Naas is the first Jupyter-based data science platform that allows users to schedule, run, and expose data projects using notebooks. It aims to make Jupyter notebooks a commodity for building data intelligence, with a focus on open-source and providing templates to kickstart projects.

naas.ai

Naas

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Naas is the first Jupyter based data-science platform that allows you to schedule, run, and expose all the awesome things you can with notebooks.

For the past 5 years, we've been working with Jupyter to integrate our client's data projects into their production environment. But going from notebooks to prod is a tedious process.

So we dreamed of making Jupyter a safe production environment to make data access as simple as writing on a piece of paper.

Then, we realized that Netflix was on the same path! They are indeed betting heavily on notebooks to enable data access from engineers to business executives.
We share their philosophy and created Naas 📒 Notebooks as a service 🚀

We wish to make Jupyter notebooks a commodity for everyone who wants to build data intelligence, with no black box. Everything is open source, and we created a set of awesome notebooks templates to kickstart your projects in minutes.

With Naas you can :
✅ Connect Naas to more than 3000 apps thanks to our integrations with Zapier, IFTT, Integromat.
✅ Create data scripts in minutes in pure Python, or with our low-code drivers
✅ Build repeatable workflows with Naas magic features (Scheduler, Webhook, Assets...)
✅ Run AI-powered data pipelines effortlessly in a state of the art infrastructure (if you choose the SaaS version)

Are you talking about data the WRONG WAY? ❌

Data leaders often struggle to convince business leaders of the importance of data management. 🤔 

To help tell a better data story, use the "3Vs" of Data Management Storytelling: 💡

1️⃣  Vocabulary: Use clear, non-technical language everyone understands.

2️⃣  Voice: Speak in a way that resonates with your audience.

3️⃣  Vision: Paint a picture of the positive future data management enables.

Focus on:

👍Clarity: Avoid technical jargon and complex explanations.

🤝 Alignment: Tailor your message to your audience's needs.

💪Benefits: Highlight the positive impact on the business.

Remember:

🧐Technical expertise won't win support, clear communication will.

😎Storytelling allows you to connect with your audience on a deeper level.

🤩Effective communication leads to greater stakeholder engagement and funding.

#datastorytelling #datamanagement #storytelling 

People don't realise the power of @tagging GPTs: 

If you've ever built a pro-code LLM app you will know that it is made up of chains and agents. 

A chain being a pre-defined sequence of steps that include interactions with LLMs (prompts), retrieval from data (documents) or systems (apps) to complete an action.

An agent is focused on completing a task but instead of following a pre-defined series of steps, it is acting as a reasoning agent that is deciding which steps are best to take depending on the situation at hand.

In short:
Chains: Predetermined string of actions.

Agents: Using the LLM to decide which actions.

OpenAI has made it possible now for any human to pay $20 per month and have access to this process of chaining together multiple GPTs in order to complete workflows. 

The world is your oyster with this functionality, so far we have built solutions for clients ranging from sales planners, RFP response tools and design thinking coaches. 

The true value lies in being able to link together GPTs (chains) but also have a human conductor be able to orchestrate these GPTs in which ever sequence is best for their workflow. 

What use cases can you think of for chaining multiple GPTs together? 

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