Here at <\/strong>comet.ml<\/strong><\/a>, we strive to make make this process of communicating both results and the steps leading up to those results easier <\/strong><\/p>\n\n\n\n Throughout every project, you\u2019ll 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.<\/p>\n\n\n\n Here are three common audiences:<\/p>\n\n\n\n Once you understand your audience, you can begin tailoring an effective and targeted presentation.<\/p>\n\n\n\n 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\u200a\u2014\u200amarginal 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.<\/p>\n\n\n\n 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.<\/p>\n\n\n\n Hungry for more? We also recommend reading these great posts that include tips on communication: (1) Aspiring Data Scientists? Master these fundamentals <\/a>from Peter Gleeson<\/a> and (2) The Data Science Process: What a data scientist actually does day-to-day<\/a> from Raj Bandyopadhyay<\/a><\/p>\n\n\n\n While there are a number of statistical and technical errors<\/a> you can make during your analysis, we\u2019ll focus on some common communication errors you might run into:<\/p>\n\n\n\n At comet.ml<\/a>, we help data scientists and machine learning engineers to automatically track their datasets, code, experiments and results creating efficiency, visibility and reproducibility.<\/p>\n\n\n\n With comet.ml, you can visualize and track all your model results\u200a\u2014\u200athis is especially helpful for long running experiments, since you can track the results live. <\/em>comet.ml also allows you to:<\/p>\n\n\n\n Just like you need to iterate with your models to improve their performance, you shouldn\u2019t expect to have a perfect presentations skills from the jump. Improving your communication skills requires flexibility for your audiences and practice over time!<\/p>\n\n\n\n So your model is finally done running, you\u2019ve tweaked and optimized all of the hyperparameters you could to obtain the best results, and you\u2019re 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 […]<\/p>\n","protected":false},"author":1,"featured_media":1656,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"customer_name":"","customer_description":"","customer_industry":"","customer_technologies":"","customer_logo":"","footnotes":""},"categories":[6],"tags":[],"coauthors":[107],"class_list":["post-1653","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-machine-learning"],"yoast_head":"\n![\"\"](\"https:\/\/www.comet.com\/site\/wp-content\/uploads\/2022\/06\/1GAqkcH9Qd1kV6ljj7s4Rpg.png\")
In this post, we\u2019ll explore:<\/strong><\/h3>\n\n\n\n
\n
\n\n\n\nWho is your audience?<\/h3>\n\n\n\n
\n
Making a targeted presentation<\/h3>\n\n\n\n
Here\u2019s a useful starting<\/em> framework you can use:<\/strong><\/h4>\n\n\n\n
\n
Common errors to watch out for:<\/h3>\n\n\n\n
\n
\n\n\n\nHow comet.ml helps communicate results<\/h3>\n\n\n\n
![\"\"\/](\"https:\/\/www.comet.com\/site\/wp-content\/uploads\/2022\/06\/1dmiXe3P82ajJuZShKEFXwA.png\")
\n
Want to see an example? Follow along as our team competes in <\/strong>Kaggle\u2019s Home Credit Default Risk competition<\/strong><\/a> in our <\/strong>public project<\/strong><\/a>!<\/strong><\/h4>\n\n\n\n
\n\n\n\nHave your own pro-tips for communicating results that we should feature here? Let us know on twitter @Cometml<\/a> <\/h4>\n","protected":false},"excerpt":{"rendered":"