AWS Machine Learning Blog

SEPTEMBER 18, 2024

What Zeta has accomplished in AI/ML In the fast-evolving landscape of digital marketing, Zeta Global stands out with its groundbreaking advancements in artificial intelligence. Zeta’s AI innovation is powered by a proprietary machine learning operations (MLOps) system, developed in-house.

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Pickl AI

NOVEMBER 29, 2024

Summary: Hydra simplifies process configuration in Machine Learning by dynamically managing parameters, organising configurations hierarchically, and enabling runtime overrides. It enhances scalability, experimentation, and reproducibility, allowing ML teams to focus on innovation. billion in 2022, is expected to soar to USD 505.42

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O'Reilly Media

OCTOBER 19, 2021

As with many burgeoning fields and disciplines, we don’t yet have a shared canonical infrastructure stack or best practices for developing and deploying data-intensive applications. What does a modern technology stack for streamlined ML processes look like? Why: Data Makes It Different. All ML projects are software projects.

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AWS Machine Learning Blog

SEPTEMBER 5, 2023

In order to improve our equipment reliability, we partnered with the Amazon Machine Learning Solutions Lab to develop a custom machine learning (ML) model capable of predicting equipment issues prior to failure. We first highlight how we use AWS Glue for highly parallel data processing.

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The MLOps Blog

JUNE 27, 2023

Alignment to other tools in the organization’s tech stack Consider how well the MLOps tool integrates with your existing tools and workflows, such as data sources, data engineering platforms, code repositories, CI/CD pipelines, monitoring systems, etc. and Pandas or Apache Spark DataFrames.

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AWS Machine Learning Blog

JUNE 27, 2023

When machine learning (ML) models are deployed into production and employed to drive business decisions, the challenge often lies in the operation and management of multiple models. They swiftly began to work on AI/ML capabilities by building image recognition models using Amazon SageMaker.

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AWS Machine Learning Blog

APRIL 29, 2024

In 2024, however, organizations are using large language models (LLMs), which require relatively little focus on NLP, shifting research and development from modeling to the infrastructure needed to support LLM workflows. Metaflow’s coherent APIs simplify the process of building real-world ML/AI systems in teams.

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AWS Machine Learning Blog

DECEMBER 12, 2023

In this post, we’ll summarize training procedure of GPT NeoX on AWS Trainium , a purpose-built machine learning (ML) accelerator optimized for deep learning training. M tokens/$) trained such models with AWS Trainium without losing any model quality. models on AWS Trn1 with Neuron NeMo library.

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AWS Machine Learning Blog

JUNE 22, 2023

Utilizing data streamed through LnW Connect, L&W aims to create better gaming experience for their end-users as well as bring more value to their casino customers. Predictive maintenance is a common ML use case for businesses with physical equipment or machinery assets. We used AutoGluon to explore several classic ML algorithms.

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AWS Machine Learning Blog

APRIL 25, 2024

Hugging Face is a popular open source hub for machine learning (ML) models. He has helped launch and scale the AI/ML powered Amazon SageMaker service and has implemented several proofs of concept using Amazon AI services. Kiran Challapalli is a deep tech business developer with the AWS public sector.

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Mlearning.ai

NOVEMBER 29, 2023

How to Use Machine Learning (ML) for Time Series Forecasting — NIX United The modern market pace calls for a respective competitive edge. Data forecasting has come a long way since formidable data processing-boosting technologies such as machine learning were introduced.

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phData

AUGUST 24, 2023

What Does a Credit Score or Decisioning ML Pipeline Look Like? Now that we have a firm grasp on the underlying business case, we will now define a machine learning pipeline in the context of credit models. Data Preparation The first step in the process is data collection and preparation. Want to learn more?

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The MLOps Blog

NOVEMBER 30, 2022

These days enterprises are sitting on a pool of data and increasingly employing machine learning and deep learning algorithms to forecast sales, predict customer churn and fraud detection, etc., ML model versioning: where are we at? The short answer is we are in the middle of a data revolution.

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Heartbeat

JUNE 12, 2023

Model Performance Monitoring includes measuring the model’s accuracy and other performance metrics over time. Feedback Monitoring gathers feedback from users and stakeholders to ensure the model meets their needs and expectations. This article will cover the challenges you can face with Machine Learning models in production.

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AWS Machine Learning Blog

SEPTEMBER 19, 2024

SageMaker Training is a comprehensive, fully managed ML service that enables scalable model training. It provides flexible compute resource selection, support for custom libraries, a pay-as-you-go pricing model, and self-healing capabilities. GB saved to /opt/ml/input/data/model/quantized/meta_model_0-8da4w.pt.

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Pickl AI

NOVEMBER 28, 2024

Understanding Machine Learning algorithms and effective data handling are also critical for success in the field. Introduction Machine Learning ( ML ) is revolutionising industries, from healthcare and finance to retail and manufacturing. This growth signifies Python’s increasing role in ML and related fields.

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Data Science Dojo

AUGUST 28, 2023

Model fine-tuning Model training: Once the data is prepared, the LLM is trained. This is done by using a machine learning algorithm to learn the patterns in the data. Model evaluation: Once the LLM is trained, it needs to be evaluated to see how well it performs.

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ODSC - Open Data Science

OCTOBER 7, 2024

In today’s landscape, AI is becoming a major focus in developing and deploying machine learning models. It isn’t just about writing code or creating algorithms — it requires robust pipelines that handle data, model training, deployment, and maintenance. Model Training: Running computations to learn from the data.

