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Banking on mainframe-led digital transformation for financial services

IBM Journey to AI blog

The world’s biggest modernization challenges are concentrated in the banking industry. Before the internet and cloud computing , and before smartphones and mobile apps, banks were shuttling payments through massive electronic settlement gateways and operating mainframes as systems of record.

DevOps 126
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FMOps/LLMOps: Operationalize generative AI and differences with MLOps

AWS Machine Learning Blog

Data science team – Data scientists need to focus on creating the best model based on predefined key performance indicators (KPIs) working in notebooks. Text-to-image – Labeled data, such as pairs of , has been used to train FMs, which are able to predict the best combination of pixels. 15K available FM reference Step 1.

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The most valuable AI use cases for business

IBM Journey to AI blog

But the question for those of us in business is what are the best business uses? More benefits from AI include building a more sustainable IT system and improving the continuous integration/continuous (CI/CD) delivery pipelines. We’re all amazed by what AI can do.

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Seldon and Snorkel AI partner to advance data-centric AI

Snorkel AI

One of Snorkel AI’s banking customers, for example, has deployed 15 downstream applications to extract relevant information from a single data source: 10-Ks. 10-Ks contain a wealth of valuable information that can be relevant across a range of ML use cases from interest rate swaps to risk factor assessments to KYC initiatives.

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Seldon and Snorkel AI partner to advance data-centric AI

Snorkel AI

One of Snorkel AI’s banking customers, for example, has deployed 15 downstream applications to extract relevant information from a single data source: 10-Ks. 10-Ks contain a wealth of valuable information that can be relevant across a range of ML use cases from interest rate swaps to risk factor assessments to KYC initiatives.

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Explore advanced techniques for hyperparameter optimization with Amazon SageMaker Automatic Model Tuning

AWS Machine Learning Blog

Hyperparameters are the knobs and levers that we use to adjust the training process, such as learning rate, batch size, regularization strength, and others, depending on the specific model and task at hand. How can I effectively search a huge hyperparameter space to find those best-performing values? Makes sense, right?

ML 83
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What Is Hyperautomation?

O'Reilly Media

We’ll see it in banking. We have to be careful about process discovery because automating the wrong processes, or automating them in inappropriate ways, wastes resources at best; at worst, it can make a business uncompetitive. Never assume that most businesses are well run, and that they represent some sort of “best practice.”