AWS Machine Learning Blog

FEBRUARY 12, 2024

Sharing in-house resources with other internal teams, the Ranking team machine learning (ML) scientists often encountered long wait times to access resources for model training and experimentation – challenging their ability to rapidly experiment and innovate. If it shows online improvement, it can be deployed to all the users.

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AWS Machine Learning Blog

OCTOBER 20, 2023

Customers of every size and industry are innovating on AWS by infusing machine learning (ML) into their products and services. Recent developments in generative AI models have further sped up the need of ML adoption across industries.

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ML Data Scientist ML Engineer Data Science 119

Flipboard

MARCH 13, 2023

If you’re a data scientist or you work with machine learning (ML) models, you have tools to label data, technology environments to train models, and …

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Machine Learning Explainability Data Scientist ML 123

Marktechpost

JULY 21, 2023

The software can explain, translate, summarize, or rewrite Any piece of writing. The software can explain, translate, summarize, or rewrite Any piece of writing. Having a tool that can help comprehend complicated data is vital in the convoluted realm of scientific study. Sider Chrome Extension Excellent for dealing with text.

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Data Scientist Neural Network Machine Learning Explainability 98

Marktechpost

MARCH 22, 2024

Data scientists and engineers frequently collaborate on machine learning ML tasks, making incremental improvements, iteratively refining ML pipelines, and checking the model’s generalizability and robustness. To build a well-documented ML pipeline, data traceability is crucial.

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AWS Machine Learning Blog

MAY 9, 2024

The rapid advancements in artificial intelligence and machine learning (AI/ML) have made these technologies a transformative force across industries. An effective approach that addresses a wide range of observed issues is the establishment of an AI/ML center of excellence (CoE). What is an AI/ML CoE?

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AWS Machine Learning Blog

JANUARY 23, 2025

In this post, we explain how to automate this process. The solution described in this post is geared towards machine learning (ML) engineers and platform teams who are often responsible for managing and standardizing custom environments at scale across an organization. Ajay Raghunathan is a Machine Learning Engineer at AWS.

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Towards AI

DECEMBER 15, 2024

When it comes to machine learning regression models, interviewers typically focus on five key performance metrics, which are the ones mostly used by Data Scientists in real time. In this article, I have explained each of these key metrics in a short and concise way, using real-life examples to make them easy to understand.

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ML Explainability Data Scientist Machine Learning 111

IBM Journey to AI blog

MAY 9, 2023

After some impressive advances over the past decade, largely thanks to the techniques of Machine Learning (ML) and Deep Learning , the technology seems to have taken a sudden leap forward. Through workload optimization an organization can reduce data warehouse costs by up to 50 percent by augmenting with this solution. [1] But why now?

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Unite.AI

APRIL 9, 2024

These techniques include Machine Learning (ML), deep learning , Natural Language Processing (NLP) , Computer Vision (CV) , descriptive statistics, and knowledge graphs. The Need for Explainability The demand for Explainable AI arises from the opacity of AI systems, which creates a significant trust gap between users and these algorithms.

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AWS Machine Learning Blog

OCTOBER 29, 2024

Recently, we’ve been witnessing the rapid development and evolution of generative AI applications, with observability and evaluation emerging as critical aspects for developers, data scientists, and stakeholders. With a strong background in AI/ML, Ishan specializes in building Generative AI solutions that drive business value.

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Towards AI

DECEMBER 15, 2024

When it comes to machine learning regression models, interviewers typically focus on five key performance metrics, which are the ones mostly used by Data Scientists in real time. In this article, I have explained each of these key metrics in a short and concise way, using real-life examples to make them easy to understand.

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ML Explainability Data Scientist Machine Learning 102

Marktechpost

APRIL 7, 2024

With the rapid advancements in machine learning (ML), there has been an increase in the demand for MLOps specialists as well. The book teaches how to build robust training loops and how to deploy scalable ML systems. The book also teaches how to design MLOps life cycle to ensure that the models are unbiased, fair, and explainable.

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AWS Machine Learning Blog

JANUARY 31, 2025

Increasingly, FMs are completing tasks that were previously solved by supervised learning, which is a subset of machine learning (ML) that involves training algorithms using a labeled dataset. About the Authors Jordan Knight is a Senior Data Scientist working for Travelers in the Business Insurance Analytics & Research Department.

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Prompt Engineering Prompt Engineer Data Scientist Large Language Models 77

ODSC - Open Data Science

MARCH 10, 2025

Training Sessions Bayesian Analysis of Survey Data: Practical Modeling withPyMC Allen Downey, PhD, Principal Data Scientist at PyMCLabs Alexander Fengler, Postdoctoral Researcher at Brown University Bayesian methods offer a flexible and powerful approach to regression modeling, and PyMC is the go-to library for Bayesian inference in Python.

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IBM Journey to AI blog

AUGUST 11, 2023

Both computer scientists and business leaders have taken note of the potential of the data. Machine learning (ML), a subset of artificial intelligence (AI), is an important piece of data-driven innovation. MLOps is the next evolution of data analysis and deep learning. What is MLOps?

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AWS Machine Learning Blog

MARCH 21, 2025

In addition to Anthropics Claude on Amazon Bedrock, the solution uses the following services: Amazon SageMaker JupyterLab The SageMakerJupyterLab application is a web-based interactive development environment (IDE) for notebooks, code, and data. He is passionate about applying cloud technologies and ML to solve real life problems.

