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#GenerativeAI with LLM's— New Hands-on Course by DeepLearning.AI & #AWS. ☁️🤖 Antje Barth, Principal Developer Advocate for #GenAI at AWS, & Tommy Nelson, Curriculum Product Manager at #DeepLearning.AI, chat course highlights, prereqs, & more. 🎙️

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22,172 görüntüleme • 2 yıl önce •via X (Twitter)

Bilim & Teknoloji#GenerativeAI#AWS#GenAI#DeepLearning
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New short course: Serverless Agentic Workflows with Amazon Bedrock. Learn to build and deploy serverless agents in this course created with Amazon Web Services and taught by Mike G Chambers, a Senior Developer Advocate at AWS specializing in GenAI. (Disclosure: I serve on Amazon's board.) Generative AI applications are becoming more complex, sophisticated, and agentic. Agentic applications have workloads that can be hard to predict in advance -- for example, what tools will it decide to call? -- and a serverless architecture helps you efficiently providing on-demand resources. This course teaches you to build and deploy a serverless agentic application. You’ll learn to create agents with tools, code execution, and guardrails, and build responsible agents for business use cases: - Build a customer service bot for a fictional tea mug business that can answering questions, retrieve information, and process orders. - Connect your customer service agent to a CRM to get customer info and log support tickets in real-time. - Explore how you invoke the agent, and see the trace to review the agent’s thought process and observation loop until it reaches its final output. - Attach a code interpreter to your agent, giving it the ability to perform accurate calculations by writing and running its own Python code. - Implement guardrails to prevent your agent from revealing sensitive information or using inappropriate language. By the end, you will have built a sophisticated AI agent capable of handling real-world customer support scenarios. Please sign up here!

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🎙️ New Deployed episode with Ryan Carson, "Builder in Residence" at Amp. It's a great convo about building code agents and building as a solo founder at the same time, and it's refreshingly honest. Case in point, on evals for coding agents: "We do not have a set of evals that we programmatically run to decide if GPT-5 is better than Sonnet-4 yet... everybody in the trenches building these agentic coding tools understands that it is so dynamic, and so frustrating." Great conversation about building with AI, developer experience, and what happens when everyone can code. Check it out. 👇

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Announcing a new Coursera course: Retrieval Augmented Generation (RAG) You'll learn to build high performance, production-ready RAG systems in this hands-on, in-depth course created by and taught by Zain, experienced AI and ML engineer, researcher, and educator. RAG is a critical component today of many LLM-based applications in customer support, internal company Q&A systems, even many of the leading chatbots that use web search to answer your questions. This course teaches you in-depth how to make RAG work well. LLMs can produce generic or outdated responses, especially when asked specialized questions not covered in its training data. RAG is the most widely used technique for addressing this. It brings in data from new data sources, such as internal documents or recent news, to give the LLM the relevant context to private, recent, or specialized information. This lets it generate more grounded and accurate responses. In this course, you’ll learn to design and implement every part of a RAG system, from retrievers to vector databases to generation to evals. You’ll learn about the fundamental principles behind RAG and how to optimize it at both the component and whole-system levels. As AI evolves, RAG is evolving too. New models can handle longer context windows, reason more effectively, and can be parts of complex agentic workflows. One exciting growth area is Agentic RAG, in which an AI agent at runtime (rather than it being hardcoded at development time) autonomously decides what data to retrieve, and when/how to go deeper. Even with this evolution, access to high-quality data at runtime is essential, which is why RAG is a key part of so many applications. You'll learn via hands-on experiences to: - Build a RAG system with retrieval and prompt augmentation - Compare retrieval methods like BM25, semantic search, and Reciprocal Rank Fusion - Chunk, index, and retrieve documents using a Weaviate vector database and a news dataset - Develop a chatbot, using open-source LLMs hosted by Together AI, for a fictional store that answers product and FAQ questions - Use evals to drive improving reliability, and incorporate multi-modal data RAG is an important foundational technique. Become good at it through this course! Please sign up here:

