Tim R. Schleicher
Founder
+49 174 932 1930
LinkedIn
tim.schleicher@lead.berlin
Alessandro Schneider
Head of Data Science
alessandro.schneider@lead.berlin
The current revolution driven by intelligent machines, game-changing platforms and the capabilities of the crowd hits organizations with full force. By implementing Machine Learning jointly with all relevant stakeholders, you will reap the benefits of the new playing field and become a leader in the age of data.
A crisis itself enables an entirely new space of opportunity. The urgency to act creates a willingness to listen, reduces inertia, and fosters an openness among employees. Most importantly, it creates a momentum to get things done!
Artificial Intelligence and Machine Learning have long been met with resistance. If done right, not any longer. ML projects can help you drive sales and shape impact. You can channel the momentum by letting your employees establish ML projects and have them create fast and tangible value! And foster Data Literacy on the way...
For Data Scientists and Machine Learning Engineers, working remotely is the (old) normal. In fact, this is what they are used to. ML projects can easily be implemented remotely. It’s easy: ML projects work also in times of COVID-19!
On top of that, you can grow your virtual collaboration capacities by working closely with those who truly know how to do it.
Machine Learning doesn’t have to be prohibitively expensive. You can wait to scale until the value of a prototype is validated! A prototyping mindset is at the heart of every ML project.
Maybe the biggest reason for starting ML projects right now is that you have to do it anyway. To remain successful, sooner or later ML has to become a fundamental pillar of every organization out there. The earlier you start, the better your organization will be off–that is what’s so nice about data.
The timing might be better than you think.
In Machine Learning, Support Vectors help us make sense of data. They are commonly used in classification problems by splitting a dataset into parts, e.g. cats and dogs. With a so-called Support Vector Machine, we find the best separating hyperplane between classes.
Although they were invented by Vapnik and Chervonenkis already in 1963, they only gained momentum this millenium. Now it is time for your organization to gain traction in the field of Machine Learning? Let us be your Support Vectors, enabling your organization to leverage data.
We draw on some of the brightest minds in the fields of data science and organizational development to co-creatively develop Machine Learning prototypes with you. Just reach out!
Tim R. Schleicher
Founder
+49 174 932 1930
LinkedIn
tim.schleicher@lead.berlin
Alessandro Schneider
Head of Data Science
alessandro.schneider@lead.berlin