AWS Machine Learning - AI

NOVEMBER 14, 2024

If an image is uploaded, it is stored in Amazon Simple Storage Service (Amazon S3) , and a custom AWS Lambda function will use a machine learning model deployed on Amazon SageMaker to analyze the image to extract a list of place names and the similarity score of each place name. Here is an example from LangChain.

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AWS Machine Learning - AI

NOVEMBER 20, 2024

The effectiveness of RAG heavily depends on the quality of context provided to the large language model (LLM), which is typically retrieved from vector stores based on user queries. The relevance of this context directly impacts the model’s ability to generate accurate and contextually appropriate responses.

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AWS Machine Learning - AI

DECEMBER 2, 2024

In this post, we explore the new Container Caching feature for SageMaker inference, addressing the challenges of deploying and scaling large language models (LLMs). You’ll learn about the key benefits of Container Caching, including faster scaling, improved resource utilization, and potential cost savings.

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AWS Machine Learning - AI

OCTOBER 16, 2024

The following were some initial challenges in automation: Language diversity – The services host both Dutch and English shows. Some local shows feature Flemish dialects, which can be difficult for some large language models (LLMs) to understand. The secondary LLM is used to evaluate the summaries on a large scale.

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Cloudera

NOVEMBER 13, 2024

Large Language Models (LLMs) will be at the core of many groundbreaking AI solutions for enterprise organizations. Here are just a few examples of the benefits of using LLMs in the enterprise for both internal and external use cases: Optimize Costs. Train new adapters for an LLM.

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AWS Machine Learning - AI

NOVEMBER 1, 2024

Fine-tuning is a powerful approach in natural language processing (NLP) and generative AI , allowing businesses to tailor pre-trained large language models (LLMs) for specific tasks. This process involves updating the model’s weights to improve its performance on targeted applications.

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O'Reilly Media - Ideas

NOVEMBER 19, 2024

Reasons for using RAG are clear: large language models (LLMs), which are effectively syntax engines, tend to “hallucinate” by inventing answers from pieces of their training data. Also, in place of expensive retraining or fine-tuning for an LLM, this approach allows for quick data updates at low cost.

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TechCrunch

OCTOBER 27, 2020

Deci’s insights screen combines all indicators of a deep learning model’s expected behavior in production, resulting in the Deci Score — a single metric summarizing the overall performance of the model. Image Credits: Deci. ”

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TechCrunch

OCTOBER 29, 2021

While at Wish, we learned that to offer the right shopping experience, you had to do absolute personalization,” Li told TechCrunch. That was done with machine learning engineers, but when I left Wish and was advising brands, I found that what we had at Wish was rare. Social commerce startup Social Chat is out to change that.

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AWS Machine Learning - AI

MARCH 13, 2025

DeepSeek-R1 , developed by AI startup DeepSeek AI , is an advanced large language model (LLM) distinguished by its innovative, multi-stage training process. Instead of relying solely on traditional pre-training and fine-tuning, DeepSeek-R1 integrates reinforcement learning to achieve more refined outputs.

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TechCrunch

FEBRUARY 11, 2022

Artificial intelligence has infiltrated a number of industries, and the restaurant industry was one of the latest to embrace this technology, driven in main part by the global pandemic and the need to shift to online orders. That need continues to grow. billion by 2025.

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AWS Machine Learning - AI

APRIL 10, 2025

To assess system reliability, engineering teams often rely on key metrics such as mean time between failures (MTBF), which measures the average operational time between hardware failures and serves as a valuable indicator of system robustness. SageMaker HyperPod runs health monitoring agents in the background for each instance.

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TechCrunch

JUNE 2, 2021

While early on, the questions were about how to build machine learning models, today the problem is how to build predictable processes around machine learning, especially in large organizations with sizable teams. He noted that the industry has changed quite a bit since then. Image Credits: Iterative.

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TechCrunch

JUNE 7, 2022

. “The main challenge in building or adopting infrastructure for machine learning is that the field moves incredibly quickly. For example, natural language processing was considered out of reach for industrial applications just a few years ago but is rapidly becoming commonplace today,” Tobin said. ”

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Perficient

NOVEMBER 20, 2024

Introduction to Multiclass Text Classification with LLMs Multiclass text classification (MTC) is a natural language processing (NLP) task where text is categorized into multiple predefined categories or classes. Traditional approaches rely on training machine learning models, requiring labeled data and iterative fine-tuning.

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AWS Machine Learning - AI

NOVEMBER 7, 2024

You can also bring your own customized models and deploy them to Amazon Bedrock for supported architectures. Prompt catalog – Crafting effective prompts is important for guiding large language models (LLMs) to generate the desired outputs. It’s serverless so you don’t have to manage the infrastructure.

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TechCrunch

NOVEMBER 14, 2021

And, we’ve also seen big advances in artificial intelligence. One thing that has clearly advanced substantially in the past decade or so is artificial intelligence. This sheer volume of data we are able to access, process and feed into models has changed AI from science fiction into reality in a few short years.

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AWS Machine Learning - AI

OCTOBER 29, 2024

This engine uses artificial intelligence (AI) and machine learning (ML) services and generative AI on AWS to extract transcripts, produce a summary, and provide a sentiment for the call. All of this data is centralized and can be used to improve metrics in scenarios such as sales or call centers.

