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Future of AI & Data
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Приєднався 19 жов 2023
🎉 Welcome to Future of AI & Data - dedicated to exploring the fascinating realms of Generative AI and Data, where innovation meets sustainability🎉
🚀 Subscribe now and be at the forefront of innovation 🚀
🌟 Dive into a world of cutting-edge technology and transformative ideas as we unravel the possibilities within Generative AI and Data-driven solutions. Whether you're a seasoned professional or an enthusiast eager to explore these fields, our videos offer insightful tutorials, thought-provoking discussions, and real life case studies.
🚀 What Sets Us Apart?
We believe in harnessing the power of technology for a sustainable future. From innovative applications to ethical considerations, we delve deep into how Generative AI and Data intersect to create impactful solutions that drive positive change.
🔔 Stay Updated:
Stay ahead of the curve and join us on this journey of discovery! Don't miss out on the latest updates and breakthroughs-subscribe to our channel.
🚀 Subscribe now and be at the forefront of innovation 🚀
🌟 Dive into a world of cutting-edge technology and transformative ideas as we unravel the possibilities within Generative AI and Data-driven solutions. Whether you're a seasoned professional or an enthusiast eager to explore these fields, our videos offer insightful tutorials, thought-provoking discussions, and real life case studies.
🚀 What Sets Us Apart?
We believe in harnessing the power of technology for a sustainable future. From innovative applications to ethical considerations, we delve deep into how Generative AI and Data intersect to create impactful solutions that drive positive change.
🔔 Stay Updated:
Stay ahead of the curve and join us on this journey of discovery! Don't miss out on the latest updates and breakthroughs-subscribe to our channel.
LLM Course Part 3 - Prompt Engineering 101
Welcome to "Mastering Prompt Engineering: Unlock the Power of LLMs!"-a comprehensive guide designed for application developers making the exciting transition to developing with Large Language Models (LLMs). This video dives deep into the essential techniques of prompt engineering, equipping you with the knowledge to maximize the potential of your AI applications.
This video is follow-up to
- LLM Course Part 1 - Large Language Models Simplified - Basics to Implementation
ua-cam.com/video/-89hdLCq_4o/v-deo.html
- LLM Course Part 2 : When and How to Use LLM
ua-cam.com/video/Y1qQb3f7FR8/v-deo.html
Prompt engineering is a critical skill that significantly influences the performance and accuracy of LLMs, making it a vital area of expertise for modern developers. This video kicks off with an engaging introduction, setting the stage for a journey through the core principles and techniques of prompt engineering. The video begins by guiding you on how to choose the most suitable prompt engineering technique tailored to your specific project needs. Understanding the right approach is key to leveraging the full capabilities of LLMs, and this section provides a solid foundation for making informed decisions.
Next, the video offers a quick reference guide, a handy tool for developers to swiftly identify and apply the most effective prompt strategies. This segment is designed to be a go-to resource, simplifying the process of prompt selection and usage in various scenarios.
The video then delves into the specifics of when to use Retrieval-Augmented Generation (RAG) and when to opt for the ReAct approach. RAG is an advanced technique that combines retrieval of relevant documents or data with generation, enriching the output with contextually accurate information. The video explains how RAG can be particularly beneficial in applications requiring detailed and context-rich responses. On the other hand, ReAct focuses on interactive AI applications, enhancing user engagement through responsive and adaptive interactions. Understanding these techniques and their appropriate contexts is crucial for optimizing the performance of your LLMs.
Preprocessing of prompts is another vital topic covered in the video. Insights are shared on how to effectively preprocess prompts to ensure they are clean, relevant, and structured for the best possible output from LLMs. This section emphasizes the importance of prompt clarity and contextuality, highlighting practical tips and best practices for preprocessing.
The video also addresses the post-processing of outputs, an often-overlooked aspect of prompt engineering. Post-processing techniques help refine and improve the generated responses, ensuring they meet the desired quality and accuracy standards. The video demonstrates how to apply these techniques to enhance the final outputs, making them more useful and reliable for end-users.
Throughout the video, there is an encouragement of experimentation, iteration, and continuous refinement of prompts. It emphasizes that prompt engineering is both an art and a science, requiring creativity and a deep understanding of LLM capabilities. By following the guidance and strategies presented in this video, developers can significantly improve their LLM projects, unlocking new possibilities and achieving superior results.
Whether you are refining a chatbot, enhancing data analysis, or creating dynamic content, this video provides the tools and insights you need to succeed. Transform your approach to LLM development with expert prompt engineering techniques.
