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5 machine learning essentials nontechnical leaders need to understand

TechCrunch

We’re living in a phenomenal moment for machine learning (ML), what Sonali Sambhus , head of developer and ML platform at Square, describes as “the democratization of ML.” It’s become the foundation of business and growth acceleration because of the incredible pace of change and development in this space.

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Taktile makes it easier to leverage machine learning in the financial industry

TechCrunch

Meet Taktile , a new startup that is working on a machine learning platform for financial services companies. This isn’t the first company that wants to leverage machine learning for financial products. They could use that data to train new models and roll out machine learning applications.

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Snorkel AI scores $35M Series B to automate data labeling in machine learning

TechCrunch

One of the more tedious aspects of machine learning is providing a set of labels to teach the machine learning model what it needs to know. It also announced a new tool called Application Studio that provides a way to build common machine learning applications using templates and predefined components.

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Hugging Face reaches $2 billion valuation to build the GitHub of machine learning

TechCrunch

Due to the success of this libary, Hugging Face quickly became the main repository for all things related to machine learning models — not just natural language processing. Essentially, Hugging Face is building the GitHub of machine learning. It’s a community-driven platform with a ton of repositories.

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Reduce ML training costs with Amazon SageMaker HyperPod

AWS Machine Learning - AI

Training a frontier model is highly compute-intensive, requiring a distributed system of hundreds, or thousands, of accelerated instances running for several weeks or months to complete a single job. For example, pre-training the Llama 3 70B model with 15 trillion training tokens took 6.5 During the training of Llama 3.1

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Remember when developers reigned supreme? The market for software coding goes soft

CIO

It seems like only yesterday when software developers were on top of the world, and anyone with basic coding experience could get multiple job offers. This yesterday, however, was five to six years ago, and developers are no longer the kings and queens of the IT employment hill. An example of the new reality comes from Salesforce.

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Leveraging AMPs for machine learning

CIO

As a result, employers no longer have to invest large sums to develop their own foundational models. Data scientists and AI engineers have so many variables to consider across the machine learning (ML) lifecycle to prevent models from degrading over time. However, the road to AI victory can be bumpy.