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AWS Machine Learning - AI

NOVEMBER 15, 2024

Model monitoring of key NLP metrics was incorporated and controls were implemented to prevent unsafe, unethical, or off-topic responses. The flexible, scalable nature of AWS services makes it straightforward to continually refine the platform through improvements to the machine learning models and addition of new features.

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TechCrunch

SEPTEMBER 8, 2022

But a particular category of startup stood out: those applying AI and machine learning to solve problems, especially for business-to-business clients. The platform is powered by large language models (think GPT-3) that reference several sources to find the most likely answers, according to co-founder Michael Royzen.

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AWS Machine Learning - AI

MARCH 18, 2025

This application allows users to ask questions in natural language and then generates a SQL query for the users request. Large language models (LLMs) are trained to generate accurate SQL queries for natural language instructions. However, off-the-shelf LLMs cant be used without some modification.

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Cloudera

DECEMBER 4, 2024

Today, Artificial Intelligence (AI) and Machine Learning (ML) are more crucial than ever for organizations to turn data into a competitive advantage. To unlock the full potential of AI, however, businesses need to deploy models and AI applications at scale, in real-time, and with low latency and high throughput.

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N2Growth Blog

DECEMBER 4, 2024

Technologies such as artificial intelligence and machine learning allow for sophisticated segmentation and targeting, enhancing the relevance and impact of marketing messages. Joint Metrics: Developing shared key performance indicators (KPIs) to measure success collectively.

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TechCrunch

DECEMBER 22, 2020

Conti acknowledged that there’s other discount-optimizing software out there, but he suggested none of them offers what Bandit ML does: “off the shelf tools that use machine learning the way giants like Uber, Amazon and Walmart do.”

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Xebia

FEBRUARY 23, 2025

We observe that the skills, responsibilities, and tasks of data scientists and machine learning engineers are increasingly overlapping. The real challenge in 2025 is using AI effectively and responsibly, which is where LLMOps (LLM Operations) comes in. It’s the toolkit for reliable, safe, and value-generating AI.

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CIO

OCTOBER 30, 2024

Quantum Metric is here to help your business harness the power of Gen AI. As Gen AI capabilities expand, so too will the opportunities for innovation and differentiation. Those who act now will lead the charge, setting new standards for what it means to deliver meaningful, impactful digital experiences in the years to come.

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CIO

AUGUST 9, 2024

At Atlanta’s Hartsfield-Jackson International Airport, an IT pilot has led to a wholesale data journey destined to transform operations at the world’s busiest airport, fueled by machine learning and generative AI. This allows us to excel in this space, and we can see some real-time ROI into those analytic solutions.”

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AWS Machine Learning - AI

OCTOBER 29, 2024

Observability refers to the ability to understand the internal state and behavior of a system by analyzing its outputs, logs, and metrics. This post focused is on Amazon Bedrock, but it can be extended to broader machine learning operations (MLOps) workflows or integrated with other AWS services such as AWS Lambda or Amazon SageMaker.

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TechCrunch

NOVEMBER 13, 2020

Here are the top five things that fell into the “learning and exploring” cohort, in ranked order: Blockchain. AI/machine learning. AI/machine learning. There’s already a clear understanding of at least some of the use cases or problems that need solving, and return-on-investment metrics have been established.

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AWS Machine Learning - AI

MARCH 14, 2025

Organizations building and deploying AI applications, particularly those using large language models (LLMs) with Retrieval Augmented Generation (RAG) systems, face a significant challenge: how to evaluate AI outputs effectively throughout the application lifecycle.

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AWS Machine Learning - AI

MARCH 5, 2025

These assistants can be powered by various backend architectures including Retrieval Augmented Generation (RAG), agentic workflows, fine-tuned large language models (LLMs), or a combination of these techniques. To learn more about FMEval, see Evaluate large language models for quality and responsibility of LLMs.

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Hacker Earth Developers Blog

AUGUST 2, 2022

Artificial intelligence has generated a lot of buzz lately. More than just a supercomputer generation, AI recreated human capabilities in machines. Hiring activities of a company are mainly outsourced to third-party AI recruitment agencies that run machine learning-based algorithmic expressions on candidate profiles.

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CIO

JANUARY 24, 2024

Generative AI and large language models (LLMs) like ChatGPT are only one aspect of AI. Downsides: Not generative; model behavior can be a black box; results can be challenging to explain. Don’t use generative AI for a problem that classical machine learning has already solved.

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AWS Machine Learning - AI

NOVEMBER 1, 2024

Metrics can be graphed by application inference profile, and teams can set alarms based on thresholds for tagged resources. With the introduction of application inference profiles, organizations need to retrieve the inference profile ARN to invoke model inference for on-demand foundation models.

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CIO

SEPTEMBER 12, 2024

Zoho has updated Zoho Analytics to add artificial intelligence to the product and enables customers create custom machine-learning models using its new Data Science and Machine Learning (DSML) Studio. Auto Analysis enables AI-powered automated metrics, reports, and the generation of dashboards.

Analytics 197

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CIO

MAY 13, 2024

IBM is betting big on its toolkit for monitoring generative AI and machine learning models, dubbed watsonx.governance , to take on rivals and position the offering as a top AI governance product, according to a senior executive at IBM. watsonx.governance is a toolkit for governing generative AI and machine learning models.

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AWS Machine Learning - AI

JULY 24, 2024

Large language models (LLMs) have achieved remarkable success in various natural language processing (NLP) tasks, but they may not always generalize well to specific domains or tasks. You may need to customize an LLM to adapt to your unique use case, improving its performance on your specific dataset or task.

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