#Prompt Engineering #RAG #LLM #training
This video is follow-up to
- LLM Course Part 1 - Large Language Models Simplified - Basics to Implementation
ua-cam.com/video/-89hdLCq_4o/v-deo.html
- LLM Course Part 2 : When and How to Use LLM
ua-cam.com/video/Y1qQb3f7FR8/v-deo.html
Prompt engineering is a critical skill that significantly influences the performance and accuracy of LLMs, making it a vital area of expertise for modern developers. This video kicks off with an engaging introduction, setting the stage for a journey through the core principles and techniques of prompt engineering. The video begins by guiding you on how to choose the most suitable prompt engineering technique tailored to your specific project needs. Understanding the right approach is key to leveraging the full capabilities of LLMs, and this section provides a solid foundation for making informed decisions.
Next, the video offers a quick reference guide, a handy tool for developers to swiftly identify and apply the most effective prompt strategies. This segment is designed to be a go-to resource, simplifying the process of prompt selection and usage in various scenarios.
The video then delves into the specifics of when to use Retrieval-Augmented Generation (RAG) and when to opt for the ReAct approach. RAG is an advanced technique that combines retrieval of relevant documents or data with generation, enriching the output with contextually accurate information. The video explains how RAG can be particularly beneficial in applications requiring detailed and context-rich responses. On the other hand, ReAct focuses on interactive AI applications, enhancing user engagement through responsive and adaptive interactions. Understanding these techniques and their appropriate contexts is crucial for optimizing the performance of your LLMs.
Preprocessing of prompts is another vital topic covered in the video. Insights are shared on how to effectively preprocess prompts to ensure they are clean, relevant, and structured for the best possible output from LLMs. This section emphasizes the importance of prompt clarity and contextuality, highlighting practical tips and best practices for preprocessing.
The video also addresses the post-processing of outputs, an often-overlooked aspect of prompt engineering. Post-processing techniques help refine and improve the generated responses, ensuring they meet the desired quality and accuracy standards. The video demonstrates how to apply these techniques to enhance the final outputs, making them more useful and reliable for end-users.
Throughout the video, there is an encouragement of experimentation, iteration, and continuous refinement of prompts. It emphasizes that prompt engineering is both an art and a science, requiring creativity and a deep understanding of LLM capabilities. By following the guidance and strategies presented in this video, developers can significantly improve their LLM projects, unlocking new possibilities and achieving superior results.
Whether you are refining a chatbot, enhancing data analysis, or creating dynamic content, this video provides the tools and insights you need to succeed. Transform your approach to LLM development with expert prompt engineering techniques.
#Prompt Engineering #RAG #LLM #training
Переглядів: 245
Відео
Transform Your Data Management with Headless Data Architecture and Apache Iceberg
Переглядів 519Місяць тому
In today's rapidly evolving data-driven world, organizations constantly seek more efficient and agile ways to harness their data. Traditional data platforms, despite their utility, often come with inherent limitations that stifle innovation and flexibility. As data volumes explode and the complexity of data use cases skyrockets, the need for a more adaptive and streamlined approach becomes unde...
11 Essential Keys to Data Warehousing & Analytics
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In data warehousing and analytics, the structuring and organization of data are paramount for efficient data retrieval, integrity, and overall performance. Central to this organization are the various types of keys used in data models. Keys are crucial elements in a database, as they define relationships between tables, enforce data integrity, and optimize query performance. They ensure each re...
AI Agents in Action - Learn with Real World Examples of Intelligent AI Agents
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5 Key Benefits of an Integrated Analytics Platform
Переглядів 582 місяці тому
In this video, we explore the transformative capabilities of an integrated analytics platform, which offers a comprehensive suite of tools and services to support the entire analytics process. Key areas covered include governance, security, data collection, storage, ingestion, transformation, advanced analytics, and visualization. By the end of this video, you will understand the five key benef...
How Data Intelligence Platforms Transform Raw Data into Strategic Gold
Переглядів 1062 місяці тому
Discover how a cutting-edge Data Intelligence Platform is revolutionizing the way organizations interact with their data, transforming it into a powerful tool for strategic decision-making and innovation. This video explores how the platform bridges the gap between raw data and actionable intelligence, enabling businesses to thrive in the digital age. Learn how leveraging advanced data analytic...
