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Ana Sayfaya Dön

An end-to-end Machine Learning system, starting with a dataset and going all the way to monitoring it in production. We start next Monday! We'll build everything using open-source tools, and I'll show you how to deploy your system on different cloud platforms. I've been running this live course for... show more

Santiago

452,380 subscribers

36,622 görüntüleme • 1 yıl önce •via X (Twitter)

Anya Rossi• Live Now

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Santiago profil fotoğrafı

Santiago1 yıl önce

You can join at

Jeffrey 🐬 confident-ai.com profil fotoğrafı

Jeffrey 🐬 confident-ai.com1 yıl önce

Evaluating a model sounds just like what we’re doing :)) Built an open-source LLM evaluating framework here 😇

Mohammed Lubbad, PhD profil fotoğrafı

Mohammed Lubbad, PhD1 yıl önce

That sounds like a comprehensive project. Open-source tools offer great flexibility for such endeavors. What cloud platforms are you considering?

Santiago profil fotoğrafı

Santiago1 yıl önce

You’ll be able to deploy anywhere. We’ll have sessions on AWS and Azure.

Pau Labarta Bajo profil fotoğrafı

Pau Labarta Bajo1 yıl önce

I've heard there is another great live course on real world ML... around a mile away 😉

Santiago profil fotoğrafı

Santiago1 yıl önce

ha ha!

Jeff Amazon profil fotoğrafı

Jeff Amazon1 yıl önce

I love that !!!

CTS Tech profil fotoğrafı

CTS Tech1 yıl önce

He speaks the truth!! 🤑🤑

NebutronX 🌩️ 🇹🇼 ネビュトロン profil fotoğrafı

NebutronX 🌩️ 🇹🇼 ネビュトロン1 yıl önce

I'm working full-time (data engineer) and working on my Master's - but I'm still thinking about signing up!

GillyDaScalper profil fotoğrafı

GillyDaScalper1 yıl önce

Do you have to have experience in coding or can I learn with some idea of coding?

Santiago profil fotoğrafı

Santiago1 yıl önce

If you haven't written code before, you shouldn't join.

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Santiago

75,788 görüntüleme • 1 yıl önce

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Alex Finn

231,802 görüntüleme • 1 yıl önce

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Almost 20 years later, AWS is still the most popular cloud in the world. The reason is simple: it just works! They have four services focused on Generative AI: 1. Amazon Q 2. Amazon Bedrock 3. SageMaker JumpStart 4. PartyRock I've been using AWS for around 15 years (honestly, I don't remember well), and their Developer Center is a gold mine. If you open their Developer Center, you'll find a new learning path, "Generative AI for Developers." I'm linking to it below. This is not just a course. This is a collection of courses, examples, videos, tutorials, and code walkthroughs. They will teach you how to use Generative AI on AWS using the four services above. ↑ That right there is a huge selling point: These classes aren't just theoretical. You'll have a chance to learn using the same professional tools everyone else uses. By the way, there are many more resources in the Developer Center: • Machine Learning • Data Operations • DevOps All of these are free. Click, click, and start learning right away. One more thing before I forget: If you are building anything with AWS, check out Amazon Q, their coding assistant. This is the best coding assistant for AWS-related work, and it's not even close. It's a Visual Studio Code extension. Install it, and it works like any other code assistant, except this one knows a lot about AWS. Thanks to AWS for sponsoring a post about their free courses and learning resources. There's a special place in Developer Heaven for you.

Santiago

22,104 görüntüleme • 1 yıl önce

MCP is an absolute game-changer. (Together with DeepSeek, MCP is probably the hottest thing in AI over the last 6 months.) I use Cursor to write code 90% of the time. I built an MCP server to connect the Cursor agent to GroundX, an open-source RAG system, and I'm not going back. This is officially insane! Here is what I did, step by step: First, a little bit of context. I maintain an end-to-end Machine Learning System with several pipelines to process data, train, evaluate, register, deploy, and monitor a model. I've written a lot of documentation explaining how the system works and how to modify and maintain it. There's also the documentation of the few libraries I used to build the system. I'm a massive fan of GroundX, an open-source enterprise-grade RAG system you can run on your servers or deploy to any cloud provider. I've been working with them for a long time. GroundX offers two services. First, the "ingest" service uses a custom, pretrained vision model to ingest and understand your data. I used this to process all the documentation I have for my code. Markdown files, source code, HTML files, and even PDF documents. Everything I've written related to my project went into GroundX. Their second service is "search," which combines text and vector search with a fine-tuned re-ranker model to retrieve information from the data. I needed to connect Cursor with this service, and that's where MCP came in. I built an MCP server with two tools: 1. The first tool would go to GroundX and retrieve the available topics. Splitting the data into topics (or "buckets," as GroundX calls them) allows me to use the same setup to serve documentation from different topics. 2. The second tool would search GroundX under a specific topic for the context related to the supplied query. The magic happens after connecting the MCP server with Cursor. Now, I can ask any questions related to my project, and Cursor's AI agent retrieves the list of available topics from the RAG system and then searches it to provide relevant context to the model. I went from getting mediocre, sometimes wrong answers to 100% truthful, complete answers. Here is the crazy part:

MCP is an absolute game-changer. (Together with DeepSeek, MCP is probably the hottest thing in AI over the last 6 months.) I use Cursor to write code 90% of the time. I built an MCP server to connect the Cursor agent to GroundX, an open-source RAG system, and I'm not going back. This is officially insane! Here is what I did, step by step: First, a little bit of context. I maintain an end-to-end Machine Learning System with several pipelines to process data, train, evaluate, register, deploy, and monitor a model. I've written a lot of documentation explaining how the system works and how to modify and maintain it. There's also the documentation of the few libraries I used to build the system. I'm a massive fan of GroundX, an open-source enterprise-grade RAG system you can run on your servers or deploy to any cloud provider. I've been working with them for a long time. GroundX offers two services. First, the "ingest" service uses a custom, pretrained vision model to ingest and understand your data. I used this to process all the documentation I have for my code. Markdown files, source code, HTML files, and even PDF documents. Everything I've written related to my project went into GroundX. Their second service is "search," which combines text and vector search with a fine-tuned re-ranker model to retrieve information from the data. I needed to connect Cursor with this service, and that's where MCP came in. I built an MCP server with two tools: 1. The first tool would go to GroundX and retrieve the available topics. Splitting the data into topics (or "buckets," as GroundX calls them) allows me to use the same setup to serve documentation from different topics. 2. The second tool would search GroundX under a specific topic for the context related to the supplied query. The magic happens after connecting the MCP server with Cursor. Now, I can ask any questions related to my project, and Cursor's AI agent retrieves the list of available topics from the RAG system and then searches it to provide relevant context to the model. I went from getting mediocre, sometimes wrong answers to 100% truthful, complete answers. Here is the crazy part:

Santiago

255,391 görüntüleme • 1 yıl önce

#WATCH | "...I am 49 years old and for the next 30 years, I will fight you inside the Parliament and outside; in the gutter and on the streets...We will see the end of you...This is the beginning of your end...We're going to come back and we're going to see the end of you...," says Mahua Moitra after her expulsion as TMC MP.

#WATCH | "...I am 49 years old and for the next 30 years, I will fight you inside the Parliament and outside; in the gutter and on the streets...We will see the end of you...This is the beginning of your end...We're going to come back and we're going to see the end of you...," says Mahua Moitra after her expulsion as TMC MP.

ANI

1,636,417 görüntüleme • 2 yıl önce