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.

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Automation DevOps Machine Learning ML 106

Viso.ai

FEBRUARY 12, 2024

True to its name, Explainable AI refers to the tools and methods that explain AI systems and how they arrive at a certain output. In this blog, we’ll dive into the need for AI explainability, the various methods available currently, and their applications. Why do we need Explainable AI (XAI)?

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Explainable AI Explainability Deep Learning Neural Network 52

IBM Journey to AI blog

AUGUST 11, 2023

How to use ML to automate the refining process into a cyclical ML process. Initiate updates and optimization—Here, ML engineers will begin “retraining” the ML model method by updating how the decision process comes to the final decision, aiming to get closer to the ideal outcome.

Data Science 170

Data Science DevOps Machine Learning Data Scientist 170

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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Machine Learning ML Engineer DevOps ML 132

AWS Machine Learning Blog

JULY 8, 2024

TWCo data scientists and ML engineers took advantage of automation, detailed experiment tracking, integrated training, and deployment pipelines to help scale MLOps effectively. ML model experimentation is one of the sub-components of the MLOps architecture. We encourage to you to get started with Amazon SageMaker today.

Data Scientist 121

Data Scientist ML Engineer Machine Learning Data Science 121

Towards AI

JANUARY 27, 2024

This page aims to explain how to solve a multilabel classification problem with minimal code focusing on a familiar CIFAR-10 image dataset. Time Series Forecasting using PyCaret This page explains how to do forecasting using Python’s low-code AutoML library PyCaret.

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Deep Learning Explainability ML Engineer Data Scientist 111

AWS Machine Learning Blog

FEBRUARY 26, 2024

Although there are many potential metrics that you can use to monitor LLM performance, we explain some of the broadest ones in this post. This could be an actual classifier that can explain why the model refused the request. Rushabh Lokhande is a Senior Data & ML Engineer with AWS Professional Services Analytics Practice.

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Large Language Models LLM Big Data Machine Learning 139

ODSC - Open Data Science

FEBRUARY 13, 2025

She explained how to integrate structured (SQL, CSV) and unstructured data (documents, Slack messages) into Neo4js graph database to create a more context-aware AI system. The workshop underscored the value of knowledge graphs in improving AI explainability and retrieval precision.

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Large Language Models AI AI Automation 52

AWS Machine Learning Blog

OCTOBER 20, 2023

An ML engineer deploys the model pipeline into the ML team test environment using a shared services CI/CD process. After stakeholder validation, the ML model is deployed to the team’s production environment. ML operations This module helps LOBs and ML engineers work on their dev instances of the model deployment template.

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ML Data Scientist ML Engineer Data Science 133

AWS Machine Learning Blog

FEBRUARY 27, 2024

However, there are many clear benefits of modernizing our ML platform and moving to Amazon SageMaker Studio and Amazon SageMaker Pipelines. Model explainability Model explainability is a pivotal part of ML deployments, because it ensures transparency in predictions.

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Machine Learning DevOps Data Scientist Data Quality 137

ODSC - Open Data Science

DECEMBER 7, 2023

The Importance of Implementing Explainable AI in Healthcare Explainable AI might be the solution everyone needs to develop a healthier, more trusting relationship with technology while expediting essential medical care in a highly demanding world.

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Data Scientist Explainable AI Data Science Python 98

Towards AI

FEBRUARY 20, 2025

But who exactly is an LLM developer, and how are they different from software developers and ML engineers? It begins by explaining vector transformations, a core idea in neural networks, and contrasts traditional methods like SVMs with learned feature mappings in Transformers.

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Neural Network Explainability LLM Software Development 79

ODSC - Open Data Science

MARCH 27, 2023

ML Governance: A Lean Approach Ryan Dawson | Principal Data Engineer | Thoughtworks Meissane Chami | Senior ML Engineer | Thoughtworks During this session, you’ll discuss the day-to-day realities of ML Governance. Some of the questions you’ll explore include How much documentation is appropriate?

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Machine Learning Data Science Data Ingestion Deep Learning 98

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. All you need to master computer vision and deep learning is for someone to explain things to you in simple, intuitive terms.

