Open-Source AI: Community is the Way

This document discusses how telecom companies can leverage artificial intelligence and analytics to drive digital transformation. It identifies key opportunities for AI including improving the customer experience, fraud mitigation, and predictive maintenance. It then outlines the components of a telecom data lake that can support these advanced analytics initiatives. Examples of AI use cases for different telecom business functions like marketing, network operations, and security are also provided. The document argues that a data lake platform optimized for analytics can help telecom companies achieve business and innovation goals through improved operations, new revenue streams, and lower costs.

Cloud Computing Evolution Why Cloud Computing needed? Cloud Computing Models Cloud Solutions Cloud Jobs opportunities Criteria for Big Data Big Data challenges Technologies to process Big Data- Hadoop Hadoop History and Architecture Hadoop Eco-System Hadoop Real-time Use cases Hadoop Job opportunities Hadoop and SAP HANA integration Summary

cloud computing - concepts and technologies and mechanisms of tackling problems in cloud you plz ignore who created it , plz focus on problem oriented points

This document discusses cloud computing concepts, technologies, and business implications. It provides an introduction to cloud models like IaaS, PaaS, and SaaS and demonstrates cloud capabilities through examples of Amazon AWS, Google App Engine, and Windows Azure. The document also discusses enabling technologies for cloud computing like virtualization and programming models for big data like MapReduce and Hadoop.

A new data science approach with cognitive assistance in a data science ecosystem to improve model predictivity.

This document summarizes a presentation on data science and artificial intelligence. It discusses how AI is transforming businesses in many ways, including automating repetitive tasks, improving customer experiences, and driving revenue growth. It also mentions that while data is important, AI is needed to transform organizations through intelligent process optimization and innovation. The document provides examples of how various companies are applying AI in sales, customer service, and other areas. It emphasizes that AI strategies should focus on innovation, identifying high-impact use cases, and developing people's data science skills.

Microservices+Approach+with+IBM+Cloud+Pak+for+Data+-+BACon+2019.pdf

As the adoption of AI technologies increases and matures, the focus will shift from exploration to time to market, productivity and integration with existing workflows. Governing Enterprise data, scaling AI model development, selecting a complete, collaborative hybrid platform and tools for rapid solution deployments are key focus areas for growing data scientist teams tasked to respond to business challenges. This talk will cover the challenges and innovations for AI at scale for the Enterprise focusing on the modernization of data analytics, the AI ladder and AI life cycle and infrastructure architecture considerations. We will conclude by viewing the benefits and innovation of running your modern AI and Data Analytics applications such as SAS Viya and SAP HANA on IBM Power Systems and IBM Storage in hybrid cloud environments.

Financial Services companies are using machine learning to reduce fraud, streamline processes, and improve their bottom line. AWS provides tools that help them easily use AI tools like MXNet and Tensor Flow to perform predictive analytics, clustering, and more advanced data analyses. In this session, you'll hear how IHS Markit has used Machine Learning on AWS to help global banking institutions manage their commodities portfolios. You will also learn how the Amazon Machine Learning Service can take the hassle out of AI.