{"id":552,"date":"2022-05-04T13:58:10","date_gmt":"2022-05-04T21:58:10","guid":{"rendered":"https:\/\/live-cometml.pantheonsite.io\/?page_id=552"},"modified":"2025-06-20T15:40:40","modified_gmt":"2025-06-20T15:40:40","slug":"resources","status":"publish","type":"page","link":"https:\/\/www.comet.com\/site\/resources\/","title":{"rendered":"Resources"},"content":{"rendered":"\n
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Comet Resources<\/h1>\n\n\n\n
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\"The<\/figure>\n\n\n\n

Featured Report<\/p>\n\n\n\n

The Ultimate Guide to MLOps<\/a><\/h3>\n\n\n\n
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Read Now<\/a><\/div>\n<\/div>\n<\/div>\n\n\n\n
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\"The<\/figure>\n\n\n\n

Featured Webinar<\/p>\n\n\n\n

The Era of Hyper-Personalization: Building Better Recommender Systems<\/a><\/h3>\n\n\n\n
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Watch Now<\/a><\/div>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n
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How to Scale Today\u2019s Data Science Initiatives<\/a><\/h4>\n\n\n\n

Learn how to define your key business objectives, manage the way you scale your ML initiatives, organize your DS team, and build out your processes and infrastructure<\/p>\n\n\n\n

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Read Now<\/a><\/div>\n<\/div>\n<\/div>\n\n\n\n
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Standardizing the ML Experimentation Process<\/a><\/h4>\n\n\n\n

ML industry experts have identified the process and requirements for developing production-ready machine learning models. You’ll find in this eBook that a standard approach helps to get more models in production.<\/p>\n\n\n\n

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Read Now<\/a><\/div>\n<\/div>\n<\/div>\n\n\n\n
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Building Effective ML Teams<\/a><\/h4>\n\n\n\n

This eBook explains the three critical traits of successful ML teams, and the risks of missing any of these items: visibility, reproducibility, and collaboration.<\/p>\n\n\n\n

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Read Now<\/a><\/div>\n<\/div>\n<\/div>\n\n\n\n
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The Ultimate Guide to MLOps<\/a><\/h4>\n\n\n\n

MLOps provides massive returns when organizations develop a robust and efficient system. Learn how to create a stronger workflow with this ultimate guide.<\/p>\n\n\n\n

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Read Now<\/a><\/div>\n<\/div>\n<\/div>\n\n\n\n
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The Era of Hyper-Personalization: Building Better Recommender Systems<\/a><\/h4>\n\n\n\n

In this roundtable with Ronay Ak from Nvidia, Serdar Kadioglu at Fidelity Investments, and Jacopo Tagliabue, we will be discussing how to build better recommender systems. Our guest speakers shared many lessons learned from building and collaborating with others in building RecSys.<\/p>\n\n\n\n

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Watch Now<\/a><\/div>\n<\/div>\n<\/div>\n\n\n\n
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Industry Panel: Defining the MLOps Stack<\/a><\/h4>\n\n\n\n

What is MLOps? What does it means to you as a data scientist? Watch this on-demand webinar to learn to the answers to these questions and more.<\/p>\n\n\n\n

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Watch Now<\/a><\/div>\n<\/div>\n<\/div>\n\n\n\n
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Customer Roundtable: Developing ML at Enterprise Scale<\/a><\/h4>\n\n\n\n

In this session, join the teams at Superb AI, AI Infrastructure Alliance, and Comet as we cover: How a modular approach to the ML tech stack enables teams to choose the best tools for their use case, data, and business<\/p>\n\n\n\n

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Watch Now<\/a><\/div>\n<\/div>\n<\/div>\n\n\n\n
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Accelerating AI Value with Modular MLOps<\/a><\/h4>\n\n\n\n

In this session, join the teams at Superb AI, AI Infrastructure Alliance, and Comet as we cover: How a modular approach to the ML tech stack enables teams to choose the best tools for their use case, data, and business<\/p>\n\n\n\n

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Watch Now<\/a><\/div>\n<\/div>\n<\/div>\n\n\n\n
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ML System Design for Continuous Experimentation<\/a><\/h4>\n\n\n\n

In this webinar, we will examine some na\u00efve ML workflows that don’t take the development-production feedback loop into account and explore why they break down.<\/p>\n\n\n\n

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Watch Now<\/a><\/div>\n<\/div>\n<\/div>\n\n\n\n
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ML in the Field featuring Vignesh Shetty of GE Healthcare<\/a><\/h4>\n\n\n\n

GE Healthcare projects are delivering REAL impactful business contributions, including reducing MRI imaging time by up to 50% while improving image quality, 30-50% reduction in exam time and 70% reduction in no-show rates. Listen to this in-depth interview and learn: -How large of an AI\/ML team is needed for these impactful projects -What level of industry\/domain expertise is needed by AI practitioners<\/p>\n\n\n\n

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Watch Now<\/a><\/div>\n<\/div>\n<\/div>\n\n\n\n
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Running Effective Machine Learning Teams<\/a><\/h4>\n\n\n\n