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Machine Learning Computer Vision Deep Learning Python 152

Towards AI

MAY 30, 2024

It starts from explaining what an LLM is in simpler terms, and takes you through a brief history of time in NLP to the most current state of technology in AI. This book provides practical insights and real-world applications of, inter alia, RAG systems and prompt engineering. Seriously, pick it up.” Ahmed Moubtahij, ing.,

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Prompt Engineering Prompt Engineer LLM NLP 108

AWS Machine Learning Blog

MARCH 13, 2024

The LLM analysis provides a violation result (Y or N) and explains the rationale behind the model’s decision regarding policy violation. The Anthropic Claude V2 model delivers responses in the instructed format (Y or N), along with an analysis explaining why it thinks the message violates the policy.

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Natural Language Processing LLM Prompt Engineering Prompt Engineer 136

AWS Machine Learning Blog

JANUARY 6, 2023

They go quite a few steps beyond AI/ML experimentation: to achieve deployment anywhere, performance at scale, cost optimization, and increasingly important, support systematic model risk management—explainability, robustness, drift, privacy protection, and more. Vendor Requirements for the IDC MarketScape.

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Data Scientist ML Machine Learning Explainability 96

AWS Machine Learning Blog

FEBRUARY 21, 2025

The concept of a compound AI system enables data scientists and ML engineers to design sophisticated generative AI systems consisting of multiple models and components. Clone the GitHub repository and follow the steps explained in the README. These components can include multiple calls to models, retrievers, or external tools.

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LLM AI AI Data Scientist 95

Chatbots Life

MAY 12, 2023

As everything is explained from scratch but extensively I hope you will find it interesting whether you are NLP Expert or just want to know what all the fuss is about. We will discuss how models such as ChatGPT will affect the work of software engineers and ML engineers. and we will also explain how GPT can create jobs.

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ChatGPT Software Engineer NLP ML Engineer 90

AWS Machine Learning Blog

MARCH 14, 2024

Yes, Adam Hadwin made a hole-in-one on hole 14 during round 3 of the 2022 Shriners Children’s Open The following explainer video highlights a few examples of interacting with the virtual assistant. Grace Lang is an Associate Data & ML engineer with AWS Professional Services.

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AWS Machine Learning Blog

FEBRUARY 28, 2024

About the Authors Sanjeeb Panda is a Data and ML engineer at Amazon. With the background in AI/ML, Data Science and Big Data, Sanjeeb design and develop innovative data and ML solutions that solve complex technical challenges and achieve strategic goals for global 3P sellers managing their businesses on Amazon.

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Metadata Generative AI LLM NLP 141

AWS Machine Learning Blog

JUNE 27, 2023

They needed a cloud platform and a strategic partner with proven expertise in delivering production-ready AI/ML solutions, to quickly bring EarthSnap to the market. We initiated a series of enhancements to deliver managed MLOps platform and augment ML engineering. Endpoints had to be deployed manually as well.

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DevOps ML Machine Learning ML Engineer 115

ODSC - Open Data Science

FEBRUARY 24, 2023

Apache Superset GitHub | Website Apache Superset is a must-try project for any ML engineer, data scientist, or data analyst. Its goal is to help with a quick analysis of target characteristics, training vs testing data, and other such data characterization tasks.

Data Analysis 126

Data Analysis Data Science Business Intelligence Python 126

Towards AI

JULY 19, 2023

What can you recommend to him as an ML Engineer? A better search engine for his site. We can also keep giving hundreds of other suggestions to him, both belonging to ML and not belonging to ML. Explain to him how this is the most profitable project using different metrics.

ML 98

ML Machine Learning ML Engineer Explainability 98

Towards AI

FEBRUARY 13, 2023

What can you recommend to him as an ML Engineer? A better search engine for his site. We can also keep giving hundreds of other suggestions to him, both belonging to ML and not belonging to ML. Explain to him how this is the most profitable project using different metrics.

