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 153

Data Science DevOps Machine Learning Data Scientist 153

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.

Machine Learning 116

Machine Learning DevOps Data Quality Data Scientist 116

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?

Machine Learning 98

Machine Learning Data Ingestion ML Explainability 98

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. Conclusion Can’t wait to start learning from these incredible speakers and experts?

Machine Learning 52

Machine Learning Data Science Python ML 52

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.

Prompt Engineer 59

Prompt Engineer Prompt Engineering LLM ML 59

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 97

ML Machine Learning ML Engineer Explainability 97

Flipboard

NOVEMBER 28, 2023

Mayer is an Applied Scientist and ML educator at AWS Machine Learning University; where she researches and teaches safety, explainability and fairness of Machine Learning and AI systems. About the Authors Mia C. Denis is a frequent public speaker, you can follow him on Twitter @dbatalov.

Responsible AI 144

Responsible AI Artificial Intelligence Artificial Intelligence AI 144

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 96

ML Machine Learning ML Engineer Explainability 96

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

Snorkel AI

JUNE 30, 2023

And usually what ends up happening is that some poor data scientist or ML engineer has to manually troubleshoot this in a Jupyter Notebook. So this path on the right side of the production icon is what we’re calling ML observability. Then there’s data quality, and then explainability. The second is drift.

Machine Learning 52

Machine Learning ML Data Quality Data Drift 52

Snorkel AI

JUNE 30, 2023

And usually what ends up happening is that some poor data scientist or ML engineer has to manually troubleshoot this in a Jupyter Notebook. So this path on the right side of the production icon is what we’re calling ML observability. Then there’s data quality, and then explainability. The second is drift.

Machine Learning 52

Machine Learning ML Data Quality Data Drift 52

Heartbeat

SEPTEMBER 18, 2023

Unleashing Innovation and Success: Comet — The Trusted ML Platform for Enterprise Environments Machine learning (ML) is a rapidly developing field, and businesses are increasingly depending on ML platforms to fuel innovation, improve efficiency, and mine data for insights. We pay our contributors, and we don’t sell ads.

ML 52

ML Data Scientist ML Engineer Machine Learning 52

Mlearning.ai

FEBRUARY 9, 2023

In industrial applications of Data Science, model complexity, model explainability, efficiency, and ease of deployment play a large role, even if that means you’re settling for a slightly less accurate model. Model explainability is an important skill for a Data Scientist’s job. This is even more common for first-time baseline models.

Data Scientist 52

Data Scientist Data Science ML Machine Learning 52

Pickl AI

JANUARY 29, 2024

AI Engineer, Machine Learning Engineer, and Robotics Engineer are prominent roles in AI. ML Engineer, Data Scientist, and Research Scientist are typical roles in Machine Learning.

Data Science 52

Data Science ML Machine Learning Artificial Intelligence 52

Heartbeat

OCTOBER 5, 2023

Interpretability and Explainability: As LLMs become more powerful, the focus on understanding model decision-making processes will intensify. We're committed to supporting and inspiring developers and engineers from all walks of life. To minimize project lifecycle friction and bridge the gap between developers and operations teams.

Large Language Models 52

Large Language Models DevOps Prompt Engineer Prompt Engineering 52

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.

Automation 80

Automation Machine Learning ML Computer Vision 80

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.

Explainability 71

Explainability Natural Language Processing Algorithm Machine Learning 71

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. How do we figure out what is causal and what isn’t, with a brief introduction to methods of structure learning and causal discovery?

Large Language Models 52

Large Language Models LLM Deep Learning ML 52

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)?

Explainable AI 52

Explainable AI Explainability Deep Learning Neural Network 52

Pickl AI

JULY 14, 2023

Explainable AI and Ethical Considerations (2010s-present): As AI systems became more complex and influential, concerns about transparency, fairness, and accountability arose. Researchers began addressing the need for Explainable AI (XAI) to make AI systems more understandable and interpretable.

Neural Network 52

Neural Network Software Engineer Artificial Intelligence Artificial Intelligence 52

Heartbeat

SEPTEMBER 6, 2023

Comet Comet is a machine learning platform built to help data scientists and ML engineers track, compare, and optimize machine learning experiments. 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.

