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Considerations for a world where ML models are becoming mission critical. In this post, I share slides and notes from a keynote I gave at the Strata Data Conference in New York last September. As the data community begins to deploy more machine learning (ML) models, I wanted to review some important considerations. Let’s begin by looking at the state of adoption.
Over the last five years, most large enterprises have slowly but surely matured from being data-aware to data-driven. They all collect data from operational and transactional applications; process the data into data lakes, data hubs, data warehouses, and data marts; and build business intelligence (BI) and analytics applications to understand what the data is telling […].
If you’ve heard the debate among IT professionals about data lakes versus data warehouses, you might be wondering which is better for your organization. You might even be wondering how these two approaches are different at all.
AI adoption is reshaping sales and marketing. But is it delivering real results? We surveyed 1,000+ GTM professionals to find out. The data is clear: AI users report 47% higher productivity and an average of 12 hours saved per week. But leaders say mainstream AI tools still fall short on accuracy and business impact. Download the full report today to see how AI is being used — and where go-to-market professionals think there are gaps and opportunities.
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In our ongoing coverage of Integrated Risk Management (IRM) technology and service providers, the relevance and frequency of client inquiry related to Governance, Risk & Compliance (GRC) continues to decline. In 2017, 28% of our relevant client inquiry related to GRC topics. This year, the percentage of GRC client inquiry has slipped to just 15%.
My son and I are really excited about the new NBA season. We are Atlanta Hawks fans, so we’re not too optimistic about this year. We know the team is young and has decided to undertake a rebuilding process. Our mantra for this season is the now familiar “trust the process”. If you’re not aware of the phrase “ Trust the Process ” comes from the Philadelphia Sixers rebuilding efforts over the past couple of years.
My son and I are really excited about the new NBA season. We are Atlanta Hawks fans, so we’re not too optimistic about this year. We know the team is young and has decided to undertake a rebuilding process. Our mantra for this season is the now familiar “trust the process”. If you’re not aware of the phrase “ Trust the Process ” comes from the Philadelphia Sixers rebuilding efforts over the past couple of years.
Not every startup is going to become a world-changing behemoth, but when a small, agile company hits on a truly disruptive idea, it can transform an entire industry.
We’re excited to release Federated Learning , the latest report and prototype from Cloudera Fast Forward Labs. Federated learning makes it possible to build machine learning systems without direct access to training data. The data remains in its original location, which helps to ensure privacy and reduces communication costs. This article is about the business case for federated learning.
It seems that most dispatching software focuses on providing back-office services. While these features are important as many companies struggle with paperwork, they mask the real challenge associated with running a business with significant transportation costs. Companies that deploy nurses, truck drivers, warehouse workers, or cleaning staff live or die based on how effectively they can deploy their teams.
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GAP's AI-Driven QA Accelerators revolutionize software testing by automating repetitive tasks and enhancing test coverage. From generating test cases and Cypress code to AI-powered code reviews and detailed defect reports, our platform streamlines QA processes, saving time and resources. Accelerate API testing with Pytest-based cases and boost accuracy while reducing human error.
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