Uncover the AI strategies powering F&B manufacturing
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Why AI adoption is a strategic imperative
By: TierPoint
Type: eBook
Artificial intelligence (AI) is no longer a technological curiosity but a catalyst reshaping business processes. In the era of ChatGPT, Gemini, Dall-E, Midjourney, and Stable Diffusion, AI’s omnipresence in generating text, images, and code reflects a broader transformative trend.
In this eBook, you’ll learn about AI’s multifaceted nature, its historical evolution, and its potential business impact. Embracing AI is not just an option for businesses—it is a strategic imperative.
Dive into the eBook to learn about:
- The history of AI
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The future of manufacturing with generative AI
By: Amazon Web Services
Type: eBook
Discover how Amazon Web Services (AWS) can help your industrial organization harness the transformative power of generative artificial intelligence (AI) to:
- Develop new product designs faster and more cost-effectively
- Minimize downtime and optimize production
- Increase supply chain visibility while mitigating risks
Read the ebook now to learn more.
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Deep learning vs. machine learning in cybersecurity
By: Deep Instinct
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CISOs like you have been turning to AI-based solutions to augment your cybersecurity. Gartner predicts that by 2035, 90% of detection and 60% of responses to cyberattacks will be handled by AI.
But not all AI tools are created equal. This eBook compares the differences between deep learning and machine learning tools for cybersecurity, helping you determine which makes the most sense for your business. You will also learn:
- The evolution of AI in cybersecurity solutions
- What should you look for in a DL-based solution?
- Questions to ask to know if it's truly a Deep Learning-based solution
Read the eBook here.
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Join Patrick Hall, one of the authors of the O’Reilly book “Machine Learning for High-Risk Applications: Approaches to Responsible AI” and principal scientist at BNH.AI, and Triveni Gandhi, Responsible AI Lead at Dataiku, for a riveting discussion on AI risk management and Responsible AI for data practitioners. The conversation will touch on concepts from the O’Reilly book, notably a snapshot of the legal and regulatory landscape, the NIST AI risk management framework, and model governance. Plus, get some practical takeaways on how to implement the NIST framework using Dataiku. Be sure to get a complimentary of the full O’Reilly ebook in the "Attachments" section below!
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Modern ML Risk Management Strategies
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Join Patrick Hall, one of the authors of the O’Reilly book “Machine Learning for High-Risk Applications: Approaches to Responsible AI” and principal scientist at BNH.AI, and Triveni Gandhi, Responsible AI Lead at Dataiku, for a riveting discussion on AI risk management and Responsible AI for data practitioners. The conversation will touch on concepts from the O’Reilly book, notably a snapshot of the legal and regulatory landscape, the NIST AI risk management framework, and model governance. Plus, get some practical takeaways on how to implement the NIST framework using Dataiku. Be sure to get a complimentary of the full O’Reilly ebook in the "Attachments" section below!
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