Machine Learning 52

Machine Learning Deep Learning Auto-complete Data Scientist 52

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.

DevOps 96

DevOps Machine Learning Data Scientist Software Engineer 96

Heartbeat

OCTOBER 30, 2023

Ideally, the responsibilities of the ML engineering team should be completed once the model is deployed. But, as a data scientist or an ML engineer, you focus on the solutions rather than problems, right? We're committed to supporting and inspiring developers and engineers from all walks of life.

Machine Learning 52

Machine Learning Data Drift ML Data Scientist 52

TheSequence

MAY 24, 2023

Could you explain the data curation and training process required for building such a model? Were there any research breakthroughs in StarCoder, or would you say it was more of a crafty ML engineering effort? StarChat (alpha) is even better at that since it was specifically fine-tuned on conversations and instructions.

Auto-complete 59

Auto-complete ML Python Machine Learning 59

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. Common metrics for classification tasks include accuracy, precision, recall, F1 score, and confusion matrix.

Computer Vision 52

Computer Vision Auto-classification Deep Learning Machine Learning 52

Snorkel AI

JUNE 9, 2023

Leveraging Data-centric AI for Document Intelligence and PDF Extraction Snorkel AI ML Engineer Ashwini Ramamoorthy highlighted the challenges of extracting entities from semi-structured documents.

Large Language Models 52

Large Language Models Data Scientist Machine Learning Computer Vision 52

Snorkel AI

JUNE 9, 2023

Leveraging Data-centric AI for Document Intelligence and PDF Extraction Snorkel AI ML Engineer Ashwini Ramamoorthy highlighted the challenges of extracting entities from semi-structured documents.

Large Language Models 52

Large Language Models Data Scientist Machine Learning Computer Vision 52

Viso.ai

FEBRUARY 11, 2024

Tutorials and explainers can also be helpful. Part of what’s exciting about Detectron2 is the still-emerging capability to use these segmentation tools on your data. FAIR provides some more resources for Detectron2 users and explanations of how to approach its use for relevant projects.

Computer Vision 52

Computer Vision Robotics Deep Learning Large Language Models 52

AWS Machine Learning Blog

FEBRUARY 12, 2024

Essential ML capabilities such as hyperparameter tuning and model explainability were lacking on premises. Finally, the team’s aspiration was to receive immediate feedback on each change made in the code, reducing the feedback loop from minutes to an instant, and thereby reducing the development cycle for ML models.

ML 113

ML Explainability Machine Learning Deep Learning 113

AWS Machine Learning Blog

JANUARY 5, 2024

Figure 11 – Model monitor dashboard with selection prompts Figure 12 – Model monitor drift analysis Conclusion The implementation explained in this post enabled Wipro to effectively migrate their on-premises models to AWS and build a scalable, automated model development framework.

Data Science 113

Data Science Data Drift DevOps Auto-complete 113

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.

Machine Learning 52

Machine Learning Categorization Data Analysis Neural Network 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.

ML 105

ML Data Scientist ML Engineer Data Science 105

AWS Machine Learning Blog

NOVEMBER 9, 2023

This collaboration ensures that your MLOps platform can adapt to evolving business needs and accelerates the adoption of ML across teams. Machine Learning Engineer with AWS Professional Services. She is passionate about developing, deploying, and explaining AI/ ML solutions across various domains.

Data Drift 102

Data Drift Auto-complete ML Automation 102

The MLOps Blog

JANUARY 23, 2023

Ok, let me explain. I believe the team will look something like this: Software delivery reliability: DevOps engineers and SREs ( DevOps vs SRE here ) ML-specific software: software engineers and data scientists Non-ML-specific software: software engineers Product: product people and subject matter experts Wait, where is the MLOps engineer?

DevOps 59

DevOps Metadata Software Engineer Data Scientist 59

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.

ChatGPT 105

ChatGPT Software Engineer NLP Prompt Engineer 105

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.

LLM 111

LLM Natural Language Processing Prompt Engineer Prompt Engineering 111

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.

Deep Learning 111

Deep Learning Neural Network Convolutional Neural Networks Data Scientist 111