ML 97

ML Machine Learning ML Engineer Explainability 97

ODSC - Open Data Science

FEBRUARY 25, 2025

Topics Include: Agentic AI DesignPatterns LLMs & RAG forAgents Agent Architectures &Chaining Evaluating AI Agent Performance Building with LangChain and LlamaIndex Real-World Applications of Autonomous Agents Who Should Attend: Data Scientists, Developers, AI Architects, and ML Engineers seeking to build cutting-edge autonomous systems.

Data Scientist 52

Data Scientist Machine Learning Large Language Models ML Engineer 52

AWS Machine Learning Blog

OCTOBER 2, 2024

Model transparency – Although achieving full transparency in generative AI models remains challenging, organizations can take several steps to enhance model transparency and explainability: Provide model cards on the model’s intended use, performance, capabilities, and potential biases.

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AWS Machine Learning Blog

FEBRUARY 7, 2025

This post, part of the Governing the ML lifecycle at scale series ( Part 1 , Part 2 , Part 3 ), explains how to set up and govern a multi-account ML platform that addresses these challenges. ML engineers Develop model deployment pipelines and control the model deployment processes.

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ML Data Scientist ML Engineer Data Science 63

AWS Machine Learning Blog

MARCH 22, 2023

Deploy the workflow To deploy the workflow, clone the GitHub repository : git clone [link] cd rekognition-customlabels-automation-with-stepfunctions sam build sam deploy --guided These commands build, package and deploy your application to AWS, with a series of prompts as explained in the repository. The code for the workflow is open-sourced.

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Automation Machine Learning Computer Vision ML 98

The MLOps Blog

JUNE 27, 2023

Model governance and compliance : They should address model governance and compliance requirements, so you can implement ethical considerations, privacy safeguards, and regulatory compliance into your ML solutions. This includes features for model explainability, fairness assessment, privacy preservation, and compliance tracking.

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Machine Learning Metadata Data Scientist Data Quality 134

AWS Machine Learning Blog

JULY 24, 2024

It can also be done at scale, as explained in Operationalize LLM Evaluation at Scale using Amazon SageMaker Clarify and MLOps services. Fine-tuning an LLM can be a complex workflow for data scientists and machine learning (ML) engineers to operationalize. You can then select the best model based on the evaluation results.

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LLM ML Generative AI Machine Learning 129

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.

Automation 139

Automation Robotics Data Scientist ML 139

The MLOps Blog

NOVEMBER 14, 2023

Visualizing deep learning models can help us with several different objectives: Interpretability and explainability: The performance of deep learning models is, at times, staggering, even for seasoned data scientists and ML engineers. Data scientists and ML engineers: Creating and training deep learning models is no easy feat.

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Deep Learning Neural Network Convolutional Neural Networks Data Scientist 111

Pickl AI

NOVEMBER 28, 2024

Fundamental Programming Skills Strong programming skills are essential for success in ML. This section will highlight the critical programming languages and concepts ML engineers should master, including Python, R , and C++, and an understanding of data structures and algorithms. million by 2030, with a remarkable CAGR of 44.8%

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Machine Learning Neural Network ML Engineer Algorithm 52

AWS Machine Learning Blog

APRIL 26, 2023

The first is by using low-code or no-code ML services such as Amazon SageMaker Canvas , Amazon SageMaker Data Wrangler , Amazon SageMaker Autopilot , and Amazon SageMaker JumpStart to help data analysts prepare data, build models, and generate predictions. Adoption of AWS ML services such as SageMaker reduces these issues.

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ML Machine Learning Data Science Data Drift 115

Towards AI

SEPTEMBER 22, 2023

Some people foresaw the emergence of prompt engineer as a new title. Is this the future of the ML engineer? Let’s think about why prompt engineering has been developed. I hope you will find the discussion inspiring; however, please note that these are my personal views, and I welcome additional perspectives.

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Prompt Engineer Prompt Engineering ML ML Engineer 59

Heartbeat

FEBRUARY 1, 2023

Machine Learning Operations (MLOps) are the aspects of ML that deal with the creation and advancement of these models. In this article, we’ll learn everything there is to know about these operations and how ML engineers go about performing them. What is MLOps? We pay our contributors, and we don’t sell ads.

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DevOps Machine Learning Data Scientist Software Engineer 96