While academic research has been improving consistently, many organizations are struggling with translating ML into business value. Now is the time to strategize with your team to overcome critical operational hurdles of ML teams.<\/p>\n\n\n\n

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Watch Now<\/a><\/div>\n<\/div>\n<\/div>\n\n\n\n
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Understanding the ML Model Lifecycle<\/a><\/h4>\n\n\n\n

What is the machine learning lifecycle? Watch this webinar to learn: The stages of the ML model lifecycle Why it\u2019s critical that machine learning teams track their models through the entire lifecycle<\/p>\n\n\n\n

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Watch Now<\/a><\/div>\n<\/div>\n<\/div>\n\n\n\n
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Building Your Futureproof Stack for MLOps<\/a><\/h4>\n\n\n\n

In this webinar, join the teams at Pachyderm and Comet as we cover: What MLOps entails, and the components of a robust stack The challenges teams face when scaling their models intro production<\/p>\n\n\n\n

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Watch Now<\/a><\/div>\n<\/div>\n<\/div>\n\n\n\n
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Lessons From the Field in Building Your MLOps Strategy<\/a><\/h4>\n\n\n\n

In our discussions with leading organizations utilizing ML like The RealReal and Uber, we have compiled real-world case studies and organizational best practices for MLOps in the enterprise.<\/p>\n\n\n\n

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Watch Now<\/a><\/div>\n<\/div>\n<\/div>\n\n\n\n
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Visualize your Object Detection Models with Jacques Verr\u00e9<\/a><\/h4>\n\n\n\n

Whether you\u2019re comparing model performance during a daily standup or onboarding a new teammate, you\u2019ll need to log the training runs with an experiment management tool like Comet. In this session, Jacques Verr\u00e9 will walk you through the process of reviewing a YOLOv5 model in Comet.<\/p>\n\n\n\n

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Watch Now<\/a><\/div>\n<\/div>\n<\/div>\n\n\n\n
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2021 ML Practitioner Survey<\/a><\/h4>\n\n\n\n

AI is encountering another hurdle to delivering value, in the form of friction among and between teams. A survey of 508 machine learning practitioners that included data scientists and engineers found challenges related to people, process, and tools. This friction can cause delays in ML development that delay or halt model deployment to production.<\/p>\n\n\n\n

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Read Now<\/a><\/div>\n<\/div>\n<\/div>\n\n\n\n
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Convergence 2022 \u2013 ML Highlights from 2021 and Lessons for 2022<\/a><\/h4>\n\n\n\n

Oren Etzioni, CEO at Allen Institute for Artificial Intelligence, was the keynote speaker at Comet’s Convergence 2022 event, where he summarizes 15 highlights of 2021 in ML and suggests lessons for 2022 and beyond.<\/p>\n\n\n\n

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Watch Now<\/a><\/div>\n<\/div>\n<\/div>\n\n\n\n
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MLOps System Design for Development and Production<\/a><\/h4>\n\n\n\n

Comet CEO Gideon Mendels discusses system design principles for managing development-production feedback loops and shares industry case studies these principles are applied to production ML systems.<\/p>\n\n\n\n

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Watch Now<\/a><\/div>\n<\/div>\n<\/div>\n\n\n\n
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More than a Statistic: Diversity in Data Science and Machine Learning<\/a><\/h4>\n\n\n\n

Diversity has become an HR catchphrase, but what does it really mean to be from an underrepresented group in tech? This webinar explores the ongoing discussions about diversity, equity, and inclusion in ML and data science.<\/p>\n\n\n\n

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Watch Now<\/a><\/div>\n<\/div>\n<\/div>\n\n\n\n
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Overcoming Machine Learning Development Challenges<\/a><\/h4>\n\n\n\n

In this webinar, Gideon Mendels shares the results of Comet\u2019s 2021 ML Practitioner Survey and talks to Ancestry’s Stanley Fujimoto about overcoming ML development challenges.<\/p>\n\n\n\n

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Watch Now<\/a><\/div>\n<\/div>\n<\/div>\n\n\n\n
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Reproducibility in ML Development<\/a><\/h4>\n\n\n\n

Reproducibility can be a barrier to ensuring positive outcomes and scaling great work. Learn about four aspects of reproducibility in ML and a five-point checklist for ensuring ML reproducibility across your organization.<\/p>\n\n\n\n

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Watch Now<\/a><\/div>\n<\/div>\n<\/div>\n\n\n\n
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Standardizing the Experiment #3: Understanding the Data<\/a><\/h4>\n\n\n\n

In this report, we perform exploratory data analysis on the HackerNews dataset. Our profiling script builds visualizations, extracts summary profiles, and logs samples of the data for reference. We investigate the relationship between our initial set of features and our target.<\/p>\n\n\n\n

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Read Now<\/a><\/div>\n<\/div>\n<\/div>\n\n\n\n
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Standardizing the Experiment #2: Baseline Models (Performance Prediction)<\/a><\/h4>\n\n\n\n

As part of the Standardizing the Experiment Series, we present a report on a baseline post Performance Prediction Model for the HackerNews Dataset.<\/p>\n\n\n\n

