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. General Outline for Scoping.

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ML Machine Learning ML Engineer Explainability 76

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.

Machine Learning 104

Machine Learning ML Engineer DevOps ML 104

IBM Journey to AI blog

AUGUST 11, 2023

Machine learning (ML), a subset of artificial intelligence (AI), is an important piece of data-driven innovation. Machine learning engineers take massive datasets and use statistical methods to create algorithms that are trained to find patterns and uncover key insights in data mining projects. What is MLOps?

Data Science 144

Data Science DevOps Machine Learning Data Scientist 144

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.

Machine Learning 112

Machine Learning DevOps Data Quality Data Scientist 112

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.

Deep Learning 93

Deep Learning Explainability ML Engineer Data Scientist 93

ODSC - Open Data Science

MARCH 27, 2023

Learn about the flow, difficulties, and tools for performing ML clustering at scale Ori Nakar | Principal Engineer, Threat Research | Imperva Given that there are billions of daily botnet attacks from millions of different IPs, the most difficult challenge of botnet detection is choosing the most relevant data. Why is it important?

Machine Learning 98

Machine Learning Data Ingestion Explainability ML 98

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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Explainable AI Data Scientist Data Science Python 98

Flipboard

NOVEMBER 28, 2023

While it might be easier to start looking at an individual machine learning (ML) model and the associated risks in isolation, it’s important to consider the details of the specific application of such a model and the corresponding use case as part of a complete AI system. In this post, we focus on AI system risk, primarily.

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Responsible AI Artificial Intelligence Artificial Intelligence AI 144

ODSC - Open Data Science

FEBRUARY 22, 2024

Key points of this talk are: In this talk, we will focus on: The dangers of using post-hoc explainability methods as tools for decision making, and how traditional ML isn’t suited in situations where want to perform interventions on the system. No-Code and Low-Code AI: A Practical Project Driven Approach to ML ​​Gwendolyn D.

Machine Learning 52

Machine Learning Data Science Python ML 52

Towards AI

SEPTEMBER 22, 2023

There were significant distinctions between academic researchers, ML practitioners, and their clients. ML consultants, who make a living by providing ML services may ask: How do we justify our profession in this scenario if the researchers are solving practical issues and the clients can use the cutting-edge AI tools on their own?

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Prompt Engineer Prompt Engineering LLM ML 57

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 Natural Language Processing 112

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 101

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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ML Explainability Machine Learning Deep Learning 110

Snorkel AI

JUNE 30, 2023

Jack Zhou, product manager at Arize , gave a lightning talk presentation entitled “How to Apply Machine Learning Observability to Your ML System” at Snorkel AI’s Future of Data-Centric AI virtual conference in August 2022. So ML ends up being a huge part of many large companies’ core functions. I’m a product manager at Arize.

Machine Learning 52

Machine Learning ML Data Quality Data Drift 52

Snorkel AI

JUNE 30, 2023

Jack Zhou, product manager at Arize , gave a lightning talk presentation entitled “How to Apply Machine Learning Observability to Your ML System” at Snorkel AI’s Future of Data-Centric AI virtual conference in August 2022. So ML ends up being a huge part of many large companies’ core functions. I’m a product manager at Arize.

Machine Learning 52

Machine Learning ML Data Quality Data Drift 52

ODSC - Open Data Science

JANUARY 10, 2024

Takeaways include: The dangers of using post-hoc explainability methods as tools for decision-making, and where traditional ML falls short. Participants will walk away with a solid grasp of feature stores, equipped with the knowledge to drive meaningful insights and enhancements in their real-world ML platforms and projects.

Large Language Models 52

Large Language Models LLM Deep Learning ML 52

Mlearning.ai

FEBRUARY 9, 2023

11 key differences in 2023 Photo by Jan Tinneberg on Unsplash Working in Data Science and Machine Learning (ML) professions can be a lot different from the expectation of it. I started working in Data Science right after graduating with an MS degree in Electrical and Computer Engineering from the University of California, Los Angeles (UCLA).

Data Scientist 52

Data Scientist Data Science ML Machine Learning 52

Heartbeat

FEBRUARY 1, 2023

Machine learning has become an essential part of our lives because we interact with various applications of ML models, whether consciously or unconsciously. Machine Learning Operations (MLOps) are the aspects of ML that deal with the creation and advancement of these models. What is MLOps?

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DevOps Machine Learning Data Scientist Software Engineer 96

Heartbeat

OCTOBER 5, 2023

Unsurprisingly, Machine Learning (ML) has seen remarkable progress, revolutionizing industries and how we interact with technology. Where is LLMOps in DevOps and MLOps In MLOps, engineers are dedicated to enhancing the efficiency and impact of ML model deployment. The focus shifts towards prompt engineering and fine-tuning.