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Yo yo yo! My team and I are working on this dope course, “Master Gorgeous UI,” where we will share our secrets to creating stunning visual design. This could be applied to your web, UX, and poster design work. together (dot) art/gorgeous We’ll also be creating a community for students where we’ll help each other. At no additional cost, participants will also have lifetime access to the course, including all future updates. Additionally, students can join monthly workshops and bi-weekly portfolio reviews. So, while you can take the course at your own pace, there will also be avenues for us to get together and chat in real time. The course will cover all the foundations of visual design, and we’ll dive deep into the how, not just the why. We’re all makers, so we prefer doing more than talking. So we’ll also do specific examples, and as a community member, you can suggest stuff to cover. I couldn’t be more excited because, while I have done courses in the past all by myself, this time, I’m doing it with a team. You’ll be able to chat with all of us on Discord and ask us questions. We’re a bunch of geeks and nerds who like to make posters, create weird websites, write silly copy, and doodle comics, so if you want that kind of stuff, you’ll be in good hands. There’s a presale going on right now with huge discounts for early birds, up to 66% off. So, if you want not to spend too much money on a ticket, this is your chance. The first lessons of the course will launch on March 4th Join the presale: together dot art/gorgeous

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Introducing "Building with Llama 4." This short course is created with Meta AI at Meta, and taught by Amit Sangani, Director of Partner Engineering for Meta’s AI team. Meta’s new Llama 4 has added three new models and introduced the Mixture-of-Experts (MoE) architecture to its family of open-weight models, making them more efficient to serve. In this course, you’ll work with two of the three new models introduced in Llama 4. First is Maverick, a 400B parameter model, with 128 experts and 17B active parameters. Second is Scout, a 109B parameter model with 16 experts and 17B active parameters. Maverick and Scout support long context windows of up to a million tokens and 10M tokens, respectively. The latter is enough to support directly inputting even fairly large GitHub repos for analysis! In hands-on lessons, you’ll build apps using Llama 4’s new multimodal capabilities including reasoning across multiple images and image grounding, in which you can identify elements in images. You’ll also use the official Llama API, work with Llama 4’s long-context abilities, and learn about Llama’s newest open-source tools: its prompt optimization tool that automatically improves system prompts and synthetic data kit that generates high-quality datasets for fine-tuning. If you need an open model, Llama is a great option, and the Llama 4 family is an important part of any GenAI developer's toolkit. Through this course, you’ll learn to call Llama 4 via API, use its optimization tools, and build features that span text, images, and large context. Please sign up here:
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Introducing "Building with Llama 4." This short course is created with Meta AI at Meta, and taught by Amit Sangani, Director of Partner Engineering for Meta’s AI team. Meta’s new Llama 4 has added three new models and introduced the Mixture-of-Experts (MoE) architecture to its family of open-weight models, making them more efficient to serve. In this course, you’ll work with two of the three new models introduced in Llama 4. First is Maverick, a 400B parameter model, with 128 experts and 17B active parameters. Second is Scout, a 109B parameter model with 16 experts and 17B active parameters. Maverick and Scout support long context windows of up to a million tokens and 10M tokens, respectively. The latter is enough to support directly inputting even fairly large GitHub repos for analysis! In hands-on lessons, you’ll build apps using Llama 4’s new multimodal capabilities including reasoning across multiple images and image grounding, in which you can identify elements in images. You’ll also use the official Llama API, work with Llama 4’s long-context abilities, and learn about Llama’s newest open-source tools: its prompt optimization tool that automatically improves system prompts and synthetic data kit that generates high-quality datasets for fine-tuning. If you need an open model, Llama is a great option, and the Llama 4 family is an important part of any GenAI developer's toolkit. Through this course, you’ll learn to call Llama 4 via API, use its optimization tools, and build features that span text, images, and large context. Please sign up here:

