Machine learning, as we know, isn’t magic, though it may seem that way sometimes. But it can be at the center of enterprise data integration and cross-domain Master Data Management efforts that may deliver millions of dollars in cost savings and other benefits. In the case of large organizations that have gone through multiple acquisitions, […]
Big Data Ecosystem Updates: Machine Learning, Deep Learning, and the Edge
One of the recent stories within the Big Data ecosystem is that Cisco is joining the AI Hardware frame with a new deep learning server powered by eight GPUs. Cisco is promising support within its AI push for Kubeflow, “which is an open source tool that makes TensorFlow compatible with the Kubernetes container orchestration engine,” […]
Embracing Data Silos: Semantic Search and Analytics Innovation
Walk around any large organization and hear people groan about finding the right data to do their work. In the typical organization, data sits in multiple places, lost behind technical and functional boundaries. These isolated systems, referred to as “data silos,” have often existed for good purposes and reasons such as helping each business function […]
The State of Data Science: Expertise Shortage Creates a Need for Innovation
The world’s most valuable resource is no longer oil but data. As a result, the skills required to convert data into insights that generate revenues are in high demand. Unfortunately, one of the biggest hurdles companies face when trying to capitalize on their data sets is a lack of data scientists and machine learning practitioners. […]
Deploying AI-Driven Systems
Artificial Intelligence (AI) will continue to transform and disrupt most industries but, to really stay competitive, organizations need to deploy AI-Driven systems quickly. However, as Ramesh Mahalingam, CEO of Vizru, an AI-based autonomous applications company, said this is not an easy feat: “AI adoption has proven quite a challenge. It takes enormous time, cost and […]
Data Quality & Data Governance can Maximize Your AI Outcomes
Click to learn more about author Tejasvi Addagada. Clean Data is a crucial need to get an outcome from Machine Learning capabilities. Scale and diversity in data is also another important aspect. How accurate is the data to give a usable outcome – is a major question? Accuracy What is easy to access – are the […]
IoT Data Management Challenges
Who isn’t thinking about the repercussions of an exploding Internet of Things (IoT) market as smart devices further expand their reach in both the commercial and consumer worlds? John McDonald, CEO of ClearObject, an IoT solution provider, works with companies that are seeking to understand what the technology means to their business, how implementations might […]
Data Lineage and Metadata Management: An Innovative Approach
“Every day, companies need to track and trace data movement processes through metadata discovery and data lineage,” emphasizes Amnon Drori, CEO and co-founder of Octopai. This task has not been easy and has often frustrated Drori throughout his twenty-year career. In the past, Drori’s teams have had to spend time checking and rechecking data, a […]
Streamlining the Production of Artificial Intelligence
People often think the algorithms used for Machine Learning (ML) are the most important factors for developing a successful ML system. However, shrewd Artificial Intelligence (AI) and Machine Learning systems in production (managing the data at all stages, with multiple models) have much more impact on the success of the model than the specific learning […]
Machine Learning and Artificial Intelligence Trends in 2019
2019 will be a critical year for Artificial Intelligence (AI) and Machine Learning (ML) technologies as real-world industry applications demonstrate their hidden benefits and value to the consumers. So far, scientists and researchers have made claims on behalf of AI-enabled technologies, but they have not really been tested in large-scale market applications. We will see […]