Large Language Models 52

Large Language Models DevOps Prompt Engineer Prompt Engineering 52

Heartbeat

OCTOBER 23, 2023

The Role of Data Scientists and ML Engineers in Health Informatics At the heart of the Age of Health Informatics are data scientists and ML engineers who play a critical role in harnessing the power of data and developing intelligent algorithms. We pay our contributors, and we don't sell ads.

Data Scientist 52

Data Scientist Machine Learning Big Data Algorithm 52

AWS Machine Learning Blog

JANUARY 5, 2024

This post was written in collaboration with Bhajandeep Singh and Ajay Vishwakarma from Wipro’s AWS AI/ML Practice. 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 110

Data Science Data Drift DevOps Auto-complete 110

Heartbeat

OCTOBER 30, 2023

Ideally, the responsibilities of the ML engineering team should be completed once the model is deployed. Model Drift and Data Drift are two of the main reasons why the ML model's performance degrades over time. But, as a data scientist or an ML engineer, you focus on the solutions rather than problems, right?

Machine Learning 52

Machine Learning Data Drift ML Data Scientist 52

Heartbeat

SEPTEMBER 6, 2023

When working on an ML project, we must compare various machine learning models with different hyperparameters and want to understand which models and hyperparameters are most effective for our use case. Comet ML web interface Conclusion That's all from this article. We pay our contributors, and we don't sell ads.

Machine Learning 52

Machine Learning Deep Learning Auto-complete Data Scientist 52

AWS Machine Learning Blog

MARCH 22, 2023

Generating this data can take months to gather and require large teams of labelers to prepare it for use in machine learning (ML). Behind the scenes, Rekognition Custom Labels automatically loads and inspects the training data, selects the right ML algorithms, trains a model, and provides model performance metrics.

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Automation Machine Learning ML Computer Vision 80

Pickl AI

JULY 14, 2023

Machine Learning and Neural Networks (1990s-2000s): Machine Learning (ML) became a focal point, enabling systems to learn from data and improve performance without explicit programming. Deep Learning, a subfield of ML, gained attention with the development of deep neural networks. Artificial Intelligence and the Future of Humans 1.

Neural Network 52

Neural Network Software Engineer Artificial Intelligence Artificial Intelligence 52

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. How do I develop my body of work?

ML 52

ML Data Scientist Machine Learning Data Science 52

TheSequence

MAY 24, 2023

I originally did a master's degree in physics focusing on astrophysics, but around that time, I noticed the breakthroughs happening in ML so I decided to switch the focus of my studies towards ML. Could you explain the data curation and training process required for building such a model?

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Auto-complete ML Python Machine Learning 59

Snorkel AI

JUNE 9, 2023

Accelerate ML Adoption by Addressing Hidden Needs Max Williams, AI platform product manager at Wells Fargo , discussed the challenges of achieving a return on investment in machine learning as well as the hidden needs an organization must address for ML to gain widespread adoption and deliver attractive returns.

Large Language Models 52

Large Language Models Data Scientist Machine Learning Computer Vision 52

Snorkel AI

JUNE 9, 2023

Accelerate ML Adoption by Addressing Hidden Needs Max Williams, AI platform product manager at Wells Fargo , discussed the challenges of achieving a return on investment in machine learning as well as the hidden needs an organization must address for ML to gain widespread adoption and deliver attractive returns.

Large Language Models 52

Large Language Models Data Scientist Machine Learning Computer Vision 52

Heartbeat

JULY 17, 2023

Comet allows ML engineers to track these metrics in real-time and visualize their performance using interactive dashboards. We’re committed to supporting and inspiring developers and engineers from all walks of life. We pay our contributors, and we don’t sell ads.

Computer Vision 52

Computer Vision Auto-classification Deep Learning Machine Learning 52

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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Data Drift Auto-complete ML Automation 99

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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ML Machine Learning Data Science Data Drift 88

The MLOps Blog

MARCH 15, 2023

This includes the tools and techniques we used to streamline the ML model development and deployment processes, as well as the measures taken to monitor and maintain models in a production environment. Costs: Oftentimes, cost is the most important aspect of any ML model deployment. I would say the same happened in our case.

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ETL Data Drift Machine Learning ML 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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Explainability Natural Language Processing Algorithm Machine Learning 70

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

The MLOps Blog

MARCH 14, 2023

When working on real-world ML projects , you come face-to-face with a series of obstacles. The ml model reproducibility problem is one of them. This is indeed an erroneous thing to do when working on ML projects at scale. Reproducibility is the key factor when it comes to stabilizing the outcomes of any ML pipeline.

ML 52

ML Machine Learning Metadata ML Engineer 52

Mlearning.ai

APRIL 17, 2023

image by author Introduction Error analysis is a vital process in diagnosing errors made by an ML model during its training and testing steps. It enables data scientists or ML engineers to evaluate their models’ performance and identify areas for improvement. Besides the size, it is significant to discuss the model metrics.

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Machine Learning Categorization Data Analysis Neural Network 52