Andrew Ng

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Build and customize complex AI applications with a flexible framework in this new short course, Building AI Applications with Haystack. Created in collaboration with deepset, makers of Haystack, and taught by Tuana, who is the developer relations lead for Haystack at deepset. Generative AI technology is changing rapidly and it can be challenging to integrate APIs from different LLMs, vector databases, and various tools such as web search. In this course, you will learn how to use the Haystack framework to make your development process more modular, allowing you to manage complexity and focus more on building your application. In detail, you’ll: - Build a RAG pipeline using Haystack’s main building blocks – components, pipelines, and document stores. - Create custom components in your pipeline by building a Hacker News summarizer that extends your app’s ability to access APIs. - Use conditional routing to create a branching pipeline with a fallback to web search mechanism when the LLM does not have the necessary context to respond to the user's query. - Build a self-reflecting agent for named entity recognition that loops using an output validator custom component. - Create a chat agent using OpenAI's function-calling capabilities which allow you to provide Haystack pipelines as tools to the LLM, enhancing that agent's capabilities. By the end of this course, you will learn a high-level orchestration framework that can help make your applications flexible, extendible, and maintainable, even as the technology stack changes, new user needs arise, and you add new features to your application. Please sign up here:
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Build and customize complex AI applications with a flexible framework in this new short course, Building AI Applications with Haystack. Created in collaboration with deepset, makers of Haystack, and taught by Tuana, who is the developer relations lead for Haystack at deepset. Generative AI technology is changing rapidly and it can be challenging to integrate APIs from different LLMs, vector databases, and various tools such as web search. In this course, you will learn how to use the Haystack framework to make your development process more modular, allowing you to manage complexity and focus more on building your application. In detail, you’ll: - Build a RAG pipeline using Haystack’s main building blocks – components, pipelines, and document stores. - Create custom components in your pipeline by building a Hacker News summarizer that extends your app’s ability to access APIs. - Use conditional routing to create a branching pipeline with a fallback to web search mechanism when the LLM does not have the necessary context to respond to the user's query. - Build a self-reflecting agent for named entity recognition that loops using an output validator custom component. - Create a chat agent using OpenAI's function-calling capabilities which allow you to provide Haystack pipelines as tools to the LLM, enhancing that agent's capabilities. By the end of this course, you will learn a high-level orchestration framework that can help make your applications flexible, extendible, and maintainable, even as the technology stack changes, new user needs arise, and you add new features to your application. Please sign up here:

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India's First IIT goes online! IIT Kharagpur ushers in a new era of digital learning with the launch of its first fully online programme: Executive Post Graduate Certificate (EPGC) in Generative AI & Agentic AI. Designed at the forefront of technological transformation, this programme brings the academic depth and research excellence of IIT Kharagpur directly to ambitious professionals worldwide. Programme Highlights: • 100% faculty-led by IIT Kharagpur CSE & AI professors • No outsourced instruction — direct mentorship from institute faculty • Hands-on learning to build and deploy production-grade AI systems • Research-driven curriculum aligned with evolving industry needs • On-campus graduation ceremony with Executive Alumni Status From foundational theory to real-world deployment, the EPGC in Generative AI & Agentic AI is crafted for leaders who aspire to shape the future of intelligent systems. Be part of the legacy. Lead the AI revolution. Apply now: #IITKharagpur #IndiasFirstIIT #IITKGPOnline #EPGC #GenerativeAI #AgenticAI #ArtificialIntelligence #AILeadership #ExecutiveEducation #FutureOfAI #AIInnovation #LifelongLearning #TechLeadership #DigitalTransformation upGrad
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India's First IIT goes online! IIT Kharagpur ushers in a new era of digital learning with the launch of its first fully online programme: Executive Post Graduate Certificate (EPGC) in Generative AI & Agentic AI. Designed at the forefront of technological transformation, this programme brings the academic depth and research excellence of IIT Kharagpur directly to ambitious professionals worldwide. Programme Highlights: • 100% faculty-led by IIT Kharagpur CSE & AI professors • No outsourced instruction — direct mentorship from institute faculty • Hands-on learning to build and deploy production-grade AI systems • Research-driven curriculum aligned with evolving industry needs • On-campus graduation ceremony with Executive Alumni Status From foundational theory to real-world deployment, the EPGC in Generative AI & Agentic AI is crafted for leaders who aspire to shape the future of intelligent systems. Be part of the legacy. Lead the AI revolution. Apply now: #IITKharagpur #IndiasFirstIIT #IITKGPOnline #EPGC #GenerativeAI #AgenticAI #ArtificialIntelligence #AILeadership #ExecutiveEducation #FutureOfAI #AIInnovation #LifelongLearning #TechLeadership #DigitalTransformation upGrad

IIT Kharagpur

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