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2019 | Buch

Democratization of AI Using Cognitive Services Kapitel lesen Erstes Kapitel lesen

Cognitive Computing Recipes

Artificial Intelligence Solutions Using Microsoft Cognitive Services and TensorFlow

verfasst von: Adnan Masood, Adnan Hashmi

Verlag: Apress

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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Über dieses Buch

Solve your AI and machine learning problems using complete and real-world code examples. Using a problem-solution approach, this book makes deep learning and machine learning accessible to everyday developers, by providing a combination of tools such as cognitive services APIs, machine learning platforms, and libraries.

Along with an overview of the contemporary technology landscape, Machine Learning and Deep Learning with Cognitive Computing Recipes covers the business case for machine learning and deep learning. Covering topics such as digital assistants, computer vision, text analytics, speech, and robotics process automation this book offers a comprehensive toolkit that you can apply quickly and easily in your own projects. With its focus on Microsoft Cognitive Services offerings, you’ll see recipes using multiple different environments including TensowFlow and CNTK to give you a broader perspective of the deep learning ecosystem.
What You Will LearnBuild production-ready solutions using Microsoft Cognitive Services APIs
Apply deep learning using TensorFlow and Microsoft Cognitive Toolkit (CNTK)
Solve enterprise problems in natural language processing and computer vision
Discover the machine learning development life cycle – from formal problem definition to deployment at scale
Who This Book Is For
Software engineers and enterprise architects who wish to understand machine learning and deep learning by building applications and solving real-world business problems.

Inhaltsverzeichnis

Frontmatter
Chapter 1. Democratization of AI Using Cognitive Services
Abstract
Unless one dwells under a rock, the disruption “havoc” that artificial intelligence, machine learning, and deep learning have been wreaking in different industries, verticals, and human lives cannot go unnoticed. When you ask Alexa to turn off the lights and set the thermostat to a chilly 67 degrees so you can cozy up to read this book, you are making use of a multitude of machine-learning and deep-learning technologies, from speech recognition to IoT and natural language understanding and processing. It has been said that the best technologies are the ones that run in the background, delivering a seamless human experience and value; artificial intelligence is quickly emerging to be the ambient caretaker.
Adnan Masood, Adnan Hashmi
Chapter 2. Building Conversational Interfaces
Abstract
Conversation as a platform, or simply “bots,” is quickly becoming the present, and irrefutably the future, of modern user interfaces. However ungainly conversational interfaces may appear in their current state, chatbots are the perfect microcosm of what we expect from AI applications in the real world, from sentiment analysis and natural language processing to comprehension, questioning and answering mechanisms, visual interaction and search, topic modeling and entity extraction, multi-turn dialogs, transaction processing, multi-modal conversations, natural language generation—you name it! It takes an AI village to build a functioning, resilient, usable, and interactive conversational event.
Adnan Masood, Adnan Hashmi
Chapter 3. Seeing Is Believing: Custom Vision
Abstract
The integration and use of computer-vision technologies in context with artificial intelligence and machine learning has become a topic of immense interest in both academia and industry. As computation power increases, new algorithms and techniques become available to be used for next generation of computer-vision research and development.
Adnan Masood, Adnan Hashmi
Chapter 4. Text Analytics: The Dark Data Frontier
Abstract
Text is everywhere. Analysts at Gartner estimate that upward of 80 percent of enterprise data today is unstructured. Our everyday interactions generate torrents of such data, including tweets, blog posts, advertisements, news, articles, research papers, descriptions, emails, YouTube comments, Yelp reviews, surveys from your insurance company, and call transcripts; there is a tremendous amount of unstructured data, and the majority of it is text. Another general way to describe this large amount of mostly monetizable data (except YouTube comments—those are toxic!) is by classifying it as dark data. The origin of this term is not well known, but it was popularized by Stanford’s Dr. Chris Re, who founded the DeepDive program for extracting valuable information from dark data. The term pertains to the mountains of raw information collected in various ways, and such data remains difficult to analyze.
Adnan Masood, Adnan Hashmi
Chapter 5. Cognitive Robotics Process Automation: Automate This!
Abstract
As automation becomes a norm in digital businesses, technology professionals are fast embracing it as a tool for creating operational efficiencies. In more recent years, robotics process automation (RPA), or IPA (intelligent process automation), has been helping out businesses by providing much-needed relief from doing mundane and repetitive tasks.
Adnan Masood, Adnan Hashmi
Chapter 6. Knowledge Management & Intelligent Search
Abstract
Knowledge management is a rather large and formidable discipline that deals with identifying, capturing, storing, retrieving, archiving, and sharing information, both inside and outside an organization. Imagine working as a helpdesk representative and receiving a ticket that requires you to address a certain issue within an application. Now consider getting all the information that is automatically associated with this ticket, such as similar tickets and their fixes, application telemetry data and logs, standard operating procedures, and other knowledge-base items associated with the application, such as wiki articles. The knowledge graph associated with this application even recommends a potential fix and provides information about the associate who last worked on a similar issue, or the same application.
Adnan Masood, Adnan Hashmi
Chapter 7. AIOps: Predictive Analytics & Machine Learning in Operations
Abstract
The operations landscape today is more complex than ever. IT Ops teams have to fight an uphill battle managing the massive amounts of data that is being generated by modern IT systems. They are expected to handle more incidents than ever before with shorter service-level agreements (SLAs), respond to these incidents more quickly, and improve on key metrics, such as mean time to detect (MTTD), mean time to failure (MTTF), mean time between failures (MTBF), and mean time to repair (MTTR). This is not because of lack of tools. Digital enterprise journal research suggests that 41 percent of enterprises use ten or more tools for IT performance monitoring, and downtime can get expensive when companies lose a whopping $5.6 million per outage and MTTR averages 4.2 hours and wastes precious resources. With a hybrid multi-cloud, multi-tenant environment, organizations need even more tools to manage the multiple facets of capacity planning, resource utilization, storage management, anomaly detection, and threat detection and analysis, to name a few.
Adnan Masood, Adnan Hashmi
Chapter 8. AI Use Cases in the Industry
Abstract
In the final chapter of this book, we want to show the art of the possible, connecting technology to real-world use cases and business scenarios. We are going to use the same problem–solution format in this chapter as in the rest of the book. However, we will not get into detailed discussion on the implementation details of the potential solutions at this point.
Adnan Masood, Adnan Hashmi
Backmatter
Metadaten
Titel
Cognitive Computing Recipes
verfasst von
Adnan Masood
Adnan Hashmi
Copyright-Jahr
2019
Verlag
Apress
Electronic ISBN
978-1-4842-4106-6
Print ISBN
978-1-4842-4105-9
DOI
https://doi.org/10.1007/978-1-4842-4106-6

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