Monthly Archives: October 2021

Video Highlights: Lessons From the Field in Building Your MLOps Strategy

Our friends over a Comet produced the video presentation below, hosted by Harpreet Sahota, to help you learn when & how to deploy MLOps from experts who have done it! In discussions with leading organizations utilizing ML like The RealReal and Uber, Comet compiled real-world case studies and organizational best practices for MLOps in the enterprise.

AWS Announces General Availability of Amazon EC2 DL1 Instances

Amazon Web Services, Inc. (AWS), an, Inc. company (NASDAQ: AMZN), announced general availability of Amazon Elastic Compute Cloud (Amazon EC2) DL1 instances, a new instance type designed for training machine learning models. DL1 instances are powered by Gaudi accelerators from Habana Labs (an Intel company) to provide up to 40% better price performance for training machine learning models than the latest GPU-powered Amazon EC2 instances.

“Above the Trend Line” – Your Industry Rumor Central for 10/29/2021

Above the Trend Line: your industry rumor central is a recurring feature of insideBIGDATA. In this column, we present a variety of short time-critical news items grouped by category such as M&A activity, people movements, funding news, financial results, industry alignments, customer wins, rumors and general scuttlebutt floating around the big data, data science and machine learning industries including behind-the-scenes anecdotes and curious buzz.

Observability: What Does the Future Hold?

In this special guest feature, Abel Gonzalez, Director of Product Marketing, Sumo Logic, lays out where Observability is going for the enterprise as well as explaining where we’ve been and why it’s important. At the end of the day, it’s critical to connect observability back to the end goal of the business—to serve its customers, community, and shareholders. Because that’s really what it’s all about. 

Heard on the Street – 10/28/2021

Welcome to insideBIGDATA’s “Heard on the Street” round-up column! In this regular feature, we highlight thought-leadership commentaries from members of the big data ecosystem. Each edition covers the trends of the day with compelling perspectives that can provide important insights to give you a competitive advantage in the marketplace.

Seven Steps to Effective AI Adoption for Your Enterprise

In this contributed article, Rinat Gareev, a Solution Architect and ML Practice Lead at Provectus, details 7 important steps that can help your organization overcome the barriers to successful AI adoption. Today many executives realize that the future success of their business may depend on the ability to effectively implement an AI strategy, to keep pace with rapid AI-driven digitalization across almost all sectors and industries. 

IBM Safer Payments recognized as ”Fraud detection and prevention product of the year”

On its 25th anniversary, Asia recognized IBM Safer Payments as “Fraud detection and prevention product of the year.” This recognition is a testament to the IBM RegTech team’s commitment to bringing next generation risk and compliance solutions to serve clients across the globe and also to meeting geo-specific industry and business needs. IBM Safer Payments won this award specifically for bringing agility to fraud fighting and for its differentiated capabilities. The solution is also well positioned to serve clients in Asia as the region gears up for disruptions from the proliferation of faster payments and the emergence of challenger banks. 

Bringing agility to fraud fighting

Fighting financial crimes and fraud has never been easy. The acceleration of digital transformation and economic uncertainty resulting from the pandemic have added more pressure on fraud management. This calls for more agile fraud management approaches.

  • IBM Safer Payments enables fraud management teams to use any data science, any machine learning, and artificial intelligence technique – proprietary or open source – to outsmart fraudsters.
  • The platform supports bring-your-own-model options where users can build externally and then import neural networks, random forests, decision trees, and regressions, using their preferred tools.
  • Models of different types can be combined into ensembles, to leverage the best of each modeling capability
  • In addition, Safer Payments includes embedded training facilities for popular machine learning algorithms.
  • Everything in Safer Payments is user-configurable and allows for adaptation to customer data model and ultra-fast configuration of new data feeds.

This unparalleled flexibility offered by IBM Safer Payments enables fraud management teams to keep pace with new fraud schemes and mount defenses well in advance.

 A truly differentiated fraud platform

Furthermore, IBM Safer Payments does not a necessitate ‘rip and replace’ approach. The solution can integrate with and augment existing fraud detection solutions without impacting operational efficiency. The solution also:

  • Enables fraud analysts to update models on the fly with little or no vendor dependencies,
  • Applies machine learning to not just train models, but also to discover new rules that fraud analysts can quickly test and integrate into the current fraud detection engine,
  • Is proven to reduce false positive ratio down to the range 1:1 to 1:3.

Upholding customer experience in Asia Pacific and across the globe

More than ever, customers demand superior seamless and frictionless experience in their digital interactions. User-centered design in customer experience can be competitive advantage. Successful organizations leverage technology to deliver the highest levels of customer experience and service. For instance, IBM recently announced that digital bank Volt Bank has integrated IBM Safer Payments solution into its online banking platform. This helps Volt give their clients a more seamless and secure banking experience across the company’s entire platform. Read about the Volt Bank story here.

Financial services technology leader FIS, looking for real-time decisioning and increased flexibility, chose IBM Safer Payments to help clients counter person-to-person payments fraud in the U.S. Read the FIS press release here.

And Bank of New Zealand announced previously that they selected IBM Safer Payments to deliver cross-channel fraud protection to its customers. The deal supports BNZ’s efforts to provide frictionless and safer payments experience to their customers. Read about the Bank of New Zealand approach here.

Join the data science revolution

IBM Safer Payments enables your fraud prevention teams to adapt their controls faster to emerging threats and detect fraud with greater speed and accuracy — and without vendor or data scientist dependencies. As part of the IBM RegTech risk and compliance solutions, Safer Payments has the analytics and simulation tools needed to continuously monitor business performance and adapt the decision model to emerging and modified fraud patterns.

Because it has an open platform, customers can leverage their existing detection models and IP all at a low total cost of operation (TCO), as all required components are self-contained and run on commodity hardware. To learn more about Safer Payments visit

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Faces as the Future of AI

In this contributed article, Dr. Sergey I. Nikolenko, Head of AI at Synthesis AI, discusses how in AI, problems related to human faces are coming to the forefront of computer vision. The article considers some of them, discusses the current state of the art, and introduces a common solution that might advance it in the near future.

5 Expert Tips to Help Build and Maintain a Powerful Customer Dashboard

n this special guest feature, Dave Hurt, CEO and Co-founder at Verbdata, discusses how building, implementing, and maintaining dashboards requires a deep understanding of how to invest resources and time in product and engineering. While this task can be laborious, to begin with, planning ahead not only ensures customer satisfaction and optimum performance but provides actionable insights to assist the decision-making process. 

2022 Trends in Data Modeling: The Interoperability Opportunity

In this contributed article, editorial consultant Jelani Harper offers some intriguing trends for 2022 centered around data modeling and the interoperability opportunity. A plethora of methods including data fabrics, revamped cloud native Master Data Management capabilities, and governance frameworks employing cognitive computing to point-and-click at sources for detailed cataloging of their data are viable means of implementing data models across the heterogeneity of the modern enterprise’s data.