In this contributed article, Stu Bailey, Co-Founder and Chief AI Architect of ModelOp, discusses how financial services companies can easily validate multiple AI/ML models and reduce ML project costs by 30% through automation. ModelOps refers to the process of enabling data scientists, data engineers, and IT operations teams to collaborate and scale models across an organization. This drives business value by getting models into production faster and with greater visibility, accountability and control.
Monthly Archives: June 2020
Model Risk Management in the Age of AI
Five real-life Netezza performance server use cases
Right now, businesses are focused on getting the most out of their Data and AI platform without overspending to make sure it is operational and running productive workloads. This means having an always-on, 24×7 system that can handle huge spikes in workloads when needed. Many customers have turned to Netezza for this reason, depending on its record of reliability and simplicity.
insideBIGDATA Latest News – 6/29/2020
In this regular column, we’ll bring you all the latest industry news centered around our main topics of focus: big data, data science, machine learning, AI, and deep learning. Our industry is constantly accelerating with new products and services being announced everyday. Fortunately, we’re in close touch with vendors from this vast ecosystem, so we’re in a unique position to inform you about all that’s new and exciting. Our massive industry database is growing all the time so stay tuned for the latest news items describing technology that may make you and your organization more competitive.
Key Trends Framing the State of AI and ML
In this special guest feature, Rachel Roumeliotis, Vice President of Content Strategy at O’Reilly Media, provides a deep dive into what topics and terms are on the rise in the data science industry, and also touches on important technology trends and shifts in learning these technologies.
COVID-19: The Great Artificial Intelligence Accelerator
In this contributed article, Ramayya Krishnan, Ph.D., the W. W. Cooper and Ruth F. Cooper Professor of Management Science and Information Systems at Heinz College and the Department of Engineering and Public Policy at Carnegie Mellon University, discusses how history shows, COVID-19 is likely to further reinforce the profound impact technology and AI have on our daily life. As long as we can collectively prepare for and embrace this reality – all indicators point to a promising future.
Jumpstart your journey to AI expertise: recap of Data and AI Virtual Forum talent sessions on demand
Talent: It’s a key issue impacting today’s AI-hungry organizations. While AI skills are in high demand, organizations admit they’re hard to come by. In fact, the lack of talent scarcity has been called out as one of the top three hurdles to AI adoption, after data complexity, and a lack of trust in AI systems.
Where Predictive Machine Learning Falls Short and What We Can Do About It
In this contributed article, technologist Bernard Brode takes a look at where prediction machine learning falls short, the implications of this, and what can be done about it. Machine learning often fails in unexpected ways, which makes managing the risks of deploying the technology difficult.
Five steps to jumpstart your data integration journey
As coined by British mathematician Clive Humby, “data is the new oil.” Like oil, data is valuable but it must be refined in order to provide value. Organizations need to collect, organize, and analyze their data across multi-cloud, hybrid cloud, and data lakes. Yet traditional ETL tools support only a limited number of delivery styles and involve a significant amount of hand-coding. In turn, enterprises are increasingly looking for machine-learning-powered integration tools to synchronize data for analytics, improve employee productivity, and prepare data for analytics.
Challenges to Consider While Implementing Big Data Strategy in Manufacturing
In this special guest feature, Piyush Jain, Founder and CEO of Simpalm, discusses the many ways in which Big Data has positively influenced the manufacturing industry, along with the major challenges faced in implementation.
Did Big Data Fail Us During COVID-19?
In this contributed article, tech blogger Caleb Danziger observes that governments and organizations across the world have employed big data to respond to the COVID-19 crisis. Some continue to sing its praises, but should that be the case? How has the world of big data affected the fight against coronavirus?
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