AWS Machine Learning Blog

JANUARY 5, 2024

Many organizations have been using a combination of on-premises and open source data science solutions to create and manage machine learning (ML) models. Data science and DevOps teams may face challenges managing these isolated tool stacks and systems.

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Explosion

JANUARY 29, 2023

We’ve published several technical blog posts and reports, and created a bunch of new videos covering many tips and tricks to get the most out of our developer tools. Edi, Lj and team have written a comprehensive blog post covering full details of the spancat implementation as well as an architecture case study on nested NER.

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phData

JANUARY 8, 2024

In this blog, we will provide general tips for crafting effective prompts and list some advanced techniques for some of the top use cases of LLMs. Quick Overview of Large Language Models (LLMs) A large language model (LLM) is a cutting-edge AI trained on massive amounts of text data.

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Heartbeat

NOVEMBER 22, 2023

Clearly explain the prerequisites required for the task and ensure that your understanding of the model aligns with your expectations for safe execution. Want to learn how to build modern software with LLMs using the newest tools and techniques in the field? Building a Shopbot using OpenAI and CometLLM 1.

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Heartbeat

FEBRUARY 29, 2024

A Multilingual Landscape One of the defining features of modern software engineering is the diversity of programming languages available. This linguistic diversity has enabled programmers to tackle various tasks, from web development and data analysis to system-level programming.

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IBM Journey to AI blog

AUGUST 1, 2023

This explains the existence of both incident and problem management, two important processes for issue and error control, maintaining uptime, and ultimately, delivering a great service to customers and other stakeholders. Organizations increasingly depend on digital technologies to serve their customers and collaborate with partners.

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AWS Machine Learning Blog

AUGUST 14, 2023

This is useful where limited labeled data is available for training. BLOOM is an autoregressive LLM trained to continue text from a prompt on vast amounts of text data using industrial-scale computational resources. The BloomZ 176B model has been trained with a large amount of data, making to applicable for many general-purpose tasks.

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AWS Machine Learning Blog

FEBRUARY 19, 2024

Modern chatbots can serve as digital agents, providing a new avenue for delivering 24/7 customer service and support across many industries. Chatbots also offer valuable data-driven insights into customer behavior while scaling effortlessly as the user base grows; therefore, they present a cost-effective solution for engaging customers.

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phData

APRIL 2, 2024

By leveraging cloud-based data platforms such as Snowflake Data Cloud , these commercial banks can aggregate and curate their data to understand individual customer preferences and offer relevant and personalized products. Why Use Snowflake for Commercial Banks?

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The MLOps Blog

APRIL 20, 2023

In this blog post, I’ll share my own experiences and the hard-won insights I’ve gained from designing, building, and deploying cutting-edge CV models across various platforms like cloud, on-premise, and edge devices. Data augmentation Data augmentation is essential for boosting the size and diversity of your dataset.

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The MLOps Blog

OCTOBER 3, 2023

This article was originally an episode of the ML Platform Podcast , a show where Piotr Niedźwiedź and Aurimas Griciūnas, together with ML platform professionals, discuss design choices, best practices, example tool stacks, and real-world learnings from some of the best ML platform professionals. Nice to have you here, Miki.

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IBM Journey to AI blog

SEPTEMBER 20, 2023

With the increasing importance of processing data where work is being performed, serving AI models at the enterprise edge enables near-real-time predictions, while abiding by data sovereignty and privacy requirements. First, enterprises produce a vast amount of unlabeled data, only a fraction of which is labeled for AI model training.

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AWS Machine Learning Blog

DECEMBER 22, 2023

This blog is co-written with Josh Reini, Shayak Sen and Anupam Datta from TruEra Amazon SageMaker JumpStart provides a variety of pretrained foundation models such as Llama-2 and Mistal 7B that can be quickly deployed to an endpoint. on(Select.Record.calls[0].args.args[0]).on_output() args.args[0]).on_output() train_and_test_dataset["train"].to_json("train.jsonl")

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AWS Machine Learning Blog

APRIL 1, 2024

In the last few years, the size of current generation models has increased significantly, and they require modern tools and infrastructure to be trained efficiently and at scale. PyTorch Distributed Data Parallelism (DDP) helps process data at scale in a simple and robust manner, but it requires the model to fit on one GPU.

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AWS Machine Learning Blog

DECEMBER 6, 2023

When prompted correctly, these models can carry coherent conversations without any task-specific training data. However, they can’t generalize well to enterprise-specific questions because, to generate an answer, they rely on the public data they were exposed to during pre-training. Sort the revenues in descending order.

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AWS Machine Learning Blog

MAY 17, 2024

The model is deployed in an AWS secure environment and under your VPC controls, providing data encryption at rest and in-transit. You can choose the model card to view details about the model such as license, data used to train, and how to use. SageMaker JumpStart availability is dependent on the model; Mixtral-8x22B v0.1

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AWS Machine Learning Blog

MARCH 14, 2024

Given the data sources, LLMs provided tools that would allow us to build a Q&A chatbot in weeks, rather than what may have taken years previously, and likely with worse performance. Using structured data to answer questions requires a way to effectively extract data that’s relevant to a user’s query.

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The MLOps Blog

JULY 18, 2023

While I wasn’t doing speech recognition, I just was familiar with fast fourier transforms and a lot of the componentry that goes into that front end, the speech recognition stack. I naturally gravitated towards it because I deeply understood that early part of the stack. Jason: Yeah, that’s really true.

