We\u2019ve roughly tripled in team size, opened a new office in Tel Aviv, and have welcomed many amazingly talented folks to our team, across engineering, product, marketing, sales, growth, and more!
But we\u2019re only getting started!<\/p>\n\n\n\n
If finding a new, exciting role is on your radar for 2022, then look no further. We\u2019re currently hiring across departments and teams<\/a>\u2014and we\u2019re always open to working with talented, motivated people from all around the world.<\/p>\n\n\n Above, I alluded to Comet\u2019s success in the ML market as a primary driver of the parallel growth in our team size and diversity.<\/p>\n\n\n\n We were thrilled to raise both Series A<\/a> and Series B<\/a> rounds of funding\u2014totaling more than $60 million USD\u2014only 6 months apart and on the back of 5x growth in annual recurring revenue<\/a>.\u00a0<\/p>\n\n\n\n This rapid growth helped us land on several \u201cML Startups to Watch\u201d lists\u2014like this one from CRN<\/a> and this one from Analytics Insight<\/a>. But accolades and specific numbers aside, our team has been energized by this traction and the concurrent reception from the broader ML community.\u00a0<\/p>\n\n\n\n It\u2019s truly humbling to witness and participate in this rapid growth, and it only steels our resolve to become the de facto AI development platform for enterprises and practitioners alike.<\/p>\n\n\n Underscoring the amazing achievements outlined above has been the rapid development and improvement of the Comet platform. We\u2019ve been blown away by all the creative and groundbreaking ways our community has been using our platform\u2014from our experiment tracking and management tools to our newly-available tools for monitoring model performance in production.\u00a0<\/p>\n\n\n\n Here\u2019s a quick rundown of what we built in 2021:<\/p>\n\n\n\n <\/p>\n\n\n\n <\/p>\n\n\n\n As we continued to improve and expand upon our powerful tools for experiment tracking, logging, and visualization, we came to realize that in order to build a more complete ML experimentation platform, we\u2019d need to address the elephant in the enterprise ML room\u2014the problem of monitoring\u2014and fixing\u2014models that start to drift or perform poorly once in production.<\/p>\n\n\n\n This realization led us to develop and launch a new component of our tool stack\u2014Model Production Monitoring (MPM)<\/a>. This new component of the Comet platform allows practitioners and teams to better understand the relationships between model performance across the ML lifecycle\u2014effectively closing the gap between model training and performance in production<\/p>\n\n\n\n To learn more about the what and why of MPM, check out the following resources:<\/p>\n\n\n\n <\/p>\n\n\n\n <\/p>\n\n\n\n While managing and tracking your team\u2019s ML experiments, understanding data sources and their lineage\u2014both in terms of inputs and outputs\u2014is crucial for projects that require multi-stage pipelines, or use cases where the output data of one model training run serves as the input data for a new training run.<\/p>\n\n\n\n We built Comet Artifacts<\/a> to solve these data lineage and usability challenges. Put concisely, Artifacts provides a convenient way to log, version, and browse data from all parts of an experimentation pipeline.<\/p>\n\n\n\n To dive deeper into Artifacts, take a closer look at the following resources<\/p>\n\n\n\n In addition to the two brand new product features, our team also worked on major iterations of some of Comet\u2019s existing features\u2014specifically, a revamp of the UI and the introduction of completely custom Python visualizations rendered in the browser.<\/p>\n\n\n\n The UI revamp<\/a> was designed to improve and streamline both the \u201cExperiments\u201d and \u201cTables\u201d pages\u2014enabling better connections between model training experiments and their resulting Panels (i.e. visualizations). Here are a couple highlights:<\/p>\n\n\n\n <\/p>\n\n\n\n Alongside the new UI, we also launched a Beta version of \u201cPython Panels\u201d<\/a>, which allow you to write custom visualizations the same way that you write all of your other scripts\u2014using the Python modules that you know and love.