LLM Course Part 2 : When and How to Use LLMs
Переглядів 942 місяці тому
#GenerativeAI #GartnerAI #ai #llm #aiexperts This video is follow-up to - LLM Course Part 1 - Large Language Models Simplified - Basics to Implementation ua-cam.com/video/-89hdLCq_4o/v-deo.html In the past year, the adoption of Generative AI (GenAI) has surged, making it one of the most widely implemented AI techniques across various business units and organizations, as highlighted by Gartner s...
LLM Optimization Part 5 - 5 Techniques for Superior Output from LLM
Переглядів 1772 місяці тому
Discover how to elevate your generative AI projects with five essential techniques in our latest video! Whether you're an AI enthusiast or a seasoned pro, these strategies will help you achieve remarkable results: Prompt Engineering: Crafting precise prompts to guide AI for optimal outcomes. Model Parameters: Fine-tuning parameters to enhance performance and accuracy. Fine Tuning: Customizing m...
How to implement a Modern Data platform with Data Mesh Architecture Pattern
Переглядів 1072 місяці тому
#datamesh #DataFabric #Datalake #DataVault #dataplatform Implementing Data Mesh Architecture In this video, we delve into the transformative world of Data Mesh Architecture and explore why and how it can revolutionize your data management practices. Our comprehensive guide covers the following key areas: Understand the fundamentals of Data Mesh and why it's becoming a pivotal architecture patte...
LLM Optimization Part 4 - 5 Techniques to reduce cost of LLM implementation
Переглядів 1773 місяці тому
#llm #generativeai #costreduction #improveperformance While the initial hype surrounding LLMs is settling, enterprises are shifting their focus towards practical AI deployment with cost-effectiveness in mind. Today, we'll delve into the world of optimizing enterprise AI budgets, specifically focusing on Large Language Models (LLMs). Generative AI and LLMs are becoming the key differentiators of...
Why NVidia is the largest company in the world?
Переглядів 2654 місяці тому
#nvidia #aws #ai #gpu #tpu #ai #generativeai The ‘big three’ public cloud services - Microsoft Azure, Google Cloud Platform (GCP), and market leader Amazon Web Services (AWS) are facing crisis with the rise of generative AI and the hardware needed to power large language models (LLMs) and other workloads. With its hold on the AI hardware segment, Nvidia is moving quickly to shore up its leverag...
LLM Optimization LLM Part 3 - Improve LLM accuracy with GraphDB
Переглядів 2554 місяці тому
#llm #generativeai #graphdatabase #trustworthy LLM Apps need RAG, RAG need Knowledge Graph The greatest challenge - and opportunity - of LLMs is extending their powerful capabilities to solve problems beyond the data on which they have been trained, and to achieve comparable results with data the LLM has never seen. Retrieval-Augmented Generation (RAG) is a technique to search for information b...
Why Databricks DBRX is the best Open Source LLM?
Переглядів 3204 місяці тому
Why Databricks DBRX is the best Open Source LLM?
Fastest way to become a Data Engineer with Free Courses
Переглядів 3654 місяці тому
Fastest way to become a Data Engineer with Free Courses
Building Performance Assessment Systems for Sustainable Buildings - LEED, BREEAM, CASBEE
Переглядів 884 місяці тому
Building Performance Assessment Systems for Sustainable Buildings - LEED, BREEAM, CASBEE
From Chaos to Clarity - A Day in the Life of a Data Engineer
Переглядів 3204 місяці тому
From Chaos to Clarity - A Day in the Life of a Data Engineer
What is Data Fabric architecture? Implementation best practices & design principles
Переглядів 1,1 тис.4 місяці тому
What is Data Fabric architecture? Implementation best practices & design principles
LLM Optimization LLM Part 2 - Large Language Model to Small Language Model
Переглядів 2455 місяців тому
LLM Optimization LLM Part 2 - Large Language Model to Small Language Model
Build Data Models for Analytics with Data Vault - Agile Data Modeling
Переглядів 2915 місяців тому
Build Data Models for Analytics with Data Vault - Agile Data Modeling
Understanding the Risks: Generative AI & Fraud - Part 1
Переглядів 1775 місяців тому
Understanding the Risks: Generative AI & Fraud - Part 1
Demystifying Feature Engineering - Harnessing the Feature Store Advantage in Machine Learning
Переглядів 1486 місяців тому
Demystifying Feature Engineering - Harnessing the Feature Store Advantage in Machine Learning
Must know Large language model terms
Переглядів 6346 місяців тому
Must know Large language model terms
How should QA professionals leverage Gen AI?