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Read Now<\/a><\/div>\n<\/div>\n<\/div>\n\n\n\n
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Standardizing the Experiment #2: Baseline Models (Topic Model)<\/a><\/h4>\n\n\n\n

As part of the Standardizing the Experiment Series, we present a report on a baseline Topic Model for the HackerNews Dataset.<\/p>\n\n\n\n

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Read Now<\/a><\/div>\n<\/div>\n<\/div>\n\n\n\n
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Standardizing the Experiment #2: Baseline Models (Sentiment Analysis)<\/a><\/h4>\n\n\n\n

As part of the Standardizing the Experiment Series, we present a report on a baseline Sentiment Analysis Model for the HackerNews Dataset.<\/p>\n\n\n\n

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Learn More<\/a><\/div>\n<\/div>\n<\/div>\n\n\n\n
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Comet Tips and Tricks<\/a><\/h4>\n\n\n\n

Here’s a collection of tips and tricks in the Comet MLOps platform, including adding multiple metrics to a built-in chart panel, filtering experiments, setting experiments as baselines in a new project, and more.<\/p>\n\n\n\n

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Learn More<\/a><\/div>\n<\/div>\n<\/div>\n\n\n\n
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Advanced ML: Multi-file Jobs<\/a><\/h4>\n\n\n\n

As the complexity of your training pipeline grows, you may find it beneficial to start modularizing code. In this report, we outline best practices for passing the Comet Experiment object between different files within a project.<\/p>\n\n\n\n

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Learn More<\/a><\/div>\n<\/div>\n<\/div>\n\n\n\n
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Standardizing the Experiment #1: Defining the Project Scope<\/a><\/h4>\n\n\n\n
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Learn More<\/a><\/div>\n<\/div>\n<\/div>\n\n\n\n
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Advanced ML: Parameter Optimization<\/a><\/h4>\n\n\n\n

A guide to using an Iterative Strategy for Hyperparameter Optimization.<\/p>\n\n\n\n

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Learn More<\/a><\/div>\n<\/div>\n<\/div>\n\n\n\n
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Profiling Datasets<\/a><\/h4>\n\n\n\n

In this report we explore how to log profiles of your Pandas DataFrames, use them to assess the quality of your inputs, and identify the data constraints of your model.<\/p>\n\n\n\n

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Learn More<\/a><\/div>\n<\/div>\n<\/div>\n\n\n\n
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Advanced ML: Parallelism<\/a><\/h4>\n\n\n\n

In this report we explore two of the most common methods for parallelized training in Machine Learning: Data Parallelism and Inter-Model Parallelism.<\/p>\n\n\n\n

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Learn More<\/a><\/div>\n<\/div>\n<\/div>\n\n\n\n
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Logging Curves to Comet<\/a><\/h4>\n\n\n\n

Learn how to visualize your ROC and Precision-Recall Curves using Comet Reports and Panels.<\/p>\n\n\n\n

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Learn More<\/a><\/div>\n<\/div>\n<\/div>\n\n\n\n
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Creating Panels with Vega<\/a><\/h4>\n\n\n\n

Learn how to build Vega visualizations directly in Comet Panels. This report will walk you through working with the Vega Visualization Grammar.<\/p>\n\n\n\n

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Learn More<\/a><\/div>\n<\/div>\n<\/div>\n\n\n\n
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Custom Visualizations to Debug Object Detection Models<\/a><\/h4>\n\n\n\n

See how custom panels can help visualize predictions from Object Detection Models.<\/p>\n\n\n\n

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Learn More<\/a><\/div>\n<\/div>\n<\/div>\n\n\n\n
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Tensorflow Model Analysis<\/a><\/h4>\n\n\n\n

Learn how to log Tensorflow Model Analysis (TFMA) visualizations to Comet.<\/p>\n\n\n\n

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Learn More<\/a><\/div>\n<\/div>\n<\/div>\n\n\n\n
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Visualizations to Interpret Convolutional Neural Networks<\/a><\/h4>\n\n\n\n

Explore a set of techniques that help visualize filters, layers and features in Convolutional Neural Networks.<\/p>\n\n\n\n

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Learn More<\/a><\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n

<\/p>\n","protected":false},"excerpt":{"rendered":"

Comet Resources Featured Report The Ultimate Guide to MLOps Featured Webinar The Era of Hyper-Personalization: Building Better Recommender Systems How to Scale Today\u2019s Data Science Initiatives Learn how to define your key business objectives, manage the way you scale your ML initiatives, organize your DS team, and build out your processes and infrastructure Standardizing the […]<\/p>\n","protected":false},"author":4,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"customer_name":"","customer_description":"","customer_industry":"","customer_technologies":"","customer_logo":"","footnotes":""},"coauthors":[232],"class_list":["post-552","page","type-page","status-publish","hentry"],"yoast_head":"\nResources | Comet<\/title>\n<meta name=\"description\" content=\"Comet gives you and your team crucial resources to become leaders in ML. Stay ahead of the field with our webinars, educational reports, and more.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"http:\/\/www.comet.com\/site\/resources\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Resources\" \/>\n<meta property=\"og:description\" content=\"Comet gives you and your team crucial resources to become leaders in ML. 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