\u00a0<\/p>\n\n\n\n <\/p>\n\n\n\n Previously, custom code Panels were only possible in JavaScript, a less popular language among ML practitioners. So we\u2019re excited about the possibilities of this new way to visualize experiment results, and we hope you\u2019ll help us further test and explore this feature in 2022.<\/p>\n\n\n\n To truly become the de facto AI development platform, we also realize Comet needs to play nicely with the tool stacks practitioners and enterprise teams already work with.\u00a0<\/p>\n\n\n\n That\u2019s why, in addition to our core ML library integrations (i.e. PyTorch, TensorFlow, Scikit Learn), we invested heavily in integrations and partnerships in 2021 that extend Comet\u2019s functionality and versatility.<\/p>\n\n\n\n Here\u2019s a quick rundown of our integration and partnership work in 2021, with links to tutorials, notebooks, and more information:<\/p>\n\n\n\n Auto log TensorboardX events to Comet<\/p>\n\n\n\n GUI model demos directly in the Comet UI<\/p>\n\n\n\n Full-stack ML observability<\/p>\n\n\n\n CI\/CD for ML workflows<\/p>\n\n\n\n <\/p>\n\n\n\n <\/p>\n\n\n\n Leverage accelerated, PyTorch-based deep learning training pipelines<\/p>\n\n\n\n Production-grade, state-of-the-art NLP<\/p>\n\n\n\n Automatic EDA tracking<\/p>\n\n\n\n Exploring and improving datasets through robust data management<\/p>\n\n\n\n Last but certainly not least, 2021 was a banner year for the Comet Community\u2014tons of new educational content, a brand new contributor program, sponsorship of two industry-leading outlets, a number of really exciting events\u2026<\/p>\n\n\n\n Oh, and, as always, Comet remains completely free<\/a> for community practitioners and academics!\u00a0<\/p>\n\n\n\n In October, we officially acquired and (re)launched Heartbeat<\/a>, a community-driven publication that, since 2017, has been at the forefront of technical content serving data science, ML, and DL practitioners.<\/p>\n\n\n\n Heartbeat\u2019s stature among industry publications, combined with its dedication to supporting practitioners and writers from around the world, made it a natural fit for the Comet community.<\/p>\n\n\n\n Since that launch in early October, we\u2019ve already been fortunate enough to publish more than 50 technical articles from more than 2 dozen technical contributors around the world.<\/p>\n\n\n\n We\u2019re committed to the legacy and future of the Heartbeat publication as a premiere source of technical content that speaks to the needs, interests, and aspirations of the ML practitioner community\u2014that\u2019s why we\u2019re paying contributors for their articles, as well as providing full editorial support along the way.\u00a0<\/p>\n\n\n\n Interested in contributing? We\u2019d love to hear from you! Learn more in our official Call for Contributors<\/a>.<\/p>\n\n\n\n In addition to Heartbeat, we also became the lead sponsors of Deep Learning Weekly, one of the premiere weekly newsletters in the industry, covering the best and most essential industry news, research, code examples, learning resources, and more across the ML and DL world.<\/p>\n\n\n\n With more than 225 published issues<\/a> and recently eclipsing 16,000 subscribers, we\u2019re excited to carry on the excellent legacy of DLW\u2014and help make it even better!<\/p>\n\n\n\n Click here to subscribe for free and read the entire archive<\/a>!<\/p>\n\n\n\n With such amazing customers, community members, and networks, we take seriously our responsibility to share that wealth of knowledge with the broader ML community.\u00a0<\/p>\n\n\n\n One way we did that in 2021 was through a number of really interesting and fun events (all virtual, unfortunately) that explored some of the most interesting and relevant industry topics, ranging from deeply technical looks at ML system design to strategies and tools for collaboration within ML workflows.