Para desvendar os mistérios da Inteligência Artificial e seu impacto na criação de conteúdo, recebemos Leandro Conti e Ahirton Lopes para uma conversa imperdível sobre o uso da IA e os avanços relacionados com a Creator Economy. 

Neste episódio do Data Hackers — a maior comunidade de AI e Data Science do Brasil-, em parceria com a Compasso Coolab no Hacktown 2024, conheçam: Leandro Conti  — Vice presidente global de Assuntos Corporativos na Hotmart; e Ahirton Lopes — Head of Data na TIVIT. Eles também contam insights poderosos para turbinar sua produtividade e impulsionar seus conteúdos nessa era digital.

Lembrando que você pode encontrar todos os podcasts da comunidade Data Hackers no Spotify, iTunes, Google Podcast, Castbox e muitas outras plataformas. Caso queira, você também pode ouvir o episódio aqui no post mesmo!

Nossa Bancada Data Hackers:

Paulo Vasconcellos — Co-founder da Data Hackers e Principal Data Scientist na Hotmart. Monique Femme — Head of Community Management na Data Hackers

How is data playing a part of the future of AI security? Where is private data hidden? Where should your company start when thinking about integrating AI and Gen AI into their technologies? Thomas Ryan, Chief Executive Officer and Founder of Bigly Sales Inc. joins us on this episode to discuss the status of data privacy with the advent of AI.

data #datascience #dataanalytics #AI #artificialintelligence #security #genai #LLM #podcast #datastorage #technology #innovation

Cyberpunk by jiglr | https://soundcloud.com/jiglrmusic Music promoted by https://www.free-stock-music.com Creative Commons Attribution 3.0 Unported License https://creativecommons.org/licenses/by/3.0/deed.en_US Hosted on Acast. See acast.com/privacy for more information.

Uncle Rico is a character in the movie Napoleon Dynamite, who is stuck in the past, reminiscing about his days as a high school football star. If only he'd won the game and went to the state championship. Some of the data industry reminds me of Uncle Rico.

During a recent panel, there was a question about whether AI can help with data management (governance, modeling, etc).

Some people were quick to dismiss this, saying that machines are no substitute for humans in their understanding and translating of "the business" to data.

Yet why are we still perpetually stuck in the mode of "80% of data projects fail"? Might AI/ML help data management move out of its rut? Or will it stay stuck in the past?

Also, please check out my new data engineering course on Coursera!

https://www.coursera.org/learn/intro-to-data-engineering

If you're working on or trying to break into a career in Data Science or Data Engineering, this one is for you. In this episode, Data Engineering expert and recovering Data Scientist Joe Reis shares some of his best tips and strategies for folks looking to launch or accelerate their data careers. You'll leave with practical and actionable advice that you can use to take your career to the next level.   What You'll Learn: Key differences between Analytics, Data Science, and Data Engineering The top skills and tools to focus on for each of these career paths How rapidly changing technology like AI is impacting the future of data jobs   Register for free to be part of the next live session: https://bit.ly/3XB3A8b   About our guest: Joe Reis is a "recovering data scientist" and the co-founder & CEO of Ternary Data. Joe's newest course Fundamentals of Data Engineering Book Follow Joe on LinkedIn

Follow us on Socials: LinkedIn YouTube Instagram (Mavens of Data) Instagram (Maven Analytics) TikTok Facebook Medium X/Twitter

My guest in this episode is Evan Shellshear, an expert in artificial intelligence and co-author of the eye-opening book "Why Data Science Projects Fail: The Harsh Realities of Implementing AI and Analytics, without the Hype." With nearly two decades of experience in developing AI tools, Evan shares his insights into the real challenges and pitfalls of data science projects, and how organizations can overcome these hurdles. About Evan Shellshear: Evan is a renowned AI expert with a Ph.D. in Game Theory from the University of Bielefeld. He has worked globally with leading companies across various industries, using advanced analytics to drive innovation and efficiency. As an author, his work seeks to demystify the complexities of AI and guide organizations toward successful implementation. Episode summary: In this episode, we explore the critical themes of Evan's book, which aims to shed light on why so many data science projects fall short of their potential. We unpack the exaggerated promises and oversimplifications that often lead to these failures, and discuss practical strategies to avoid them. Discussion highlights: Why Do Data Science Projects Fail? Evan discusses the common pitfalls, including unrealistic expectations and lack of understanding of project complexities.Balancing costs and benefits: How organizations can weigh the costs of failure against the potential benefits of successful data science projects.Avoiding failures: Practical advice on increasing success rates by setting realistic goals and aligning projects with business priorities.Impact of organizational culture: How cultural factors within a company can make or break data science initiatives.Measuring success: Effective metrics and indicators for evaluating project outcomes.You can find out more about Evan's book here, and connect with him via LinkedIn.

This session looks at the ever-increasing demand for data and AI, the current challenges slowing development and how companies can overcome these challenges and shorten time to value using generative AI and open tables like Apache Iceberg. It also looks at how this approach makes it possible to transitioning away from siloed analytical systems to a modern data architecture where multiple teams can create reusable data products across multiple clouds and op-premises environments using generative AI in Data Fabric and share that data across multiple analytical workloads. 

