Test Data Rehab

MOSTLY AI is the pioneering leader in the creation of structured synthetic data. It enables anyone to generate high-quality, production-like synthetic data for smarter AI and smarter testing. Synthetic data teams at Fortune 100 companies and others can originate, amend, and share datasets in ways that overcome the ethical challenges of using real, anonymized, or dummy data. AI-generated synthetic data is private, provides a reduction in time-to-data, and puts more machine learning models into production.

42CAP

PUSH Ventures

Earlybird Venture Capital

Citi Ventures

Molten Ventures

MOSTLY AI

🚀 Conditional Data Generation for All 🚀

Richard Pelgrim has just published a must-read tutorial on conditional synthetic data generation on MOSTLY AI's blog. 📚👨‍💻

➡  https://lnkd.in/dN9-ZPyt

Learn to have more control over the statistical distributions of your synthetic data by setting specific conditions in advance. Explore use cases and get hands on with data, code and a Google Colab notebook, to implement each step as you learn! 

Here is what awaits you:

🛠️ What is conditional data generation: Discover the process in 5 simple steps, starting from splitting your data tables to post-processing for complete, partially synthetic datasets.
🛠️ Practical implementation: Work through a use case on the UCI Adult Income dataset to simulate a scenario with no gender income gap, and another with Airbnb data focusing on geolocation.
🛠️ Statistically representative data: The tutorial ensures you generate data that remains statistically representative within the conditions you create.

Do you have questions? Feel free to reach out in the comment section! 

#DataGeneration #SyntheticData #DataScience #Tutorial #Learning #DataSimulation #ConditionalDataGeneration #SyntheticDataGeneration #AI #MachineLearning #ML 

🌟 Our very own Digital Marketing Manager at MOSTLY AI, Todor Lilkov, has kept his fingers on the pulse of the synthetic data market for years. In this blog post, he shares a comprehensive overview of synthetic data companies operating in both structured and unstructured data spaces.

For anyone engaged in AI, data science, or the broader tech industry, understanding the landscape of synthetic data is crucial. Read the blog post to find out:

⭐ Who the key players are,
⭐ Which companies have received funding and the amounts,
⭐ The number of acquisitions that have occurred so far,
⭐ What factors are driving the technology’s explosive growth?

➡ https://bit.ly/48uToRE

Are you aware of a synthetic data company not included in our list? We are happy to update it; let us know if we missed something in the comment section!

#AI #SyntheticDataCompanies #GenerativeAI #SyntheticData #DataScience #FutureOfAI #MOSTLYAI #GoSynthetic #DataPrivacy #PrivacyEnhancingTechnology #DataGovernance #DataProtection

💳 Mastercard's Chief Privacy and Data Responsibility Officer, Caroline Louveaux, is the latest podcast guest at MOSTLY AI. Listen to the episode and learn what you need to start doing now to succeed in today’s AI and data ecosystem.

There is lots of actionable advice in Caroline’s conversation with Alexandra Ebert that you don’t want to miss:

Why Mastercard created the new role of the Chief Data Responsibility Officer,
How to create a privacy & data governance program that your business will love,
How to ace regulatory compliance despite the EU’s “tsunami of new regulation”,
How can good AI and data governance enable AI instead of killing it,
And why do PETs have so much potential to facilitate your AI success?

🎧 Search for the latest episode of the Data Democratization Podcast on your favorite platform for up-to-date know-how from Mastercard's privacy pro.

💡 Did you know that synthetic data generation offers a great way to rebalance your data for ML classification problems?

This tutorial by Richard Pelgrim covers various rebalancing methods and compares results, including random oversampling, SMOTE, and synthetic data generation. Check out the blog to find out which rebalancing method came out as the clear winner!

🔗 Read the blog post here: https://lnkd.in/dfTybgXa

#MachineLearning #DataScience #ImbalancedData #Rebalancing #DataAnalytics #SMOTE #ML

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