Переглядів 1206 місяців тому
How should QA professionals leverage Gen AI?
How AI & ML can help Retailers in Waste Reduction and reduce carbon footprint?
Переглядів 677 місяців тому
How AI & ML can help Retailers in Waste Reduction and reduce carbon footprint?
How are Smart Cities building Net Zero Buildings for a Sustainable Future?
Переглядів 1057 місяців тому
How are Smart Cities building Net Zero Buildings for a Sustainable Future?
Data Modeling Process - A Practical Guide
Переглядів 1 тис.7 місяців тому
Data Modeling Process - A Practical Guide
Can AI replace programmers? Compare Copolit, CodeWhisperer and Code Llama
Переглядів 3137 місяців тому
Can AI replace programmers? Compare Copolit, CodeWhisperer and Code Llama
How to become a Data Modeler - Build Your Knowledge Base
Переглядів 3028 місяців тому
How to become a Data Modeler - Build Your Knowledge Base
How is AI transforming Healthcare? Insights from Case Studies - Mount Sinai, Sloan Kettering
Переглядів 1188 місяців тому
How is AI transforming Healthcare? Insights from Case Studies - Mount Sinai, Sloan Kettering
LLM Course Part 1 - Large Language Models Simplified - Basics to Implementation
Переглядів 1,7 тис.8 місяців тому
LLM Course Part 1 - Large Language Models Simplified - Basics to Implementation
Hello friends, Can anyone recommend a solid, up-to-date training program(s) that cover different cloud infrastructure services specifically aimed at deploying solutions using LLM models of any kind? open source or close source? (Agnostic arbitrage approach) If you have any suggestions or experiences to share on the subject, I'd love to hear them! Ideal training courses should cover: The main cloud services (IaaS, PaaS, SaaS) Deploying and hosting open source LLMs Data and storage management for LLMs Optimizing performance and scalability Security and governance of LLM solutions.
I think the pre and post processing are key to get better results. What do you guys think 🤔?
Great video! good examples for prompting techniques..useful best practices. Looking forward to more content like this! 👍
its the wild west of ai .. who knows who will win, might be a new start up or nvidia in long term.. whoever is able to make a breakthrough in super efficient chipsets and software implementation and training speeds .. one day , perhape , everyone will be assigned an ai at birth to direct one's life..
nice video
Thanks. Each key type is so clear highly recommend watching
Thank you for this insightful video on headless data architecture and Apache Iceberg. The detailed explanation of how decoupling data storage from processing can enhance flexibility and efficiency is highly informative. it would be great to see a deep dive into real-world case studies showcasing successful implementations of headless data architecture. Excellent content! #DataManagement #ApacheIceberg #BigDataSolutions
thanks. explanation of each key type is so clear and insightful. Highly recommend watching #DataWarehousing #Analytics #BigData
Superb
thanks...good use cases and case studies..
don't agree AI Agents will be collaborators. these will lead to huge job loss. Bill Gates says _"To create a new app or service, you'll just tell your agent what you want." "If your friend just had surgery, your agent will offer to send flowers and be able to order them for you." "agents can capture what makes a tutor effective, they’ll unlock this supplemental instruction for everyone who wants it"_
quite possible..but my belief is that the existing workers will upskill and get to different roles which need more Creativity and Problem Solving
What is the difference between LLM and Small language models ?
Typically 1-2 B parameter model or quantized model. Some more details in this video Large Language Model to Small Language Model - Quantization simplified ua-cam.com/video/yyZV6So1bl4/v-deo.html
how about small language models?
Some details here - Large Language Model to Small Language Model - Quantization simplified ua-cam.com/video/yyZV6So1bl4/v-deo.html
GPT 4 is $30/$60 per 1mn tokens, but cheaper models with similar performance are just 15/30 cents per million tokens. Using the lower priced model reduces the total cost of processing wikipedia to US$ 900 vs. your estimate of US$ 360,000.
Agreed. Small language models are better when it comes to enterprise use cases. some thoughts around reducing LLM costs in this video - ua-cam.com/video/xlzTITjl2G0/v-deo.htmlsi=rzK6C6XvLRRtU2e_
Totally agree to all the points. It will always help organisations to build their businesses well
Curious about the long-term impacts of these strategic shifts on AI's accessibility and innovation.