This session explores Gemini's capabilities, architecture, and performance benchmarks. We'll delve into the significance of its extensive context window and address the critical aspects of safety, security, and responsible AI use. Hallucination, a common concern in LLM applications, remains a focal point of ongoing development. This talk will highlight recent advancements aimed at mitigating the risk of hallucination to enhance LLMs utility across various applications.

Snowflake had a big challenge: How do you enable a team of 1,000 sales engineers and field CTOs to successfully deploy over 100 new data products per week and demonstrate every feature and capability in the Snowflake AI Data Cloud tailored to different customer needs?

In this session, Andrew Helgeson, Manager of Technology Platform Alliances at Snowflake, and Guy Adams, CTO at DataOps.live, will explain how Snowflake builds and deploys hundreds of data products using DataOps.live. Join us for a deep dive into Snowflake's innovative approach to automating complex data product deployment — and to learn how Snowflake Solutions Central revolutionizes solution discovery and deployment to drive customer success.

In today’s rapidly evolving technological landscape, the integration of data within organisations is not just a trend but a necessity. This panel discussion will explore how data literacy and the adoption of a data-driven culture can act as catalysts for significant organisational change. We will delve into the roles of Chief Data Officers, Chief Innovation Officers, and Chief AI Officers, examining whether history is repeating itself with new and emerging roles. The discussion will be punctuated by shifts in technology capability and will address whether AI is a true catalyst for organisational change.

In a world where Artificial Intelligence is the new normal, interpersonal skills like critical thinking, persuasion and emotional intelligence will sit alongside the traditional skillset of the data leader as businesses are now scaling and monetising their AI initiatives. 

Organisations must ensure that their leadership is balanced to avoid bias and ensure relevance to the customer, and the leader of the future will be the linchpin to ensure that the opportunity from AI is realised. o how should businesses nurture emerging leaders to ensure that they are developing and retaining top talent in an age of acute skills shortage and salary inflation? 

And how can future leaders equip themselves with the right skills and networks to build sustainable careers right up to the C-suite? 

Join this panel of experts as they discuss the future of leadership in a world where artificial intelligence is central to decision making and why getting it right is a business imperative.

Learn how to automatically scale LLMs in production, optimize your resource usage, and improve performance for your AI-driven applications.

The Dell AI Factory with NVIDIA is a framework to accelerate and de-risk AI adoption and AI powered innovation in the enterprise. Join us to explore how – with this open and extensible end to end solution – we help organisations align the right use case to the most impactful business outcomes.

We will showcase how organisations are leveraging our broad range of capabilities and ecosystem of partnerships, to take advantage of their enterprise data. From the edge, through to the multicloud and private data centre environments, together we’ll explore how to build differentiated and effective business capabilities. 

Everything has changed in the last year with Generative AI entering onto the scene. This means a re-shuffling of priorities and budgets, putting AI-enabled Data & Analytics right back at the top of the agenda. In this session we will discuss: 

• That there is no Generative AI without data – but it has to be the right data 

• The importance of being able to bring together organised and trusted data 

• Why your data integration strategy is the foundation to successfully using AI

2024 is the year of the AI agent. But what are AI agents and how are they different from traditional chatbots we all know? In this talk, we’ll dive into how AI agents work and what makes them different from legacy chatbots. Listeners will leave with a good understanding of AI agent architecture and their newly unlocked capabilities.

2024 is the year of AI agents. Learn how they differ from traditional chatbots and explore their architecture and new capabilities.

In an increasingly multicultural and globalised world, understanding cultural identity has never been more vital. With more than 200,000 years of human evolution, our capacity for culture has thrived as we journeyed from the equator to every part of the globe. We’ve learned, traded and sometimes battled with a growing and diverse global population. Today, our diversity is more significant than ever, especially as we grapple with the lightning-fast evolution of AI technology and look for solutions to global challenges.

Join us in this session as we draw from historical insights that remain highly relevant for our collaboration in a modern context. We’ll explore the cost of conflicts and the disproportionate investment required to better understand our diversity. What does a nuanced, multifactorial, data-driven approach to cultural diversity entail? And what is the price we pay when we oversimplify methods of measuring and categorising our most precious asset – our multifaceted culture.

Simplify your GenAI journey and unlock the hidden power within your databases. Businesses often feel pressured to adopt new, specialized technologies to stay ahead. However, the power to revolutionize your applications with GenAI may already reside within your current database infrastructure. 

We’ll build understanding of vector capabilities, ease of use/ROI, and how PostgreSQL, enhanced with the pgvector extension, can address 80% of common GenAI use cases, providing a streamlined and cost-effective path to AI-driven solutions.

Join us to demystify the hype around dedicated vector databases and explore how built-in vector capabilities existing databases can efficiently support your GenAI workloads without extra overhead.

Recent advances in artificial intelligence have sparked both wonder and anxiety as we contemplate its transformative potential. To nurture a future where AI is leveraged to the benefit of people and society it is vital to understand the importance of responsible AI practices, guided by principles such as fairness, inclusiveness, and transparency.

In this session we will discuss practical tools and resources for implementing these practices, as well as the role of the Responsible AI and Effects in Engineering and Research.