partner content Articles and Reports

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Why Should Enterprises Care About Full-Stack Optimization of AI?

Why Should Enterprises Care About Full-Stack Optimization of AI?

In 2018, James Kobielus wrote an article on the AI market’s shift to workload-optimized hardware platforms, in which he proposed:
Workload-optimized hardware/software platforms will find a clear niche for on-premises deployment in enterprises’ AI development shops. Before long, no enterprise data lake will be complete without pre-optimized platforms for one or more of the core AI workloads: data ingest and preparation, data modeling and training, and data deployment and operationalization.
We are seeing Kobielus’ words come true. In the past year, nearly 100 companies have announced some sort of AI-optimized IP, chip, or system optimized, primarily for inferencing workloads but also for training. Hyperscalers like Facebook, Amazon, and Google are increasingly talking publicly about "full-stack" optimization of AI, from silicon, through algorithms, up to the application layer.

data-science

Maximizing a Small Data Science Team with AutoML

The financial services sector has been one of the early adopters of data science and AI technologies. That said, financial firms that have engaged in AI projects will have realized that they require a deep understanding of data management and skilled data science professionals to solve these complex problems.

Information Extraction in Oil and Gas - Using AI to Find Oil

Information Extraction in Oil and Gas – Using AI to Find Oil

Oil and gas companies face many of the same challenges as large banks and established insurance firms when it comes to searching through their backlogs of documents. They want to use the data stored within these documents to make decisions on where to drill and determine whether or not they’re in compliance with laws and regulations. 

AI and Alternative Data for Quantamental Investing

AI and Alternative Data for Quantamental Investing

AI and machine learning have had successful applications in the financial sector even before the entry of the mobile banking ecosystem. AI is being used to leverage insights from data for financial investing and trading, wealth management, asset management, and risk management.

AI Hardware - Businesses Are Considering More Than Just Performance

AI Hardware – Businesses Are Considering More Than Just Performance

AI hardware is a fast-growing interest among tech media, and there is a lot of opportunity for computer hardware developers when it comes to building chipsets for AI. That said, margins for AI chipsets can differ wildly depending on the use-case for which they’re being built.

Information Extraction in Insurance - Claims and Underwriting

Information Extraction in Insurance – Claims and Underwriting

Customer data is essential for insurance firms to stay competitive in the coming decade. Insurance companies at present have backlogs of data on past and existing customers in the form of policy agreements, applications, and claims forms. They’ve also collected millions of images showing car damage, property damage, and personal injuries.