Remove 2019 Remove Artificial Inteligence Remove Data Engineering
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Remember when developers reigned supreme? The market for software coding goes soft

CIO

Job titles like data engineer, machine learning engineer, and AI product manager have supplanted traditional software developers near the top of the heap as companies rush to adopt AI and cybersecurity professionals remain in high demand.

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Are you ready for MLOps? 🫵

Xebia

The time when Hardvard Business Review posted the Data Scientist to be the “Sexiest Job of the 21st Century” is more than a decade ago [1]. In 2019 alone the Data Scientist job postings on Indeed rose by 256% [2]. No longer is Machine Learning development only about training a ML model.

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Henkel embraces gen AI as enabler and strategic disruptor

CIO

As Henkel CDIO Michael Nilles puts it, by 2019, Marc Andreessen’s pronouncement that “software is eating the world” had come true for the CPG sector, and Henkel was at risk of falling behind. “We We took it seriously and said we need to have software, data, and AI capabilities,” says Nilles, who signed on to the CDIO role at the time.

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Tecton raises $100M, proving that the MLOps market is still hot

TechCrunch

Machine learning can provide companies with a competitive advantage by using the data they’re collecting — for example, purchasing patterns — to generate predictions that power revenue-generating products (e.g. At a high level, Tecton automates the process of building features using real-time data sources.

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AI startup Faculty wins contract to predict future requirements for the UK’s NHS

TechCrunch

Faculty , a VC-backed artificial intelligence startup, has won a tender to work with the NHS to make better predictions about its future requirements for patients, based on data drawn from how it handled the COVID-19 pandemic. In December 2019, Faculty raised a $10.5 Data across the NHS is rather an archipelago.

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Predibase exits stealth with a low-code platform for building AI models

TechCrunch

“The major challenges we see today in the industry are that machine learning projects tend to have elongated time-to-value and very low access across an organization. “Given these challenges, organizations today need to choose between two flawed approaches when it comes to developing machine learning. .

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Databricks crossed $350M run rate in Q3, up from $200M one year ago

TechCrunch

Namely Databricks , a data analytics company that was most recently valued at around $6.2 billion in its October, 2019 Series F when it raised $400 million. Ghodsi reckons you need three things: First, data engineering, or getting customer data “massaged into the right forms so that you can actually start using it.”