Marktechpost

MARCH 12, 2024

When building machine learning (ML) models using preexisting datasets, experts in the field must first familiarize themselves with the data, decipher its structure, and determine which subset to use as features. So much so that a basic barrier, the great range of data formats, is slowing advancement in ML.

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The MLOps Blog

MAY 10, 2023

When working on real-world machine learning (ML) use cases, finding the best algorithm/model is not the end of your responsibilities. It is crucial to save, store, and package these models for their future use and deployment to production. Reusability & reproducibility: Building ML models is time-consuming by nature.

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AWS Machine Learning Blog

JANUARY 12, 2024

In contrast, text embeddings use machine learning (ML) capabilities to capture the meaning of unstructured data. Embeddings are generated by representational language models that translate text into numerical vectors and encode contextual information in a document.

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AWS Machine Learning Blog

MAY 30, 2024

The solution is available on the GitHub repository and can be deployed to your AWS account using an AWS Cloud Development Kit (AWS CDK) package. It also generates text embedding and multimodal embedding on the frame level using Amazon Titan models. Detect generic objects and labels using the Amazon Rekognition label detection API.

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AWS Machine Learning Blog

JULY 19, 2023

Amazon SageMaker Model Cards enable you to standardize how models are documented, thereby achieving visibility into the lifecycle of a model, from designing, building, training, and evaluation. They provide a factsheet of the model that is important for model governance.

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Ehud Reiter

DECEMBER 19, 2023

Papers mentioned in blogs I have talked about some of the 2023 papers which impressed me in previous blogs, including What LLMs cannot do A bad way to measure hallucination LLM hype brings memories of IBM Watson Future of NLG evaluation: LLMs and high quality human eval? Using large language models in psychology. Which is great!

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AWS Machine Learning Blog

NOVEMBER 9, 2023

Building out a machine learning operations (MLOps) platform in the rapidly evolving landscape of artificial intelligence (AI) and machine learning (ML) for organizations is essential for seamlessly bridging the gap between data science experimentation and deployment while meeting the requirements around model performance, security, and compliance.

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AWS Machine Learning Blog

JANUARY 10, 2024

ML Engineer at Tiger Analytics. The large machine learning (ML) model development lifecycle requires a scalable model release process similar to that of software development. Model developers often work together in developing ML models and require a robust MLOps platform to work in.

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Towards AI

SEPTEMBER 28, 2023

Deployment & Serving: Exploring 6 Key MLOps Questions using AWS SageMaker This article is part of the AWS SageMaker series for exploration of ’31 Questions that Shape Fortune 500 ML Strategy’. A/B testing, canary, shadow, etc.)▢ [Reproducibility] How do you package your ML models for serving in the cloud or at the edge?▢

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AWS Machine Learning Blog

MAY 10, 2023

Project Jupyter is a multi-stakeholder, open-source project that builds applications, open standards, and tools for data science, machine learning (ML), and computational science. Given the importance of Jupyter to data scientists and ML developers, AWS is an active sponsor and contributor to Project Jupyter.

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AWS Machine Learning Blog

NOVEMBER 14, 2023

At the core of these cutting-edge solutions lies a foundation model (FM), a highly advanced machine learning model that is pre-trained on vast amounts of data. However, these models are not without their challenges. These can be barriers for many organizations looking to build their own foundation models.

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AWS Machine Learning Blog

SEPTEMBER 20, 2023

In these scenarios, as you start to embrace generative AI, large language models (LLMs) and machine learning (ML) technologies as a core part of your business, you may be looking for options to take advantage of AWS AI and ML capabilities outside of AWS in a multicloud environment.

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IBM Journey to AI blog

MAY 15, 2024

Organizations require reliable data for robust AI models and accurate insights, yet the current technology landscape presents unparalleled data quality challenges. Data must be combined and harmonized from multiple sources into a unified, coherent format before being used with AI models. There are several styles of data integration.

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AWS Machine Learning Blog

DECEMBER 19, 2023

Artificial intelligence (AI) and machine learning (ML) are becoming an integral part of systems and processes, enabling decisions in real time, thereby driving top and bottom-line improvements across organizations. However, putting an ML model into production at scale is challenging and requires a set of best practices.

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AWS Machine Learning Blog

OCTOBER 2, 2023

We show you how to use AWS IoT Greengrass to manage model inference at the edge and how to automate the process using AWS Step Functions and other AWS services. In Part 2 , we showed how to automate the labeling and model training parts of the pipeline. If you haven’t read it yet, we recommend checking out Part 1.

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Mlearning.ai

APRIL 10, 2023

Hey guys in this very short blog we will see how we can train and test multiple models using Lazypredict which is an amazing open-source python package. With LazyPredict, data scientists can quickly build and compare several models on their datasets with just a few lines of code. The package requires Python 3.6

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AssemblyAI

MAY 25, 2023

or greater An AssemblyAI API key , which can be copied from the AssemblyAI dashboard All code in this blog post is available open source under the MIT license on GitHub under the transcribe-english-punctuation directory of the assemblyai-examples Git repository. or newer The AssemblyAI Python SDK, version 0.3.2 What's Next?

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Marktechpost

JULY 5, 2023

General circulation models (GCMs) are tools that scientists use to forecast the weather and climate in the future. These models are very simple to comprehend and produce appreciably accurate results. These models are very simple to comprehend and produce appreciably accurate results.

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AWS Machine Learning Blog

JUNE 27, 2023

Amazon SageMaker JumpStart is a machine learning (ML) hub that can help you accelerate your ML journey. With SageMaker JumpStart, you can discover and deploy publicly available and proprietary foundation models to dedicated Amazon SageMaker instances for your generative AI applications.

