SaaStr
MAY 19, 2020
One area to watch here is no doubt artificial intelligence, with numerous companies having taken it upon themselves to apply machine learning and deep learning to give themselves an edge in the industry. Challenge #1: How can I acquire data in order to train my health product’s machine learning model?
Tom Tunguz
JUNE 5, 2023
Training, deploying, & optimizing machine learning models has historically required teams of dedicated researchers, production engineers, data collection & labeling teams. Even fully staffed, teams required years to develop models with reasonably accurate performance. Today, it’s a matter of days or weeks.
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Intercom, Inc.
OCTOBER 13, 2020
We are at the start of a revolution in customer communication, powered by machine learning and artificial intelligence. So, modern machine learning opens up vast possibilities – but how do you harness this technology to make an actual customer-facing product? The cupcake approach to building bots.
Intercom, Inc.
SEPTEMBER 9, 2022
GPT-3 can create human-like text on demand, and DALL-E, a machine learning model that generates images from text prompts, has exploded in popularity on social media, answering the world’s most pressing questions such as, “what would Darth Vader look like ice fishing?” Today, we have an interesting topic to discuss.
Advertiser: Data Robot
By leveraging the power of automated machine learning, banks have the potential to make data-driven decisions for products, services, and operations. Read the whitepaper, How Banks Are Winning with AI and Automated Machine Learning, to find out more about how banks are tackling their biggest data science challenges.
Intercom, Inc.
JUNE 22, 2018
It’s easy to believe that machine learning is hard. After all, you’re teaching machines that work in ones and zeros to reach their own conclusions about the world. Indeed, the majority of literature on machine learning is riddled with complex notation, formulae and superfluous language.
Tom Tunguz
JANUARY 17, 2019
AI Agencies use machine learning to disrupt a market dominated by agencies. Often, these startups begin as software companies selling machine learning software into agencies. The startup leverages machine learning under the hood. There is a new twist in SaaS with a parallel dynamic.
Advertiser: Data Robot
By leveraging the power of automated machine learning, banks have the potential to make data-driven decisions for products, services, and operations. Read the white paper, How Banks Are Winning with AI and Automated Machine Learning, to find out more about how banks are tackling their biggest data science challenges.
Advertiser: Data Robot
But in order to reap the rewards of Intelligent Process Automation, organizations must first educate themselves and prepare for the adoption of IPA. In Data Robot's new ebook, Intelligent Process Automation: Boosting Bots with AI and Machine Learning, we cover important issues related to IPA, including: What is RPA?
Advertiser: Data Robot
Today’s economy is under pressure from inflation, rising interest rates, and disruptions in the global supply chain. As a result, many organizations are seeking new ways to overcome challenges — to be agile and rapidly respond to constant change. We do not know what the future holds.
Advertiser: Data Robot
Many organizations are dipping their toes into machine learning and artificial intelligence (AI). Download this comprehensive guide to learn: What is MLOps? How can MLOps tools deliver trusted, scalable, and secure infrastructure for machine learning projects? Why do AI-driven organizations need it?
Advertiser: Data Robot
With the number of available data science roles increasing by a staggering 650% since 2012, organizations are clearly looking for professionals who have the right combination of computer science, modeling, mathematics, and business skills. Fostering collaboration between DevOps and machine learning operations (MLOps) teams.
Advertiser: Data Robot
You know you want to invest in artificial intelligence (AI) and machine learning to take full advantage of the wealth of available data at your fingertips. But rapid change, vendor churn, hype and jargon make it increasingly difficult to choose an AI vendor.
Advertiser: Data Robot
The risk of bias in artificial intelligence (AI) has been the source of much concern and debate. How to choose the appropriate fairness and bias metrics to prioritize for your machine learning models. How to successfully navigate the bias versus accuracy trade-off for final model selection and much more.
Advertiser: Data Robot
The game-changing potential of artificial intelligence (AI) and machine learning is well-documented. Any organization that is considering adopting AI at their organization must first be willing to trust in AI technology.
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