This guy is a huge fraud. How'd he get hired

Don't believe this app This app fraud in India milian of dollar. Don't invest this company

Froad app yamathitan

can i get the link of dataset that used in this project

Intersting...reminds me of some features of JMP...which are really useful since years. Tanks for incorporating them in Dataiku.

❤

Must have a paid version to deploy an app?

nice high level overview.

your voice is not clear at all. the code was helpful if you would have zoomed it properly atleast

Amazing breadth

There are some people in this country maybe other country's that they don't want the AI touching them in surgery's or the normal health treatments what you say to them.

5841

IECI card

❤

I don't see data quality on our version at my company

Pls share the speaker's name.

How do you think I could use AI in Dataiku?

How to confirm that our dataset will not be used for anty training for LLM model we selected for prompt enginnering?

Barry is great!

是看到caty女士的视频发现这个AI的，似乎也适用于我们这些其他的化学行业需求

This is a great little series. Thank you for the all this information. :)

This is a powerful story of analyzing your customer journey. I love that they have a set of KPIs for a customer “happy path” and then they monitor against that with the process mining techniques described. Simple yet profound

Does it work the same way in Dataiku version 11 ?

Nice overview. Does the user pay for all the underlying compute for building/training/inferencing or is this included in the SaaS subscription?

Armor sekurety

📝 Summary of Key Points: 📌 The speaker discusses the challenges associated with operationalization in MLOps, such as complex toolings, code rewriting, delays in model deployment, and lack of alignment with business needs. 🧐 Dataiku offers a three-pillar approach to MLOps: unification, operationalization, and repeatability. Under the unification pillar, features like the flow, model support, and experiment tracking promote collaboration and transparency. 🚀 The operationalization pillar focuses on readiness for production and includes features like metrics and checks, the deployer, and model evaluation store to ensure robustness and performance. 🌐 The repeatability pillar emphasizes learning from one project and applying it to subsequent ones, with features like the feature store, blueprint, and blueprint designer facilitating knowledge sharing and standardization. 💬 Dataiku supports a multi-platform ecosystem, allowing users to connect and deploy models from other ML platforms. New features include external models for visualization and monitoring, and deploy anywhere for deploying models in other platforms. 📊 Dataiku's unified monitoring provides a centralized control center for managing projects and assets across multiple platforms, with plans to support additional platforms like Databricks and Snowflake in the future. 💡 Additional Insights and Observations: 💬 "Operationalization is a team effort" - highlighting the collaborative nature of MLOps. 📊 No specific data or statistics were mentioned in the video. 🌐 Dataiku's support for a multi-platform ecosystem enhances flexibility and integration capabilities. 📣 Concluding Remarks: Dataiku offers a comprehensive solution to the challenges of operationalizing MLOps at scale. Their three-pillar approach of unification, operationalization, and repeatability addresses collaboration, readiness for production, and knowledge sharing. With support for a multi-platform ecosystem and unified monitoring, Dataiku aims to provide a robust and efficient MLOps platform for organizations. Made with Talkbud.

really appreciate all technical details of the use cases!

use people

Hi, I can't find this solution in my Dataiku solutions.. please guide

I can't find this solution in Dataiku along with customer review analyser.. ??

Thanks for share

Hey man ❤❤❤❤❤❤❤

Hey man good ❤❤❤❤❤❤❤❤❤❤❤❤❤❤❤❤❤❤

Well ❤❤❤❤❤

Very usefully ❤❤❤

Really nice demo, can see an analytics professional being in heaven with these tools at their fingertips.

Can I write back to CDP?

I want to know more about dataiku as SOPs

Thus dataiku is easy ?

Here are the summarized points from the various sections of the transcript: FDA Submissions & Generative AI: The onus of ensuring the quality and accuracy of FDA submissions lies on the submitter's side. While there's no significant change foreseen immediately, the future might hold more efficient ways of filing and submissions with the FDA due to the influence of generative AI. Regional Regulators & Generative AI: Different regional regulators, such as the European Union, have their perspectives on generative AI. The EU, for instance, has the EUA act that is shaping up to accommodate generative AI's role in the industry. Regulatory Attitudes: Different regulators might have varied stances towards generative AI. In Europe, a more cautious approach is anticipated. The application of AI in regulated sectors, like Financial Services and Insurance (FSI), might see different responses. For instance, AI's role in anti-money laundering can accelerate investigations, but the primary takeaway is to consider how these new technologies integrate into existing systems and processes. Proving Business Value: It's essential to identify use cases where generative AI can quickly demonstrate its impact. Pfizer, for instance, is working with Pfizer Digital to determine areas where AI can make a significant difference. There's an emphasis on choosing projects with a balance of effort and impact. Future Plans & Industrialization: The teams are investing in understanding usage patterns of generative AI and working towards their industrialization. They are collaborating closely with customers to determine the most valuable applications of the technology. Concluding Remarks: The session ends with gratitude for contributions, an announcement about the availability of an on-demand version of the recording, and an invitation to future sessions in the series.

This session provided a great overview of how generative AI can be applied to drive business growth

This talk is so underrated

Wonderful ❤❤❤❤❤❤❤