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DataRobot Blog

MARCH 16, 2023

Why model-driven AI falls short of delivering value Teams that just focus model performance using model-centric and data-centric ML risk missing the big picture business context. DataRobot AI Platform release we’ve broken down the barriers that exist across the ML lifecycle.

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AWS Machine Learning Blog

DECEMBER 13, 2023

In this post, we showcase fine-tuning a Llama 2 model using a Parameter-Efficient Fine-Tuning (PEFT) method and deploy the fine-tuned model on AWS Inferentia2. We then use a large model inference container powered by Deep Java Library (DJLServing) as our model serving solution.

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The MLOps Blog

MAY 9, 2023

One of the most prevalent complaints we hear from ML engineers in the community is how costly and error-prone it is to manually go through the ML workflow of building and deploying models. Building end-to-end machine learning pipelines lets ML engineers build once, rerun, and reuse many times.

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AWS Machine Learning Blog

MAY 8, 2024

To address this challenge, Dialog Axiata has pioneered a cutting-edge solution called the Home Broadband (HBB) Churn Prediction Model. This strategic use of AWS services delivers efficiency and scalability of their operations, as well as the implementation of advanced AI/ML applications.

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AWS Machine Learning Blog

FEBRUARY 29, 2024

Creating scalable and efficient machine learning (ML) pipelines is crucial for streamlining the development, deployment, and management of ML models. The framework code and examples presented here only cover model training pipelines, but can be readily extended to batch inference pipelines as well.

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The MLOps Blog

JUNE 27, 2023

As you delve into the landscape of MLOps in 2023, you will find a plethora of tools and platforms that have gained traction and are shaping the way models are developed, deployed, and monitored. Assess the pricing models, including any hidden costs, and ensure they fit your budget and scaling requirements. For example, neptune.ai

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AWS Machine Learning Blog

FEBRUARY 27, 2024

In this post, we share how Axfood, a large Swedish food retailer, improved operations and scalability of their existing artificial intelligence (AI) and machine learning (ML) operations by prototyping in close collaboration with AWS experts and using Amazon SageMaker. This is a guest post written by Axfood AB.

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AWS Machine Learning Blog

NOVEMBER 8, 2023

The MONAI AI models and applications can be hosted on Amazon SageMaker , which is a fully managed service to deploy machine learning (ML) models at scale. To create an asynchronous inference endpoint , you will need to create a SageMaker model and endpoint configuration first.

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AWS Machine Learning Blog

DECEMBER 13, 2023

Machine learning (ML) models do not operate in isolation. To deliver value, they must integrate into existing production systems and infrastructure, which necessitates considering the entire ML lifecycle during design and development. The SageMaker Model Registry centralizes model tracking, simplifying model deployment.

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AWS Machine Learning Blog

MAY 10, 2023

You can now register machine learning (ML) models built in Amazon SageMaker Canvas with a single click to the Amazon SageMaker Model Registry , enabling you to operationalize ML models in production. If it performs to your requirements, you can update the approval status of the model version to approved.

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Flipboard

SEPTEMBER 4, 2023

Envision yourself as an ML Engineer at one of the world’s largest companies. You make a Machine Learning (ML) pipeline that does everything, from gathering and preparing data to making predictions. Containers are lightweight packages that act as portable, sandboxed environments for use on any hardware that supports them.

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AWS Machine Learning Blog

DECEMBER 12, 2023

This new workforce requires rapid reskilling and understanding of disruptive services such as artificial intelligence (AI) and machine learning (ML) to drive meaningful outcomes. In this post, we share how Vodafone is advancing its ML skills using AWS DeepRacer and Accenture. Why is machine learning important to Vodafone?

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Heartbeat

JULY 18, 2023

Multiple models are typically developed as the training proceeds when performing ML and AI tasks, making it challenging to keep track of them. The development of ML and AI benefits greatly from team collaborations. In this article, we’ll show you how to use the R SDK for Comet to build a simple NLP project.

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Bugra Akyildiz

MARCH 16, 2024

Articles Vgel wrote a blog post on the representation engineering, focusing on the control vector in LLMs. Control vector is a vector (technically a list of vectors, one per layer) that you can apply to model activations during inference to control the model's behavior without additional prompting. for strength in (-2.2,

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AWS Machine Learning Blog

MAY 1, 2024

Large language models (LLMs) are making a significant impact in the realm of artificial intelligence (AI). Llama 2 is an auto-regressive language model that uses an optimized transformer architecture and is intended for commercial and research use in English. Llama2 by Meta is an example of an LLM offered by AWS.

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IBM Journey to AI blog

OCTOBER 20, 2023

Artificial intelligence platforms enable individuals to create, evaluate, implement and update machine learning (ML) and deep learning models in a more scalable way. This unified experience optimizes the process of developing and deploying ML models by streamlining workflows for increased efficiency.

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AWS Machine Learning Blog

JUNE 29, 2023

Fine-tuning large language models (LLMs) allows you to adjust open-source foundational models to achieve improved performance on your domain-specific tasks. In this post, we discuss the advantages of using Amazon SageMaker notebooks to fine-tune state-of-the-art open-source models.

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AWS Machine Learning Blog

FEBRUARY 20, 2024

In this post, we discuss deploying scalable machine learning (ML) models for diarizing media content using Amazon SageMaker , with a focus on the WhisperX model. The WhisperX model, based on OpenAI’s Whisper , performs transcriptions and diarization for media assets.

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