance hardware platforms for AI development. It is all about the developers since Google doesn't own the users like Microsoft.
- Make TensorFlow the king of AI hardware and software.
- Apply AI to the development of AI. Google claims its recently announced Google Cloud AutoML can greatly simplify the complex tasks of DNN development. Instead of augmenting a pre-trained API with additional custom data (as Microsoft offers), Cloud AutoML builds a custom Deep Learning model, starting with the customer own data. AutoML comes with really cool dashboards so you can easily see the efficacy of the model as you develop and tune it. Google even provides in-house data tagging as a service-a manual process which some people believe will eventually be automated by AIs.
- Broaden Google reach beyond the data center into edge and consumer devices and autonomous vehicles. Capture the entire spectrum of AI development on the Google Cloud Platform.
- Strategy: Use Microsoft massive enterprise and government installed base and its extensive portfolio of productivity and business process tools to become the default provider of ML technologies in the enterprise.
- Provide a wealth of Machine Learning APIs to process every data type, since each company or agency data is distinct to their business. Enable the user to extend the trained neural network with data samples that encompass the organization products, people, vocabulary, etc. ( Microsoft was the first company to go down this path, and now offers 29 APIs-many of which support customization of the DNN training data).
- Provide the highest performing Machine Learning Framework for those customers who need to build their own Deep Neural Networks, especially for natural language processing.
- Enhance every Microsoft product with AI-provide smart features to Office 365, Dynamics, Windows, and eventually every product in the Redmond vault.
- Start by providing the tools and platforms that were developed for Amazon massive online business as services on AWS. Tools developed for Alexa and for Amazon own eCommerce are now available to help you easily build a chat-bot or voice-activated product or service.
- Provide world-class development tools such as the MXNet framework, Lex, Rekognition, and SageMaker to ease the development burden. These tools are all very sticky, ensuring that AWS will be the deployment platform after the development process is finished. SageMaker is especially interesting, offering a fully-managed platform for the entire machine learning development lifecycle.
- Provide the most cost-effective cloud infrastructure for every developer, regardless of which CPU, GPU, or AI Framework the developer selects.