comet.ml announces $4.5 million investment to double-down on Comet Enterprise and Meta Learning Capabilities
April 22, 2020
Current customers include Ancestry, General Electric, The National Institute of Health and Fortune 100 Companies…
So your model is finally done running, you’ve tweaked and optimized all of the hyperparameters you could to obtain the best results, and you’re ready to present your findings. Now what?
One of the most important skills for data scientists to have is being able to clearly communicate results so different stakeholders can understand. Since data projects are collaborative across functions and data science results are often incorporated into a larger final project, the true impact of a data scientists’ work depends on how well others can understand their insights to take further action.
Here at comet.ml, we strive to make make this process of communicating both results and the steps leading up to those results easier
Throughout every project, you’ll encounter people with varying levels of technical expertise, buy-in, and business goals. When you present to these different stakeholders, make sure you keep an eye on how your work ties into their role and decisions.
Here are three common audiences:
Once you understand your audience, you can begin tailoring an effective and targeted presentation.
With non-technical stakeholders, you should avoid highly technical terms (e.g. your hyperparameters for your TensorFlow model) and instead, try to frame the machine learning problem into the same terms in which business decisions are made — marginal cost and benefit. However, with your engineering or devOps team, they will need to know details such as how long the model takes to train and GPU/CPU metrics during training.
The most important thing to recognize is that this should not be the only time these results are communicated. Frequent communication and feedback will help alleviate pressure on the final presentation, increase buy-in for your work, and help ease business stakeholders into technical details.
Hungry for more? We also recommend reading these great posts that include tips on communication: (1) Aspiring Data Scientists? Master these fundamentals from Peter Gleeson and (2) The Data Science Process: What a data scientist actually does day-to-day from Raj Bandyopadhyay
While there are a number of statistical and technical errors you can make during your analysis, we’ll focus on some common communication errors you might run into:
At comet.ml, we help data scientists and machine learning engineers to automatically track their datasets, code, experiments and results creating efficiency, visibility and reproducibility.
With comet.ml, you can visualize and track all your model results — this is especially helpful for long running experiments, since you can track the results live. comet.ml also allows you to:
Just like you need to iterate with your models to improve their performance, you shouldn’t expect to have a perfect presentations skills from the jump. Improving your communication skills requires flexibility for your audiences and practice over time!