<\/p>\n\n\n\n Here\u2019s a rundown of some of our favorite events from 2021:<\/p>\n\n\n\n This virtual roundtable from early December featured a wide-ranging discussion with ML leaders from Uber AI, The RealReal, and WorkFusion, exploring the past year of their ML work in the enterprise. We covered:<\/p>\n\n\n\n This was an incredibly dynamic session with tons of valuable insights from some of the biggest and best ML teams in the world. ICYMI, you can access the full session on-demand here<\/a>, as well as review some of the best highlights on this YouTube Playlist<\/a>.<\/p>\n\n\n\n In late October, Comet CEO and co-founder Gideon Mendels joined the well-regarded Stanford MLSys Seminar series to discuss how some of the industry leaders are approaching MLOps system design\u2014all the way from creating an initial baseline to creating systems to monitor and iterate on models once they\u2019re in production.\u00a0<\/p>\n\n\n\n Essentially, a deeply technical and thorough examination of the development-to-production feedback loop.<\/p>\n\n\n\n You can check out the full session here<\/a>.<\/p>\n\n\n\n Comet Data Scientist Harpreet Sahota\u2014new to the Comet team in 2021\u2014jumped right into the deep end with his excellent presentation centered on some of the lessons our team has learned about building and executing an MLOps strategy. This presentation focuses on when, why, and how growing ML teams should think about hiring, building, and buying their MLOps approach.<\/p>\n\n\n\n While Harpreet delivered this talk numerous times, here\u2019s the most recent version<\/a> from early November, delivered in partnership with Neuton AI<\/a>.\u00a0<\/p>\n\n\n\n <\/p>\n\n\n\n
\n\n\nCompany Growth<\/h2>\n\n\n\n
\n\n\nProduct Developments<\/h2>\n\n\n\n
MPM: Model Production Monitoring<\/h3>\n\n\n\n
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Comet Artifacts<\/h3>\n\n\n\n
![\"\"](\"https:\/\/wordpress.comet.ml\/app\/uploads\/2021\/12\/Artifacts-screenshot-1.png\")
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Revamped UI and Python Panels<\/h3>\n\n\n\n
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<\/figure>\n\n\n\n![\"\"](\"https:\/\/wordpress.comet.ml\/app\/uploads\/2021\/12\/Python-Panels-asset.png\")
<\/figure>\n\n\n\nIntegrations and Partnerships<\/h3>\n\n\n\n
TensorboardX\u00a0<\/strong><\/h4>\n\n\n\n
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Gradio<\/strong><\/h4>\n\n\n\n
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New Relic<\/strong><\/h4>\n\n\n\n
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GitLab<\/strong><\/h4>\n\n\n\n
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Catalyst<\/strong><\/h4>\n\n\n\n
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Spark NLP<\/strong><\/h4>\n\n\n\n
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Sweetviz\u00a0<\/strong><\/h4>\n\n\n\n
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Aquarium Learn<\/strong><\/h4>\n\n\n\n
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\n\n\nComet Community<\/h2>\n\n\n\n
Heartbeat: Community Publication and Contributor Program<\/h3>\n\n\n\n
![\"\"](\"https:\/\/wordpress.comet.ml\/app\/uploads\/2021\/12\/1qWSDYJFfTfMOgkvADei-RQ.png\")
<\/figure>\n\n\n\nDeep Learning Weekly: Industry-Leading Newsletter<\/h3>\n\n\n\n
![\"\"](\"https:\/\/wordpress.comet.ml\/app\/uploads\/2021\/12\/15oEAWXxcjLsSR8cLECqncg.jpg\")
<\/figure>\n\n\n\nComet Industry Events<\/h3>\n\n\n\n
Comet Roundtable: ML Development at Scale in the Enterprise<\/strong><\/h4>\n\n\n\n
![\"\"](\"https:\/\/wordpress.comet.ml\/app\/uploads\/2021\/12\/ML_Dev_ad_V2-1024x535.jpg\")
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Stanford MLSys Seminar: MLOps System Design<\/strong><\/h4>\n\n\n\n
![\"\"](\"https:\/\/wordpress.comet.ml\/app\/uploads\/2021\/12\/ML-Ops-System-Design-feat-Comet-ML-CEO-Gideon-Mendels-Stanford-MLSys-44-YouTube-1024x526.png\")
<\/figure>\n\n\n\nLessons from the Field in Building Your MLOps Strategy<\/strong><\/h4>\n\n\n\n
![\"\"](\"https:\/\/wordpress.comet.ml\/app\/uploads\/2021\/12\/ODSC_-Building-an-MLOps-Platform-for-the-Future.jpg\")
<\/figure>\n\n\n\nIndustry Q&A: Building Creative User Interfaces and Experiences with ML<\/strong><\/h4>\n\n\n\n