{"id":2670,"date":"2022-01-13T14:31:36","date_gmt":"2022-01-13T22:31:36","guid":{"rendered":"https:\/\/live-cometml.pantheonsite.io\/blog\/comet-office-hours-seven-simple-steps-to-standardizing-the-ml-experiment-1-12-22\/"},"modified":"2022-01-13T14:31:36","modified_gmt":"2022-01-13T22:31:36","slug":"comet-office-hours-seven-simple-steps-to-standardizing-the-ml-experiment-1-12-22","status":"publish","type":"post","link":"https:\/\/www.comet.com\/site\/blog\/comet-office-hours-seven-simple-steps-to-standardizing-the-ml-experiment-1-12-22\/","title":{"rendered":"7 Simple Steps to Standardizing the ML Experiment (Jan. 12)"},"content":{"rendered":"\n<p>Welcome to another recap of the Comet ML Office Hours, powered by <a href=\"https:\/\/theartistsofdatascience.fireside.fm\/\">The Artists of Data Science<\/a>! This week we&#8217;re covering Session 2 of our new series. This session took place Jan. 12th, 2022.<\/p>\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n<p>As a reminder, we\u2019d love to see any and all of you at these fifty minute sessions\u2014so feel free to <a href=\"https:\/\/comet-ml.zoom.us\/meeting\/register\/tZItf-urrzMoE9N-HWSIK68qfI1eDB_jlUeN\">register for upcoming Office Hours sessions here<\/a>! As always, there&#8217;s a lot more in the full session (which you can <a href=\"https:\/\/youtu.be\/AAScd3pZBs4\">find on our YouTube channel<\/a>), so be sure to check it out, alongside clips from roundtables, webinars, and previous Office Hours.<\/p>\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n<h2 class=\"wp-block-heading\">Defining Scope and Success<\/h2>\n\n\n\n<p>As we embark on this 8-session series on Standardizing the Experiment, one thing to keep in mind is that tons of work goes into an ML project before a model begins: namely, defining scope and success, the focus of this week&#8217;s session.<\/p>\n\n\n\n<p>When defining success, a clear understanding is needed for all stakeholders, with project outcomes and objectives consistent across teams. Find out more from the clip below:<\/p>\n<p><iframe loading=\"lazy\" title=\"Defining Success for the ML Project\" width=\"500\" height=\"281\" src=\"https:\/\/www.youtube.com\/embed\/IhFUFwCBVxg?feature=oembed\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe><\/p>\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n<h2 class=\"wp-block-heading\">Key Habits<\/h2>\n\n\n\n<p>Harpreet highlighted two key habits that are needed for creating ML projects that are consistently reproducible, standardized, and collaborative:<\/p>\n\n\n\n<p>Keeping track of metadata and using a systemized way of logging that data.<\/p>\n<p><iframe loading=\"lazy\" title=\"Two Key Habits for Reproducible and Explainable ML Projects\" width=\"500\" height=\"281\" src=\"https:\/\/www.youtube.com\/embed\/16PVSfxZo6c?feature=oembed\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe><\/p>\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n<h2 class=\"wp-block-heading\">Ask Questions<\/h2>\n\n\n\n<p>Overall the main takeaway was to ask questions. Ask the business&#8217;s objectives, ask for priorities, ask for the current solution, ask everything.<\/p>\n\n\n\n<p>Harpreet went over a number of questions that all data scientists and ML engineers should be asking before tackling a project. Protecting your time, energy, and resources is paramount and can only happen if you gather enough information at the start of the project versus midway through.<\/p>\n<p><iframe loading=\"lazy\" title=\"Picking the Right Problem\" width=\"500\" height=\"281\" src=\"https:\/\/www.youtube.com\/embed\/rpmVv4z-mz8?feature=oembed\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" referrerpolicy=\"strict-origin-when-cross-origin\" allowfullscreen><\/iframe><\/p>\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n<h2 class=\"wp-block-heading\">Resources<\/h2>\n\n\n\n<p>During the session Harpreet highlighted the work of Andrew Ng, particularly his Machine Learning <a href=\"https:\/\/www.coursera.org\/learn\/machine-learning\">Coursera course<\/a>. Harpreet also <a href=\"https:\/\/heartbeat.comet.ml\/how-to-define-scope-and-success-criteria-for-your-machine-learning-projects-5021569a961f\">wrote an article on Heartbeat<\/a> defining scope and success.<\/p>\n\n\n\n<p>As always, there&#8217;s more to be discussed and discovered. Check out Comet&#8217;s contributor-led publication <a href=\"https:\/\/heartbeat.comet.ml\/\">Heartbeat<\/a> as well as our YouTube, Twitter, and LinkedIn for more great information.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><a href=\"https:\/\/heartbeat.comet.ml\/comet-office-hours-bookshelf-a463abdf4a5e\">Comet Office Hours Bookshelf<\/a><\/li>\n<li>Office Hours <a href=\"https:\/\/www.youtube.com\/playlist?list=PLX9GmL8cVn_wkVp7g942xiHrKrT_zVpLU\">YouTube Playlist<\/a><\/li>\n<li>Follow Comet ML on <a href=\"http:\/\/twitter.com\/Cometml\">Twitter<\/a><\/li>\n<li>Connect with Comet on <a href=\"https:\/\/www.linkedin.com\/company\/comet-ml\">LinkedIn<\/a><\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\">Curious to Learn more? Join Us!<\/h2>\n\n\n\n<p>We run these virtual Office Hours every Wednesday at 11am EST (New York, NY). Completely free to attend and participate, and we&#8217;d love to see any and all of you there! We&#8217;ve got a great series planned and welcome questions for Harpreet or any of our guests via email to emilie@comet.ml.<\/p>\n\n\n\n<p>But most importantly:<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><a href=\"http:\/\/bit.ly\/comet-ml-oh\">Register for Comet Office Hours<\/a><\/h3>\n\n\n<hr class=\"wp-block-separator\" \/>\n\n\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Notes from the second session of a brand new Office Hours series: Seven Simple Steps to Standardizing the Experiment discussing how to define scope and success for an ML project.<\/p>\n","protected":false},"author":112,"featured_media":2671,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"customer_name":"","customer_description":"","customer_industry":"","customer_technologies":"","customer_logo":"","footnotes":""},"categories":[13],"tags":[],"coauthors":[131],"class_list":["post-2670","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-office-hours"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v25.9 (Yoast SEO v25.9) - 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