Current AI development is fundamentally broken

 

Up to 85% of AI projects fail to make it to production. But inevitably all organizations have to innovate and become AI and data science organizations to enable data-driven decision making, solve problems faster, and stay relevant. 

Interdisciplinary collaboration is the key

The Omdena advantage is to get access to cross-functional teams with a mix of skills and perspectives. Our diverse teams of up to 50 collaborators are able to do data innovation, apply the latest tools, and make end-to-end AI development accessible. 

Our results-driven and innovative development model has resulted in a global track-record of successful use cases.

 

 

Machine Learning Solar

Combining data from multiple sources, types, and disciplines

Having a single source of data is often not enough to solve a real-world problem. Omdena´s interdisciplinary teams are able to identify and combine structured and unstructured data most effectively.

For example, to build a model to map areas for solar adoption in Nigeria (see left image) we used satellite images to identify the population density in a particular area and combined it with Google Search data to understand the business landscape. 

From hierarchical silos to networked teams

Traditional organizational structures are not designed to enable successful AI development.

Our projects are powered through bottom-up collaboration characterized by open dialogue, where diverse ideas are shared freely to find the best solution to a problem. Our test-and-learn mentality incorporates feedback early on and speeds up agile development to deliver results where they are most needed. 

Unlocking the value of Collaborative AI

 

Fast & agile development

Transformative innovation

Ethical solutions 

months only is the average duration of a project where up to 50 collaborators deliver a functional solution.

nationalities in average represented per project in combination with diverse skills and processes for knowledge sharing make new ideas flourish.

%

women in our projects, paired with technical and non-technical team members help to prevent bias and develop inclusive and trusted solutions.

What our partners say about us 

 

Word Resources Institute

We’re really excited about the results of this project. My team currently uses the code and infrastructure on almost a weekly basis. 

John Brandt

Data Scientist, World Resources Institute
AI solutions

Omdena is making a real change in building AI solutions for meaningful problems.

Kirk Borne

Worldwide #1 Top AI Influencer
AI solutions

Omdena is a revolutionary idea and by far the best thing I saw in 2019. The biggest value is the delivery of a working solution in a very precise timeframe (8 weeks).

Semih Boyaci

Co-Founder Impact Hub Istanbul

This is an amazing project. I really love the direction Omdena is setting. A real positive and visible influence in the field.

Brandon Rohrer

Global AI influencer

A great collaborative experience for mission-driven organizations and UN agencies with limited resources and AI expertise. The biggest value we have experienced is the great diversity of backgrounds and ideas.

Rebeca Moreno Jiménez

UNHCR Innovation

Omdena’s community accomplished in 8 weeks what we tried for two years while working with large corporations. I can say Omdena is one of the world’s finest set of Data Scientists working for Social Good.

Rahul Ranjan Srivastava

Tech Lead, Safecity

The collaborative approach of Omdena is taking innovation to a whole new level. We are proud to have worked together on addressing zero hunger in our “crop’s identification challenge”. We believe this is the start of a long journey together.

Saurav Suman

UN World Food Program Nepal
Sintecsys

Outstanding in many ways! The challenge provided Sintecsys’s Team an intense and accurate deep dive into AI with amazing results. For Sintecsys, from now on, Omdena is the official AI partner. 

Osmar Rossetto Bambini Fº

Head of Innovation, Sintecsys

 

Omdena Collaborators come from various backgrounds

Prasanna Muralidharan

Prasanna Muralidharan

Data Scientist at Microsoft

Colton Magnant, Ph.D.

Colton Magnant, Ph.D.

Professor and Lead Data Scientist at UPS

Reem Mahmoud

Reem Mahmoud

Ph.D. Student Machine Learning, AI Education Lead at Zaka

Anel Nurkayeva

Anel Nurkayeva

Operations Analyst at Google

Samson Afolabi

Samson Afolabi

Data Scientist at BASF

Aya Salama

Aya Salama

CS Masters Cambridge, Senior ML Engineer at Aigorithm LLC

Elke Klaassen

Elke Klaassen

Data Scientist at Enexis Groep

Rosana de Oliveira Gomes

Rosana de Oliveira Gomes

Postdoc Physics, Frankfurt Institute for Advanced Studies

How it works

1

        Project submission

Once an organization has submitted a feasible project, we source a high-performing team.

2

            Ideation

The team analyzes the problem from various angles to prioritize for impact and feasibility. 

3

            Data collection and preparation

Different task groups identify innovative ways to collect and augment multiple data sources.

 

4

AI modeling

Through our unique collaborative processes and fast iteration cycles we build the best-fit model.

5

            Scaling

We extend the model to a production-level solution with continuous updates & maintenance.

 

Questions?
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More about the power of Collaborative AI

Results from our projects
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