Data Radar Maps : How To Build a Robust and Actionable Data Framework From Scratch

Learn how high performance innovation-driven teams, from Mercedes, FedEx, brighter AI to smaller startups in Silicon Valley, Berlin and Cluj build an actionable data framework from scratch, to drive product growth.

See how Data Engineers, Data Analysts and Data Scientists working for Product Teams identify the key aspects of a robust, sustainable and actionable data framework.

- It starts by identifying the areas where teams have strong data foundations, data source potentials and availabilities.
- We cover the missing gaps in data and how to build structures for extracting data from new sources that are relevant to the product.
- Identify areas of improvement and immediate data needs for teams to improve their level of maturity in terms of data analysis and insights utilization.
- Share how a robust data framework looks like, if we built it from scratch. This is executed via the Data Radar Maps, a novel artifact that we implemented with our clients.

Flow of the talk comprises of beginning with data practices today, what data models mean for product teams, what Data Radar Maps are and finally, real-life case studies of how companies use these “data-informed” models to drive product growth.

The audience that would get the most value from this talk -

🧠 Data Scientists, Data Analysts, Data Engineers, Business Analysts

Level of knowledge for the talk → Intermediate

Data Professionals should have an understanding of data analysis, data science essentials and building data architectures and pipelines.

Key takeaways from this talk would be

- What are the key components of a robust data framework in organizations?
- What is a Data Radar Map and why is it relevant for your data teams?
- How to build this Data Radar Map from scratch (sources, taxonomy, frameworks, utilization)?
- What are the list of tools, frameworks and best practices for data framework design?

You can learn more about me and the work here

[www.linkedin.com/in/thejoyofdata](http://www.linkedin.com/in/thejoyofdata)

Would be great if I could have a wireless mic and a HDMI cable that I could plug into my laptop. I bring a remote which I use to control the slide flow and pointing at screen.

Questions are welcome after as well as during the presentation if any aspect of the slides are unclear.

Would also prefer to have some space to walk about because I don't like presenting while being restricted to a small standing space.

AI Model Management Life Circles : ML Ops For Generative AI Models From Research to Deployment

I share a novel framework called MM Life Circles, which allow teams to visualize and manage the entire lifecycle of generative AI models, from research to deployment including best practices for handover between each phase.

So essentially, I start with choosing a generative AI model as an example, then cover the data side of it followed by training frameworks. Finally, I wrap it up in a complete end-end journey that spans the model lifecycle from research to benchmarking to deployment.

The tools I dig into can be varied, but it's usually a mix of PyTorch Lightning, Tensorflow, Tensorboard, mlflow, Kubeflow, Weights & Biases, neptune.ai and a few more newer ones.

We cover all the essential phases of a model lifecycle, namely -

- Phase I : Research & Prototyping
- Phase II : Hyperparameter Tuning & Model Selection
- Phase III : Model Pruning & Optimization
- Phase IV : Production, Testing & Deployment

We aim to fundamentally document and manage every single pathway your model will go through during its life from research all the way to deployment

- Comprises of research platforms such as Python, R studio and associated libraries
- Covers model building and tuning frameworks such as [Pytorch Lightning](https://www.pytorchlightning.ai/index.html), [Weights & Biases](https://wandb.ai/site), [mlflow](https://github.com/mlflow/mlflow) etc.
- Benchmarking & Optimization Tools such as [onnx](https://onnx.ai/), [tensorflow](https://www.tensorflow.org/tensorboard), [tensorRT](https://developer.nvidia.com/tensorrt) optimization
- Testing & Deployment Tools comprising of unit, functional and integration tests along with deployment CI/CD services of Github, Gitlab, [Jenkins](https://www.jenkins.io/) etc.

The structure of the presentation is usually a few slides on the section of the life cycle (training, benchmarking, registration) followed by a tiny demo either on the tool or on a bash interface. So I usually switch between powerpoint and a terminal screen share for this.

Target Audience :
- Data Scientists
- AI Researchers
- MLOps Engineers
- ML Engineers
- Data Engineers

Key Takeaways for Audience :

What does the model lifecycle for generative AI models look like?
What kind of platforms allow us to import boiler plate code and infrastructures for building models (Pytorch Lightning, Tensorboard)?
How do we build or integrate model management platforms (mlflow, WAB etc.)
How do we hand-off models to production / software / backend teams?
How should we deploy models to production / code coverage by data scientists to ensure qualitative model testing?

Ideally the session is a mix of powerpoint / jumping in and out of tools / VS code for demos / terminal runs. The goal is to simulate the workflow and just take a peek into each phase / tool as we go through it.

Data Maturity Models For Product : Boosting product led growth (PLG) strategies with data!

Learn how high performance product led growth (PLG) teams such as Mercedes, FedEx, brighter AI (and many more in Silicon Valley, Berlin and Cluj) use “data-informed” models to leverage the full potential of their organizational data. 🎯

This talk covers how product led growth (PLG) strategies can truly transform with the power of data integration and utilization.

- It starts by outlining the current landscape of how product and data teams collaborate today.
- This is followed by debunking some common misconceptions when it comes to cross-team working with product and data (e.g. data requests don’t have to be one-off transactions)
- Next, we showcase a real life client example on how product and data teams can collaborate effectively to drive product growth and roadmap upcoming features.
- Finally, we share resources that could be helpful for folks to take away with them to get started.

The ideal audience for this talk would be -

🌟 Product Managers, Product Owners

🧠 Data Scientists, Data Analysts, Data Engineers, Business Analysts

💡 Startup Founders / New Brand Creators

Level of knowledge is INTERMEDIATE with some basic pre-requisite knowledge on product feature building, product led growth (PLG) and data analytics.

Key Takeaways from this would be -

1. What are the misunderstandings that exist today in teams that want to bring product and data teams closer together? How can we resolve this inefficiencies?
2. How do the data teams measure what we are building? What does the data side of “measuring product outcomes” look like?
3. How can we integrate live or historical organizational data into the KPI driver trees or PLG frameworks?
4. How can we make the KPI driver trees have actionable insights? Can it provide information that can directly influence decisions related to the product metric being tracked?
5. How can product managers, product owners understand or interpret the insights and analytics (provided by data teams) without needing to be super fluent in technical knowledge?