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INTEL X

IntelX is an end-to-end ML platform concept designed to

take models from dataset setup to deployment and monitoring in one guided workflow. It helps teams ship and improve prediction models faster by reducing tool-switching and making progress visible.

MachineLab is an end-to-end ML platform concept designed to take models from dataset setup to deployment and monitoring in one guided workflow. It helps teams ship and improve prediction models faster by reducing tool-switching and making progress visible.

Space

Machine Learning

Role

Product Designer

Users

Data Scientists

ML Engineers

Data Scientists

ML Engineers

Team

1 Designers

2 Front-end &

1 Back-end developer

1 Project Manager

1 Designers

2 Front-end &

1 Back-end developer

1 Project Manager

Problem

Ascendeum’s ML work was slowed by fragmented tools and handoffs across data prep, training, deployment, and monitoring, creating delays in shipping models that impact ad performance.
Teams spent disproportionate time coordinating workflows instead of improving models that drive client ROI.

Ascendeum’s ML work was slowed by fragmented tools and handoffs across data prep, training, deployment, and monitoring, creating delays in shipping models that impact ad performance.
Teams spent disproportionate time coordinating workflows instead of improving models that drive client ROI.

Business goals

Reduce time lost in fragmented ML workflows by bringing data prep, training, deployment, and monitoring into one structured system.
Improve model shipping speed, governance, and cross-team visibility so Ascendeum can deliver stronger client ROI through faster ML iteration.

Reduce time lost in fragmented ML workflows by bringing data prep, training, deployment, and monitoring into one structured system.
Improve model shipping speed, governance, and cross-team visibility so Ascendeum can deliver stronger client ROI through faster ML iteration.

User goals

Help data scientists and ML engineers move models through each stage with less tool switching, clearer ownership, and fewer repetitive setup tasks.
Make the ML lifecycle easier to follow through a stage-based workflow that supports both beginners and advanced users with the right level of guidance and control.

Help data scientists and ML engineers move models through each stage with less tool switching, clearer ownership, and fewer repetitive setup tasks.
Make the ML lifecycle easier to follow through a stage-based workflow that supports both beginners and advanced users with the right level of guidance and control.

Design Approach

I used a research-led design approach-interviewing Ascendeum data scientists/admins and shadowing real ML workflows—to map a consistent AdTech model lifecycle and identify where time was lost.
Then designed a stage-based “Model Journey” where models move as cards through each step with progressive disclosure, making the flow beginner-friendly while keeping expert controls for tuning, deployment, and monitoring.

I used a research-led design approach-interviewing Ascendeum data scientists/admins and shadowing real ML workflows—to map a consistent AdTech model lifecycle and identify where time was lost.
Then designed a stage-based “Model Journey” where models move as cards through each step with progressive disclosure, making the flow beginner-friendly while keeping expert controls for tuning, deployment, and monitoring.

Impact

Faster iteration loops: reduced coordination overhead so models and updates can move from experimentation to production faster.

Better onboarding: new team members can follow a consistent step-by-step workflow rather than learning many disconnected tools.

Experiment setup time reduced from 45-90 min to 27-45min per run with a unified workflow and reusable configuration

Data movement overhead reduced from 4-6 hrs/week to less than 3-4hrs/week by reducing rework and repeated transfers

Faster iteration loops: reduced coordination overhead so models and updates can move from experimentation to production faster.

Better onboarding: new team members can follow a consistent step-by-step workflow rather than learning many disconnected tools.

Experiment setup time reduced from 45-90 min to 27-45min per run with a unified workflow and reusable configuration

Data movement overhead reduced from 4-6 hrs/week to less than 3-4hrs/week by reducing rework and repeated transfers

PM Handoff

Success Criteria

Deployment Time :    Reduce model deployment from months to days or hours.  
User Adoption  :  Beginners can build and deploy models without support.  
Cross-team Efficiency  :  Reduce handoff time between data scientists and ML engineers by 50% through shared workflows.

Secondary Research

Conducted lightweight AI-assisted secondary research to quickly understand the machine learning space, its lifecycle, and the key tools and roles involved.
Used this to build foundational context on how machine learning supports Adtech use cases such as prediction, optimization, and model deployment before moving into user interviews.

Primary Research by Interview

Critical Pain Points

Efficiency bottlenecks and lack of advanced workflow support.
Visibility and governance challenges across team workflows.
Approx 4-6 hrs/week wasted on data movement.
Approx 45–90 minutes spent just recreating setup (datasets, splits, feature steps, metrics) before training.

Key Research Insights