Thanks @AdvantestInc for viewing and your comment. My view is that Nvidia’s approach will give opportunities to smaller cloud providers to enter the market and innovate. Nvidia will dominate the AI market for the near future and possibly long term if their reference architectures get an adoption like CUDA by the developer community. This might have a significant impact on the cloud provider’s dominance and they will have to come up with better propositions for the enterprise customers for AI. Microsoft is better placed at the moment. Let’s see what AWS and Google come up with.
super informative thanks..now i know why there are only NVidia V100s in Asia with the hyperscalers
Thanks @SustainHub for viewing and your comment. Yes apac region has smaller share of GPUs. India's Yotta did get a lot of GPUs from Nvidia. Similarly is other countries the smaller cloud providers have a good supply.
Good and relevant information on how to acquire skills essential for data engineering!
completely agree...LLM Apps need RAG & RAG need Knowledge Graph 👍
good information..👍Unlike other open MoE models like Mixtral and Grok-1, which use 8 experts and choose 2, DBRX uses 16 experts and selects 4 of them for any given input. This fine-grained approach provides a staggering ~65 times more possible combinations of experts, enhancing the model's overall quality and performance👍
Thanks for viewing and sharing your thoughts
the question is - is it enterprise ready?
super informative 👍 must watch for someone who wants to become a data engineer
thanks...good information about LLM implementation costs
more information around reducing LLM costs here - ua-cam.com/video/xlzTITjl2G0/v-deo.htmlsi=rzK6C6XvLRRtU2e_
I want to help promote your UA-cam channel organically
Wow Very Nice
Great insights on the risks and opportunities of Generative AI in fraud prevention. One advanced tactic is using AI-driven behavioral biometrics, which can uniquely identify individuals based on their interactions with devices, adding an extra layer of security beyond traditional methods. This is a game-changer in detecting sophisticated fraud attempts.
Thanks @leadgenjay for your comment! You're absolutely right, AI-driven behavioral biometrics are a powerful tool in the fight against fraud.
thanks..video is a good introduction to feature engineering and how they can be used to improve operational machine learning.
Generative AI will increase the accuracy and bring better results
Absolutely! Generative AI has the potential to revolutionize the QA professions by enhancing accuracy and delivering superior results. Exciting times ahead!
good information for QA professions 👍must view
Thank you so much for your support! I appreciate you taking the time to watch and comment.
Bread waste can save so much carbon amazing
Transformers important topic for understanding llm
Good information on large language model
Chatgpt plug-ins amazing I had not played around with those..👍
Interesting to know why buildings is so important for decarbonisation..yes all smart buildings need to be netzero
Good to see how content consumption has changed and the power Is shifting away from the consumer to the platform providers..take back control 🙏
Good information on how retail waste management can be reduced..AI is not just being used by social media companies but also for reducing carbon footprint 👍
Yes the focus of AI should be for saving the planet.
Data science is fast growing..good info on how to be part of the growth story
Relevant and useful content!
Nice short course 👍
Very insightful video. Sustainability is not just about preserving nature but safeguarding future generations
Thank you so much for your comment! I'm glad you found the video insightful. It's important to spread awareness about the importance of sustainability and its impact on future generations.
wonder which mode of entertainment would you rate as your favorite? book vs tv vs social media vs fast fashion social media
youtube
Book!
completely agree Netzero buildings are the future of sustainable cities. 38% carbon consumption by buildings...
Absolutely! Net zero buildings have the potential to make a huge impact on creating sustainable and eco-friendly cities. It's exciting to see the progress being made in this area.
Explained in a very simple language. Everyone from different backgrounds can understand the basic concept of Data modeling Process.
I appreciate your kind words! It's my goal to break down complex concepts and make them understandable for everyone. I'm glad you found it helpful!
Good crash course on data modeling process
Thanks for checking out my video on data modeling process. I hope it gave you a good overview and helped you understand the topic better. Let me know if you have any questions!
Thanks for checking out the video! I'm glad it served as a crash course for you. Let me know if there's anything specific you'd like me to cover in the future.
Thanks...this field is evolving at fast pace...good to know which jobs are- high paying but to have a sustained career passion is the key
This new area is really cool and helps solve problems in our everyday lives. AI and Generative AI are changing things a lot and will keep getting better.
Agree!!
Good info 🙏
Thank you for watching and finding the information helpful!
Interesting quiz on transformers
Thanks for watching! I'm glad you found the quiz interesting. Did you learn something new about transformers?
yes its the passion for data that counts..
Absolutely! Passion is what drives us to explore and uncover the hidden insights in data. Keep up the great work!
✋ 'Promo SM'