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Metadata Convolutional Neural Networks Generative AI Data Scientist 109

Artificial Corner

AUGUST 3, 2023

After the launch of ChatGPT in late 2022, many companies have started to implement their own AI and ML technologies in their platforms. AI-first companies are those who in their every product and workflow implement AI and ML. In ML, Uber focuses on two domains i.e., operations and research. Such is the case for Uber.

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ML Machine Learning Data Scientist Deep Learning 98

Unite.AI

NOVEMBER 6, 2023

Moreover, Multimodal AI techniques have emerged, capable of processing multiple data modalities, i.e., text, images, audio, and videos simultaneously. With these advancements, it’s natural to wonder: Are we approaching the end of traditional machine learning (ML)? What is Traditional Machine Learning?

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Towards AI

AUGUST 29, 2024

I explore the differences between RAG and sending all data in the input and explain why we believe RAG will remain relevant for the foreseeable future. by Sachin Khandewal This blog explains different ways to query SQL Databases using Groq to access the LLMs. So, is RAG dead? Read the complete issue here!

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AWS Machine Learning Blog

NOVEMBER 14, 2024

We recently announced the general availability of cross-account sharing of Amazon SageMaker Model Registry using AWS Resource Access Manager (AWS RAM) , making it easier to securely share and discover machine learning (ML) models across your AWS accounts. Mitigation strategies : Implementing measures to minimize or eliminate risks.

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ODSC - Open Data Science

MARCH 14, 2025

Sequence modeling uses sequentially ordered training data to teach models to predict the next element in a series, taking previous elements context and dependencies into account. It is vital in the machine learning (ML) field, inspiring numerous architectures. However, it has left data scientists without a unified framework.

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Unite.AI

JULY 3, 2024

Can you explain the vision behind TileDB and how it aims to revolutionize the modern database landscape? The problem is that complex data that is not naturally represented as tables is considered as “unstructured,” and is typically either stored as flat files in bespoke data formats, or managed by disparate, purpose-built databases.

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AWS Machine Learning Blog

APRIL 26, 2023

Do you need help to move your organization’s Machine Learning (ML) journey from pilot to production? Most executives think ML can apply to any business decision, but on average only half of the ML projects make it to production. Challenges Customers may face several challenges when implementing machine learning (ML) solutions.

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AWS Machine Learning Blog

JULY 6, 2023

Many organizations choose SageMaker as their ML platform because it provides a common set of tools for developers and data scientists. Most of the options explained are also applicable if SageMaker is running in the SaaS AWS account. In some cases, an ISV may deploy their software in the customer AWS account.

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ML Data Scientist Metadata ETL 87

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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Machine Learning DevOps Data Scientist Data Quality 129

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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DevOps Software Engineer Data Scientist ML 145

Marktechpost

MAY 30, 2024

Introduction to AI and Machine Learning on Google Cloud This course introduces Google Cloud’s AI and ML offerings for predictive and generative projects, covering technologies, products, and tools across the data-to-AI lifecycle. It includes labs on feature engineering with BigQuery ML, Keras, and TensorFlow.

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Marktechpost

APRIL 29, 2024

IBM Data Science Professional Certificate This course helps master the practical skills and knowledge necessary for a proficient data scientist. It is a beginner-friendly course that teaches the tools, languages, and libraries data scientists use, such as Python and SQL.

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Data Science Data Scientist Python Machine Learning 124

O'Reilly Media

JANUARY 17, 2023

This mindset has followed me into my work in ML/AI. Because if companies use code to automate business rules, they use ML/AI to automate decisions. Given that, what would you say is the job of a data scientist (or ML engineer, or any other such title)? But first, let’s talk about the typical ML workflow.

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AWS Machine Learning Blog

SEPTEMBER 23, 2024

Machine learning (ML) projects are inherently complex, involving multiple intricate steps—from data collection and preprocessing to model building, deployment, and maintenance. To start our ML project predicting the probability of readmission for diabetes patients, you need to download the Diabetes 130-US hospitals dataset.

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ML Computer Vision Data Scientist Machine Learning 78

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. AI platform tools enable knowledge workers to analyze data, formulate predictions and execute tasks with greater speed and precision than they can manually.

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AWS Machine Learning Blog

SEPTEMBER 19, 2023

Amazon SageMaker Feature Store provides an end-to-end solution to automate feature engineering for machine learning (ML). For many ML use cases, raw data like log files, sensor readings, or transaction records need to be transformed into meaningful features that are optimized for model training. SageMaker Studio set up.

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ML Data Ingestion Python Machine Learning 110

ODSC - Open Data Science

APRIL 2, 2025

Engineers who can visualize data, explain outputs, and align their work with business objectives are consistently more valuable to theirteams. Lets not forget data wrangling. Roles like Data Scientist, ML Engineer, and the emerging LLM Engineer are in high demand.

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Prompt Engineer Prompt Engineering Large Language Models ML Engineer 52

AWS Machine Learning Blog

JANUARY 25, 2023

Model explainability refers to the process of relating the prediction of a machine learning (ML) model to the input feature values of an instance in humanly understandable terms. This field is often referred to as explainable artificial intelligence (XAI). In this post, we illustrate the use of Clarify for explaining NLP models.

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DataRobot Blog

MARCH 16, 2023

Traditionally, developing appropriate data science code and interpreting the results to solve a use-case is manually done by data scientists. Data scientists still need to review and evaluate these results. Ultimately, users benefit from a transparent, and clear explanation of what ML predictions means to them.

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Data Science OpenAI Data Scientist Large Language Models 98

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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Machine Learning Metadata Data Scientist Data Quality 134