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Language models have transformed how we interact with data, enabling applications like chatbots, sentiment analysis, and even automated content generation. However, most discussions revolve around large-scale models like GPT-3 or GPT-4, which require significant computational resources and vast datasets. While these models are powerful, they are not always practical for domain-specific tasks or deployment in […] The post Small Language Models, Big Impact: Fine-Tuning DistilGPT-2 for Medica
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AI adoption is reshaping sales and marketing. But is it delivering real results? We surveyed 1,000+ GTM professionals to find out. The data is clear: AI users report 47% higher productivity and an average of 12 hours saved per week. But leaders say mainstream AI tools still fall short on accuracy and business impact. Download the full report today to see how AI is being used — and where go-to-market professionals think there are gaps and opportunities.
Amazon Redshift is a fast, scalable, secure, and fully managed cloud data warehouse that you can use to analyze your data at scale. Tens of thousands of customers use Amazon Redshift to process exabytes of data to power their analytical workloads.The Amazon Redshift Data API simplifies programmatic access to Amazon Redshift data warehouses by providing a secure HTTP endpoint for executing SQL queries, so that you don’t have to deal with managing drivers, database connections, network configurati
Introduction Research published in academic journals plays a crucial role in improving drug discovery by revealing new biological targets, mechanisms, and treatment strategies. To effectively tap into this wealth of information, various AI technologies can sift through large amounts of literature to uncover key insights. This helps researchers identify potential drug targets and innovative solutions more easily, fostering collaboration and speeding up the drug development process.
It is appealing to migrate from self-managed OpenSearch and Elasticsearch clusters in legacy versions to Amazon OpenSearch Service to enjoy the ease of use, native integration with AWS services, and rich features from the open-source environment ( OpenSearch is now part of Linux Foundation ). However, the data migration process can be daunting, especially when downtime and data consistency are critical concerns for your production workload.
It is appealing to migrate from self-managed OpenSearch and Elasticsearch clusters in legacy versions to Amazon OpenSearch Service to enjoy the ease of use, native integration with AWS services, and rich features from the open-source environment ( OpenSearch is now part of Linux Foundation ). However, the data migration process can be daunting, especially when downtime and data consistency are critical concerns for your production workload.
At AWS, we are committed to empowering organizations with tools that streamline data analytics and transformation processes. We are excited to announce that the dbt adapter for Amazon Athena is now officially supported in dbt Cloud. This integration enables data teams to efficiently transform and manage data using Athena with dbt Cloud’s robust features, enhancing the overall data workflow experience.
Dataiku offers powerful data prep features to help you transform raw data into actionable insights, from seamless data ingestion to advanced feature engineering. So, when it comes to cool and underrated data preparation features in Dataiku that we think people should know about, we couldn’t stop at just one list.
Speaker: Ben Epstein, Stealth Founder & CTO | Tony Karrer, Founder & CTO, Aggregage
When tasked with building a fundamentally new product line with deeper insights than previously achievable for a high-value client, Ben Epstein and his team faced a significant challenge: how to harness LLMs to produce consistent, high-accuracy outputs at scale. In this new session, Ben will share how he and his team engineered a system (based on proven software engineering approaches) that employs reproducible test variations (via temperature 0 and fixed seeds), and enables non-LLM evaluation m
Organizations run millions of Apache Spark applications each month to prepare, move, and process their data for analytics and machine learning (ML). Building and maintaining these Spark applications is an iterative process, where developers spend significant time testing and troubleshooting their code. During development, data engineers often spend hours sifting through log files, analyzing execution plans, and making configuration changes to resolve issues.
In the era of big data and rapid technological advancement, the ability to analyze and interpret data effectively has become a cornerstone of decision-making and innovation. Python, renowned for its simplicity and versatility, has emerged as the leading programming language for data analysis. Its extensive library ecosystem enables users to seamlessly handle diverse tasks, from […] The post Top 10 Python Libraries for Data Analysis appeared first on Analytics Vidhya.
ZoomInfo customers aren’t just selling — they’re winning. Revenue teams using our Go-To-Market Intelligence platform grew pipeline by 32%, increased deal sizes by 40%, and booked 55% more meetings. Download this report to see what 11,000+ customers say about our Go-To-Market Intelligence platform and how it impacts their bottom line. The data speaks for itself!
Imagine a software engineer creating marketing strategies or a program manager designing tech apps—sounds unconventional, right? This is the new reality of the modern workspace, where multitasking is revolutionized with AI agents! Powered by advanced large language models (LLMs), AI agents are evolving from simple assistants to autonomous contributors.
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