AI

Pluralsight – Modernise Your It Infrastructure For AU

Pluralsight – Modernise Your It Infrastructure For Ai-NOLEDGE
English | Size: 80.45 MB
Category: Tutorial


Notes: Big Data LDN 2019 | Modernise Your IT Infrastructure for AI | Brian Carpenter
In this session, learn about the latest best practices for designing end-to-end AI platforms for your data science teams. You can seamlessly fit AI projects into a unified analytics plan with the right infrastructure built for scaling Brian Carpenter will map out how Pure Storage can support your overall AI strategy by walking through an example AI data hub microservices deployment

Pluralsight – Dataops For Ai And Digital Transformation

Pluralsight – Dataops For Ai And Digital Transformation-NOLEDGE
English | Size: 107.36 MB
Category: Tutorial


Notes: Big Data LDN 2019 | DataOps for AI and Digital Transformation | Jay Limburn
Organizations are under competitive disruptive, and regulatory pressures Leveraging data and AI at the speed of business is the biggest differentiator. However, 81% of organizations don’t understand their data provides little to no value. For those aiming to succeed in digital transformation and AI, DataOps is essential to get to business-ready data providing automated, curated, and trusted data pipeline between data providers and data consumers. That means a scalable, agile, and faster path to achieving business objectives In this session, IBM presents DataOps methodology with demonstrations to help you maximize your people, process and technology to accelerate journey to AI and digital transformation

Pluralsight – From Zero To AI Hero Microsoft Azure Plus AI Conference 2019

Pluralsight – From Zero To Ai Hero Microsoft Azure Plus Ai Conference 2019-NOLEDGE
English | Size: 145.53 MB
Category: Tutorial


Notes: Don’t miss the upcoming Microsoft Azure + AI event on December 8-10 2020 in Las Vegas, Nevada. Automated ML is an emerging field in Machine Learning that helps developers and new data scientists with little data science knowledge build Machine Learning models and solutions without understanding the complexity of Learning Algorithm selection, and Hyper parameter tuning. With Azure Machine Learning’s automated machine learning capability, given a dataset and a few configuration parameters you will get a trained high quality Machine Learning model for the dataset that you can use for Predictions. In this session with Rachel Kellam, you will learn how CBRE & Walgreen-Boots are using it for productivity gains empowering domain experts to build ML
based solutions and scale to build several models with Azure Machine Learning’s automated ML

Pluralsight – A Deep Dive Into Conversational AI Using Azure Bot Service And Cognitive Services

Pluralsight – A Deep Dive Into Conversational Ai Using Azure Bot Service And Cognitive Services-NOLEDGE
English | Size: 162.38 MB
Category: Tutorial


Notes: Microsoft Ignite 2019 | A Deep-dive into Conversational AI using Azure Bot Service and Cognitive Services | Darren Jefford
Enterprises have a significant need to deliver a conversational assistant tailored to their brand, personalized to their users, and made available across a broad range of devices and social canvases. In this session, we deep dive into building conversational AI solutions leveraging the latest tools from the Azure Bot Service and Cognitive Services. We also showcase how to build an end-to-end solution using the open-source Virtual Assistant that provides developers and enterprises a set of core foundational capabilities and full control over the end-user experience

Pluralsight – Research Vs Reality In Ai Would You Trust Your Model With Your Life

Pluralsight – Research Vs Reality In Ai Would You Trust Your Model With Your Life-NOLEDGE
English | Size: 97.86 MB
Category: Tutorial


Big Data LDN 2019 | Research vs. Reality in AI: Would You Trust Your Model with Your Life? | Heather Gorr
There are many considerations before deploying deep learning models into the real world, especially in safety-critical environments like automated driving, smart medical devices, aerospace, and biomedical applications. A deep learning researcher can achieve 99% accuracy on a deep learning model, but what about the edge cases? What if those edge cases represent someone’s life? Is AI ready to move from research to reality? Model accuracy is only one part of a production-ready system, which also includes: model justification and documentation, rigorous testing, use of specialized hardware (GPUs, FPGAs, cloud resources, etc.), and collaboration between multiple people with various expertise related to the project and system. In this session, Heather Gorr will discuss the importance of explainable models, system design, and testing before an AI system is production-ready.