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DataRobot

DECEMBER 16, 2021

and train models with a single click of a button. Advanced users will appreciate tunable parameters and full access to configuring how DataRobot processes data and builds models with composable ML. Access the full potential of your models by using DataRobot with your text data.

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AWS Machine Learning Blog

SEPTEMBER 26, 2024

It removes the undifferentiated heavy lifting involved in building and optimizing machine learning (ML) infrastructure for training foundation models (FMs). In this post, we share an ML infrastructure architecture that uses SageMaker HyperPod to support research team innovation in video generation.

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ODSC - Open Data Science

APRIL 6, 2023

But its status as the go-between for programming and data professionals isn’t its only power. Within SQL you can also filter data, aggregate it and create valuations, manipulate data, update it, and even do data modeling.

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phData

AUGUST 29, 2024

Based on our experience, manufacturing customers have the most success (in terms of adopting AI) with the following applications: Demand Forecasting Whether building better demand forecasts , optimizing logistics, scheduling production, or improving inventory management, ML-driven demand forecasting delivers real predictive value.

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AWS Machine Learning Blog

SEPTEMBER 12, 2024

Thomson Reuters , a global content and technology-driven company, has been using artificial intelligence and machine learning (AI/ML) in its professional information products for decades. It provides resilient and persistent clusters for large-scale deep learning training of FMs on long-running compute clusters.

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DagsHub

DECEMBER 5, 2023

This is where ML experiment tracking comes into play! What is ML Experiment Tracking? ML experiment tracking is the process of recording, organizing, and analyzing the results of ML experiments. It helps data scientists keep track of their experiments, reproduce their results, and collaborate with others effectively.

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DagsHub

APRIL 21, 2024

Source: [link] Similarly, while building any machine learning-based product or service, training and evaluating the model on a few real-world samples does not necessarily mean the end of your responsibilities. You need to make that model available to the end users, monitor it, and retrain it for better performance if needed.

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DataRobot Blog

SEPTEMBER 13, 2022

Data scientists drive business outcomes. Many implement machine learning and artificial intelligence to tackle challenges in the age of Big Data. They develop and continuously optimize AI/ML models , collaborating with stakeholders across the enterprise to inform decisions that drive strategic business value.

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IBM Journey to AI blog

JULY 6, 2023

It uses advanced tools to look at raw data, gather a data set, process it, and develop insights to create meaning. Areas making up the data science field include mining, statistics, data analytics, data modeling, machine learning modeling and programming. What is machine learning?

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Iguazio

FEBRUARY 8, 2024

LLM models are large deep learning models that are trained on vast datasets, are adaptable to various tasks and specialize in NLP tasks. They are characterized by their enormous size, complexity, and the vast amount of data they process. Data Pipeline - Manages and processes various data sources.

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Heartbeat

NOVEMBER 28, 2023

Introduction Welcome to the step-by-step guide on efficiently managing TensorFlow/Keras model development with Comet. TensorFlow and Keras have emerged as powerful frameworks for building and training deep learning models. Introducing MLOps Machine learning (ML) is an essential tool for businesses of all sizes.

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phData

MARCH 4, 2024

By utilizing Snowflake as its central repository for user data and integrating it with various machine-learning tools, the service would be able to store and analyze petabytes of data efficiently, providing accurate recommendations at scale. Many datasets are produced and sold on the Marketplace to enrich data models.

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DagsHub

OCTOBER 14, 2024

With these several trials, it is quite common to lose track of all the combinations that you might have tried out in pursuit of a better ML model. Why is ML Experiment Tracking Important? This is one of the most important reasons why you need to keep track of all the experiments (including minor ones) in the modeling phase.

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Heartbeat

OCTOBER 5, 2023

Unsurprisingly, Machine Learning (ML) has seen remarkable progress, revolutionizing industries and how we interact with technology. The emergence of Large Language Models (LLMs) like OpenAI's GPT , Meta's Llama , and Google's BERT has ushered in a new era in this field. Their mission?

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Mlearning.ai

AUGUST 18, 2023

As an ML engineer you’re in charge of some code/model. MLOps cover all of the rest, how to track your experiments, how to share your work, how to version your models etc (Full list in the previous post. ). Not having a local model is not an excuse to throw organization, versioning and just good ol’ clean code patterns for.

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Pickl AI

MAY 30, 2023

Moreover, you can easily opt for 6 month certification program that pays well in the field that will allow you to gain perfection in ML. Learn the techniques in Machine Learning Use different tools for applications of ML and NLP Salary of the ML Engineer in India ranges between 3 Lakhs to 20.8 Lakhs annually.

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Iguazio

JANUARY 22, 2024

Definitions: Foundation Models, Gen AI, and LLMs Before diving into the practice of productizing LLMs, let’s review the basic definitions of GenAI elements: Foundation Models (FMs) - Large deep learning models that are pre-trained with attention mechanisms on massive datasets.

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AWS Machine Learning Blog

MAY 30, 2023

All you do is share data along with labeling requirements, and Ground Truth Plus sets up and manages your data labeling workflow based on these requirements. From there, an expert workforce that is trained on a variety of machine learning (ML) tasks labels your data. He holds a PhD from the University of Waterloo.

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ML Review

FEBRUARY 26, 2018

Since intentions determine the subsequent domain identification flow, the intention stratum is a necessary first step in initiating contextual and domain data model processes. Chitchatting, such as “I’m in a bad mood”, pulls up a method that marries the retrieval model with deep learning (DL).

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