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phData

JUNE 7, 2023

The best AI-powered products are fueled by a diverse collection of high-quality data. The most critical and impactful step you can take towards enterprise AI today is ensuring you have a solid data foundation built on the modern data stack with mature operational pipelines, including all your most critical operational data.

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Heartbeat

DECEMBER 20, 2023

This blog aims to equip you with a thorough understanding of these powerful neural network architectures. Andrew Jones of Data Science Infinity Imagine that you train a network to recognize pictures of a statue. Want to learn how to build modern software with LLMs using the newest tools and techniques in the field?

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Dataconomy

MAY 8, 2023

If you are looking for an AI drawing generator list that explains the best of them, you have just found it and more information about AI art’s current state. You might think of AI drawing as a generative art where the artist combines data and algorithms to create something completely new.

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IBM Journey to AI blog

MAY 9, 2023

Unlike traditional ML, where each new use case requires a new model to be designed and built using specific data, foundation models are trained on large amounts of unlabeled data, which can then be adapted to new scenarios and business applications. AI is already driving results for business. Watsonx.ai Watsonx.ai

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Alation

JANUARY 25, 2022

Data fabric is now on the minds of most data management leaders. In our previous blog, Data Mesh vs. Data Fabric: A Love Story , we defined data fabric and outlined its uses and motivations. The data catalog is a foundational layer of the data fabric.

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PyImageSearch

OCTOBER 16, 2023

Setting Up a GPU Development Environment Using Docker Introduction Docker is slowly becoming ubiquitous within the modern development ecosystem. Setting Up Our Virtual Machine Allocation For this blog, we will use the Google Cloud Platform (GCP) to host a GPU-enabled Virtual Machine. It can also be used on Cloud-based providers (e.g.,

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Heartbeat

JUNE 14, 2023

1] explain that the reliability diagram is plotted as follows. This post from stats stack exchange provides further intuition behind T. Finally, we explained about a few calibration techniques that can enable neural networks to output reliable and interpretable confidence estimates. On calibration of modern neural networks.

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phData

MAY 25, 2023

How to Optimize Power BI and Snowflake for Advanced Analytics Spencer Baucke May 25, 2023 The world of business intelligence and data modernization has never been more competitive than it is today. Microsoft Power BI has been the leader in the analytics and business intelligence platforms category for several years running.

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Snorkel AI

MARCH 1, 2023

Foundation models are large AI models trained on enormous quantities of unlabeled data—usually through self-supervised learning. Data scientists can build upon generalized FMs and fine-tune custom versions with domain-specific or task-specific training data. and they’re stacked at the beginning of the distribution.

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Flipboard

JULY 31, 2023

Brief Background of Machine Learning Did you know that machine learning is a part of artificial intelligence that enables computers to learn from data without explicit programming using statistical techniques? Importance and Role of Datasets in Machine Learning Data is king. Many call this software 2.0.

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AWS Machine Learning Blog

FEBRUARY 10, 2023

Through a collaboration between the Next Gen Stats team and the Amazon ML Solutions Lab , we have developed the machine learning (ML)-powered stat of coverage classification that accurately identifies the defense coverage scheme based on the player tracking data. and Big Data Bowl Kaggle Zoo solution ( Gordeev et al. ).

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AWS Machine Learning Blog

OCTOBER 9, 2023

Sliding-window attention uses the stacked layers of a transformer to attend in the past beyond the window size to increase context length. The model is deployed in an AWS secure environment and under your VPC controls, helping ensure data security. Rats always gnaw, and no one is certain why—there are few modern rat studies.

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Heartbeat

NOVEMBER 6, 2023

Module Chaining: The chaining feature allows users to stack up various modules to enhance the model’s performance further. Want to learn how to build modern software with LLMs using the newest tools and techniques in the field? The output of one model becomes the input of the subsequent one. Let’s discuss these in detail.

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ML Review

AUGUST 25, 2018

In this post I will explain NALU, its architecture, its components and significance over traditional neural networks. They can almost always learn any meaningful relationship between inputs (data or features) and outputs (labels or targets). Observe that how this NAC is different than the one mentioned first in the post.

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AWS Machine Learning Blog

NOVEMBER 30, 2023

To do this, we’re investing and rapidly innovating to provide the most comprehensive set of capabilities across the three layers of the generative AI stack. Customers must acquire large amounts of data and prepare it. This typically involves a lot of manual work cleaning data, removing duplicates, enriching and transforming it.

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The MLOps Blog

MAY 9, 2023

They run scripts manually to preprocess their training data, rerun the deployment scripts, manually tune their models, and spend their working hours keeping previously developed models up to date. The orchestration here implies that the dependencies and data flow between the workflow steps must be completed in the proper order.

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The MLOps Blog

DECEMBER 28, 2022

Hence, the ML teams must have a mix of strong data architects and engineering experts that can successfully operationalize the ML model. Data scientists frame the business problem and the objective into a statistical solution and start with the very first step of data exploration.

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The MLOps Blog

MARCH 21, 2023

From gathering and processing data to building models through experiments, deploying the best ones, and managing them at scale for continuous value in production—it’s a lot. As the number of ML-powered apps and services grows, it gets overwhelming for data scientists and ML engineers to build and deploy models at scale.

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phData

NOVEMBER 9, 2023

This blog was originally written by Keith Smith and updated for 2024 by Justin Delisi. Snowflake’s Data Cloud has emerged as a leader in cloud data warehousing. What is the Snowflake Data Cloud? What Components Make up the Snowflake Data Cloud?

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