article thumbnail

Hidden Technical Debt in AI

Tom Tunguz

That little black box in the middle is machine learning code. I remember reading Google’s 2015 Hidden Technical Debt in ML paper & thinking how little of a machine learning application was actual machine learning. With the dawn of AI, it seemed large language models would subsume these boxes.

article thumbnail

The $939B Question: Is AI Eating SaaS or Feeding It?

SaaStr

They launched their Artificial Intelligence Platform (AIP) in mid-2023 and bet the entire company on AI transformation. CEO Alex Karp’s insight: “Our early insights surrounding the commoditization of large language models have evolved from theory to fact.” Palantir was ready.

AI Search 289
Insiders

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

The VP of AI Trap: Why Hiring One Exec Won’t Transform Your Company. In Fact, It May Make It Worse.

SaaStr

” I saw a term sheet the other day where a leading VC firm reserved $1m of the round … for hiring a “VP of AI” Leadership teams scrambling to post job descriptions for “Head of Artificial Intelligence.” ” Recruiters cold-calling anyone with “machine learning” on their LinkedIn.

article thumbnail

5 Interesting Learnings from Palantir at $2.7 Billion in ARR

SaaStr

Artificial Intelligence Platform (AIP) is a Year Old But Fueling $159m in Q2 Bookings Alone To some Cloud and SaaS leaders, AI is a table-stakes addition. Growth Has Re-Accelerated Fueled by commercial and government contracts, and by AI-related demand in both, Palantir is seeing growth re-accelerate from 2023. Pretty impressive. #2.

article thumbnail

How Banks Are Winning with AI and Automated Machine Learning

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.

article thumbnail

5 Key Learnings from How Top SaaS Companies are Actually Productizing AI with Paragon

SaaStr

Why LLM Wrappers Failed – And What Works Instead The first wave of AI products were mostly “LLM wrappers” – simple chatbots built on top of models like GPT. Here’s what Brandon Fu (CEO, Paragon) and Ethan Lee (Director of Product) shared at SaaStr AI Day about what’s actually working: 1.

AI 238
article thumbnail

Cutting Through the Noise of Gen AI with CEOs of Writer, Orby and Limitless + NEA

SaaStr

May Habib from Writer heads a full-stack generative AI company that combines large language models with microservices to build custom AI applications, agents, and workflows for enterprise clients. Writer is at the forefront of creating flexible, tailored AI solutions that integrate seamlessly into existing business processes.

AI Search 244
article thumbnail

How Banks Are Winning with AI and Automated Machine Learning

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.

article thumbnail

Intelligent Process Automation: Boosting Bots with AI and Machine Learning

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?

article thumbnail

Resilient Machine Learning with MLOps

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.

article thumbnail

MLOps 101: The Foundation for Your AI Strategy

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?

article thumbnail

Embedding BI: Architectural Considerations and Technical Requirements

While data platforms, artificial intelligence (AI), machine learning (ML), and programming platforms have evolved to leverage big data and streaming data, the front-end user experience has not kept up. Traditional Business Intelligence (BI) aren’t built for modern data platforms and don’t work on modern architectures.

article thumbnail

The Role of Artificial Intelligence in Pandemic Response: Lessons Learned From COVID-19

In March 2020, the world was hit with an unprecedented crisis when the COVID-19 pandemic struck. As the disease tragically took more and more lives, policymakers were confronted with widely divergent predictions of how many more lives might be lost and the best ways to protect people.

article thumbnail

5 Things a Data Scientist Can Do to Stay Current

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.

article thumbnail

How to Choose an AI Vendor

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.