LLMs enable an exciting new class of data processing applications over large collections of unstructured documents. Several new programming frameworks have enabled developers to build these applications by composing them out of semantic operators: a declarative set of AI-powered data transformations with natural language specifications. These include LLM-powered maps, filters, joins, etc. used for document processing tasks such as information extraction, summarization, and more. While systems of semantic operators have achieved strong performance on benchmarks, they can be difficult to optimize. An optimizer for this setting must determine how to physically implement each semantic operator in a way that optimizes the system globally. Existing optimizers are limited in the number of optimizations they can apply, and most (if not all) cannot optimize system quality, cost, or latency subject to constraint(s) on the other dimensions. In this paper we present Abacus, an extensible, cost-based optimizer which searches for the best implementation of a semantic operator system given a (possibly constrained) optimization objective. Abacus estimates operator performance by leveraging a minimal set of validation examples and, if available, prior beliefs about operator performance. We evaluate Abacus on document processing workloads in the biomedical and legal domains (BioDEX; CUAD) and multi-modal question answering (MMQA). We demonstrate that systems optimized by Abacus achieve 18.7%-39.2% better quality and up to 23.6x lower cost and 4.2x lower latency than the next best system.

In recent years, as more enterprises have moved their infrastructure to the cloud, significant challenges have emerged in achieving holistic cloud spend visibility and cost optimization. FinOps practices provide a way for enterprises to achieve these business goals by optimizing cloud costs and bringing accountability to cloud spend. This paper presents ABACUS - Automated Budget Analysis and Cloud Usage Surveillance, a FinOps solution for optimizing cloud costs by setting budgets, enforcing those budgets through blocking new deployments, and alerting appropriate teams if spending breaches a budget threshold. ABACUS also leverages best practices like Infrastructure-as-Code to alert engineering teams of the expected cost of deployment before resources are deployed in the cloud. Finally, future research directions are proposed to advance the state of the art in this important field.

Join us for a 2 hour hands-on workshop and learn how to easily create and deploy models. Tomorrow's AI systems will be built by AI while data science teams play a supervisory role. Much like Tony Stark instructing Jarvis, data science teams can instruct generative AI to executive tasks. We will be doing a 2-hour end to end demonstration and workshop of our state of the art AI-Assisted Data Science platform. The workshop will kick off with a technical talk surrounding Generative AI, LLMs, and its implications within the industry.

Our popular event StateoftheArt() is back again this coming January. This time around we're proud to announce that we will open with an exciting discussion with Dr. Sebastian Raschka on generative AI and LLMS and an open Q&A for a chance to to learn more about the future of AI and deep learning. Certificates will be provided to those who attend this section. Afterwards, we deep-dive into exciting ways deep learning is applied across a wide variety of industries with leaders from Home Depot, Momentive, & Twilio. Following that is "The Economy of the Future" where you can tune in to an economist and learn their perspective on AI/ML.

Abacus.AI is the world's first end-to-end AI platform that enables real-time deep learning at scale for common enterprise use cases. With our state-of-the-art MLOps platform, you can bring your own models, or use our neural network techniques to create highly-accurate models, and operationalize them across a wide array of use cases including forecasting, personalization, vision, anomaly detection and NLP.

Abacus.AI, announced today that it has been recognized as a Cool Vendor based on the October 11, 2021 Gartner Report "Cool Vendors in AI Core Technologies" by Farhan Choudhary, Alexander Linden, Pieter den Hamer, Arun Chandrasekaran, and Jim Hare. Abacus.AI is the world's first end to end AI and ML platform and has custom vertical specific for many enterprise AI use-cases including personalization, forecasting, NLP and vision. The platform may be used by data science teams who want to plug and play their own models and instantly deploy their models to production or by line of business owners who may want to use Abacus's innovative neural-architecture-search techniques to build a custom model based on the shape of their datasets and the use-case. Its AI platform democratizes the use of AI enabling organizations of all sizes to design, train, and operationalize machine and deep learning models. In addition, Abacus.AI has vertically integrated support for several common enterprise use-cases. This allows organizations of all sizes, from small startups to Fortune 500 companies, to use the company's state-of-the-art platform to enhance their AI adoption.

Noogata, a startup that offers a no-code AI solution for enterprises, today announced that it has raised a $12 million seed round led by Team8, with participation from Skylake Capital. The company, which was founded in 2019 and counts Colgate and PepsiCo among its customers, currently focuses on e-commerce, retail and financial services, but it notes that it will use the new funding to power its product development and expand into new industries. The company's platform offers a collection of what are essentially pre-built AI building blocks that enterprises can then connect to third-party tools like their data warehouse, Salesforce, Stripe and other data sources. An e-commerce retailer could use this to optimize its pricing, for example, thanks to recommendations from the Noogata platform, while a brick-and-mortar retailer could use it to plan which assortment to allocate to a given location. "We believe data teams are at the epicenter of digital transformation and that to drive impact, they need to be able to unlock the value of data. They need access to relevant, continuous and explainable insights and predictions that are reliable and up-to-date," said Noogata co-founder and CEO Assaf Egozi.

With Abacus.AI, an organization’s team of developers--with access to the organization’s data--can use its platform to create deep-learning systems that can make accurate predictions without having to invest the time and resources to do so from scratch.

RealityEngines.AI, the machine learning startup co-founded by former AWS and Google exec Bindu Reddy, today announced that it is rebranding as Abacus.AI and launching its autonomous AI service into general availability. In addition, the company also today disclosed that it has raised a $13 million Series A round led by Index Ventures' Mike Volpi, who will also join the company's board. Seed investors Eric Schmidt, Jerry Yang and Ram Shriram also participated in this oversubscribed round, with Shriram also joining the company's board. New investors include Mariam Naficy, Erica Shultz, Neha Narkhede, Xuezhao Lan and Jeannette Furstenberg, as well as Decibel Partners. This new round brings the company's total funding to $18.25 million.

RealityEngines.AI, the machine learning startup co-founded by former AWS and Google exec Bindu Reddy, today announced that it is rebranding as Abacus.AI and launching its autonomous AI service into general availability. In addition, the company also today disclosed that it has raised a $13 million Series A round led by Index Ventures' Mike Volpi, who will also join the company's board. Seed investors Eric Schmidt, Jerry Yang and Ram Shriram also participated in this oversubscribed round, with Shriram also joining the company's board. This new round brings the company's total funding to $18.25 million. At its core, RealityEngines.AI's Abacus.AI's mission is to help businesses implement modern deep learning systems into their customer experience and business processes without having to do the heavy lifting of learning how to train models themselves.