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Nagesh Nama 03.27.24 6 min read

GenAI based Data Visualization for GxP Manufacturing

How can a line supervisor, technical or an automation engineer generate data visualization in the era of GenAI? Answer: A single prompt. (Does your Data Analytics Platform do this?)
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Nagesh Nama 03.21.24 3 min read

Changing the game in patient care with Annalise.ai

Changing the game in patient care with Annalise.ai The realm of artificial intelligence (AI) technology in medical prowess is taking off, and we now transition into a new epoch. Leading the way for this disruptive wave is Annalise.ai—a cutting-edge company marrying medical intellect with the pinnacle of AI technology. This paper will seek to highlight how Annalise.ai has been defining the future of medical imaging:
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Nagesh Nama 03.20.24 11 min read

Unleashing Power of AI in GxP Manufacturing: A Transformative Roadmap

In today's highly regulated and data-driven pharmaceutical and biotechnology industries, the quest for operational excellence, quality assurance, and cost optimization has never been more pressing. As companies grapple with the complexities of Good Manufacturing Practices (GMP) compliance and the ever-growing volumes of data, the integration of Artificial Intelligence (AI) and Machine Learning (ML) emerges as a game-changer.
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Nagesh Nama 03.13.24 5 min read

The Imperative of AI/ML in Pharma Manufacturing

Unlocking Efficiency and Innovation: The Imperative of AI/ML in Pharma Manufacturing Executive Summary In an era where innovation is not just held aloft but expected, the pharmaceutical, biotech, and medical devices manufacturing industries stand at a crossroads. This whitepaper goes deeper into the AI and ML technologies based on immense experience of over three decades of its CEO, Nagesh Nama. This underlines an acute need for the top leadership in life sciences to undertake AI integration: it is not just a technological upgrade of activity but a strategic imperative that allows them operational excellence to gain competitive advantage.
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Nagesh Nama 03.08.24 4 min read

Open Source or Closed Source for AI Development?

The future lies in a balanced approach, taking the best from open-source and closed AI models. Open Source Some of the best technological breakthroughs have been due to the open-source movement, be it the Linux operating system or Apache Web Server software. Open-source AI has to be transparent and an ethic to be powered by the community, which offers enormous advantages. Democratizes development in AI, accelerating innovation, and ensuring the resulting benefits are brought to a wide population. Hugging Face is one of the best examples wherein it has successfully rallied together both researchers and developers from across the globe to come together for innovating in the space of natural language processing. Closed Source However, the potential dangers of open-source AI are too big to ignore. AI technologies in the wrong hands, if misused, could be bad news for national security. Closed AI systems give substantial benefits to the protection of the proprietary information and security of the AI models. Support for faster development cycles, ease of use, and commercial advantage—vital for the United States in competition to hold on to their competitive edge. A Balanced Approach Where critics of open-source AI often question its validity over issues relating to intellectual property protection and commercial viability, these challenges can easily be met with good regulatory frameworks. Clear guidance on the development and deployment of responsible AI will help balance fostering innovation against its perils. The EU's proposed AI Act is an epitome of such a regulatory effort that wishes to set harmonized rules of the game for the development and use of AI systems, including open-source models. The EU's proposal also imposes stricter requirements for AI applications that are considered the most risky and lays down a regulatory framework with a risk-based approach for a well-functioning, balanced, and trusted AI system. Given these considerations, I propose a mixed approach to AI development for the United States. That would encourage taking great benefit from innovation and transparency of the open-source AI, using great frameworks towards security and ethical concerns. For example, we can encourage sharing AI models or researches in such a way that they would be open for community contribution and scrutiny, while keeping certain parts secure. That sets up the need for industry leaders to learn how to be transparent and work with researchers and communities by contributing to open-source AI initiatives. Developers and researchers must contribute to and participate in the open-source project in accordance with existing guidelines and norms in the development of responsible AI. Thus, the debate of whether AI should be open or closed source is in no way binary but a spectrum of possibilities. We need a balanced way, where the cherry-picked values between the strengths of the two models would exist in a way commensurate with the requirements of innovation, national security, and ethical standards in the development of AI. What questions do you have about artificial intelligence in Life sciences? No question is too big or too small. Submit your questions about AI via this survey here.
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Nagesh Nama 03.06.24 3 min read

Artificial Intelligence is bringing hanges to Pharma Manufacturing

Introduction In the world of pharmaceutical manufacturing, especially within sterile environments, professionals face daily challenges that seem pulled straight from a science fiction scenario. Imagine donning a spacesuit, layering gloves, and navigating through a labyrinth of gowning procedures, only to interact with a computer interface that feels decades old. This scenario isn't just hypothetical—it's the reality for many working in biotech drug manufacturing today.
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Nagesh Nama 03.02.24 2 min read

Staying ahead in life sciences means constant innovation & compliance.

Introduction Staying ahead in the life sciences sector means constant innovation and absolute compliance. But here lies the challenge—how do you manage both effectively in the world of software validation? Enter Managed Validation Services—your key to unlocking efficiency, accelerating innovation, and ensuring compliance amidst the rapidly evolving landscape.
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Nagesh Nama 02.21.24 2 min read

SaaS Rise - The Premier SaaS CEO Community

What is SaaS Rise? and How can it help SaaS CEOs? SaasRise is a premier community for SaaS CEOs and founders with annual recurring revenue (ARR) between $1M and $100M. Created by a successful SaaS CEO Ryan Allis with a nine-figure exit, SaasRise offers a platform for growth, scaling resources, and a mastermind group focused on unique challenges and opportunities in the SaaS industry. It's a place for detailed discussions on business models, unit economics, and product-led growth, aiming to support high-growth SaaS leaders through the entire scaling process.
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Author Name 03.18.23 < 1 min read

Is your IT Infrastructure / Apps Data Integrity Compliant?

If you are an SMB Lifescience company, then you need to ask the question: Is my IT Infrastructure / Apps Data Integrity Compliant?
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