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 Science Data Ingestion Deep Learning 98

Flipboard

OCTOBER 9, 2023

This lesson is the 2nd of a 3-part series on Docker for Machine Learning : Getting Started with Docker for Machine Learning Getting Used to Docker for Machine Learning (this tutorial) Lesson 3 To learn how to create a Docker Container for Machine Learning, just keep reading. That’s not the case.

Machine Learning 153

Machine Learning Computer Vision Deep Learning Python 153

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.

Machine Learning 134

Machine Learning Metadata Data Scientist Data Quality 134

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

JUNE 27, 2023

Earth.com’s leadership team recognized the vast potential of EarthSnap and set out to create an application that utilizes the latest deep learning (DL) architectures for computer vision (CV). That is where Provectus , an AWS Premier Consulting Partner with competencies in Machine Learning, Data & Analytics, and DevOps, stepped in.

DevOps 117

DevOps ML Machine Learning ML Engineer 117

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

Pickl AI

NOVEMBER 28, 2024

Continuous learning is essential to keep pace with advancements in Machine Learning technologies. Fundamental Programming Skills Strong programming skills are essential for success in ML. Python’s readability and extensive community support and resources make it an ideal choice for ML engineers.

Machine Learning 52

Machine Learning Neural Network ML Engineer Algorithm 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.

Generative AI 128

Generative AI Data Ingestion AI AI 128

Heartbeat

SEPTEMBER 6, 2023

There are machine learning platforms that can perform all these tasks, and Comet is one such platform. Comet Comet is a machine learning platform built to help data scientists and ML engineers track, compare, and optimize machine learning experiments. We will build our deep learning model using those parameters.

Machine Learning 52

Machine Learning Deep Learning Auto-complete Data Scientist 52

AWS Machine Learning Blog

OCTOBER 19, 2023

Customers increasingly want to use deep learning approaches such as large language models (LLMs) to automate the extraction of data and insights. For many industries, data that is useful for machine learning (ML) may contain personally identifiable information (PII).

Machine Learning 119

Machine Learning Python ML Automation 119

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

The MLOps Blog

MAY 10, 2023

In this section, you will see different ways of saving machine learning (ML) as well as deep learning (DL) models. Note: The focus of this article is not to show you how you can create the best ML model but to explain how effectively you can save trained models. values y = dataset.iloc[:, 4 ].values

Python 105

Python Metadata ML Machine Learning 105

O'Reilly Media

OCTOBER 19, 2021

Not only is data larger, but models—deep learning models in particular—are much larger than before. Modern ML applications need to be carefully orchestrated: with the dramatic increase in the complexity of apps, which can require dozens of interconnected steps, developers need better software paradigms, such as first-class DAGs.

DevOps 144

DevOps Software Engineer Data Scientist ML 144

Heartbeat

OCTOBER 23, 2023

We will examine real-life applications where health informatics has outperformed traditional methods, discuss recent advances in the field, and highlight machine learning tools such as time series analysis with ARIMA and ARTXP that are transforming health informatics. 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

SEPTEMBER 23, 2024

Throughout this exercise, you use Amazon Q Developer in SageMaker Studio for various stages of the development lifecycle and experience firsthand how this natural language assistant can help even the most experienced data scientists or ML engineers streamline the development process and accelerate time-to-value.

ML 90

ML Computer Vision Data Scientist Machine Learning 90

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. In the industry, deep learning is not always the preferred approach.

Data Scientist 52

Data Scientist Data Science ML Machine Learning 52

AWS Machine Learning Blog

OCTOBER 15, 2024

In this post, we discuss Bria’s family of models, explain the Amazon SageMaker platform, and walk through how to discover, deploy, and run inference on a Bria 2.3 About the Authors Bar Fingerman is the Head of AI/ML Engineering at Bria. model using SageMaker JumpStart. Overview of Bria 2.3, HD, and Bria 2.3

Generative AI 86

Generative AI ML Python Responsible AI 86

Pickl AI

JULY 14, 2023

Big Data and Deep Learning (2010s-2020s): The availability of massive amounts of data and increased computational power led to the rise of Big Data analytics. Deep Learning, a subfield of ML, gained attention with the development of deep neural networks.

Neural Network 52

Neural Network Software Engineer Artificial Intelligence Artificial Intelligence 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. 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 Data Science 52

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

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

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 Machine Learning Prompt Engineering 52

Heartbeat

JULY 17, 2023

Comet allows ML engineers to track these metrics in real-time and visualize their performance using interactive dashboards. Editor’s Note: Heartbeat is a contributor-driven online publication and community dedicated to providing premier educational resources for data science, machine learning, and deep learning practitioners.

Computer Vision 52

Computer Vision Auto-classification Deep Learning Machine Learning 52

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

Mlearning.ai

AUGUST 18, 2023

TL;DR This series explain how to implement intermediate MLOps with simple python code, without introducing MLOps frameworks (MLflow, DVC …). As an ML engineer you’re in charge of some code/model. If you rather jump straight to the code, here’s the repository [link]. Replace MLOps with program .Source can be slim and focused.

DevOps 52

DevOps Python ML ML Engineer 52

Viso.ai

FEBRUARY 11, 2024

Detectron2 is a deep learning model built on the Pytorch framework, which is said to be one of the most promising modular object detection libraries being pioneered. Tutorials and explainers can also be helpful. As for additional data set options, sources include Cityscapes, LVIS, and PASCAL VOC.

Computer Vision 52

Computer Vision Robotics Deep Learning Large Language Models 52

The MLOps Blog

OCTOBER 3, 2023

What helped me both in the transition to the data scientist role and then also to the MLOps engineer role was doing a combination of boot camps, and when I was going to the MLOps engineer role, I also took this one workshop that’s pretty well-known called Full Stack Deep Learning. I really enjoyed it. We offer that.

ML 52

ML Data Scientist Machine Learning Data Science 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 135

ML Explainability Machine Learning Deep Learning 135

The MLOps Blog

AUGUST 11, 2023

At that point, the Data Scientists or ML Engineers become curious and start looking for such implementations. Many questions regarding building machine learning pipelines and systems have already been answered and come from industry best practices and patterns. Model parallelism What is model parallelism?

ML 52

ML Machine Learning Data Ingestion Deep Learning 52

AWS Machine Learning Blog

DECEMBER 2, 2024

These improvements are available across a wide range of SageMaker’s Deep Learning Containers (DLCs), including Large Model Inference (LMI, powered by vLLM and multiple other frameworks), Hugging Face Text Generation Inference (TGI), PyTorch (Powered by TorchServe), and NVIDIA Triton. gpu-py311-cu124-ubuntu22.04-v2.0",

Generative AI 107

Generative AI Machine Learning Large Language Models AI 107

The MLOps Blog

MARCH 21, 2023

As the number of ML-powered apps and services grows, it gets overwhelming for data scientists and ML engineers to build and deploy models at scale. In this comprehensive guide, we’ll explore everything you need to know about machine learning platforms, including: Components that make up an ML platform.

Machine Learning 69

Machine Learning Data Scientist ML Metadata 69

AWS Machine Learning Blog

OCTOBER 11, 2024

Generative AI solutions often use Retrieval Augmented Generation (RAG) architectures, which augment external knowledge sources for improving content quality, context understanding, creativity, domain-adaptability, personalization, transparency, and explainability. Ginni Malik is a Senior Data & ML Engineer with AWS Professional Services.

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Metadata Generative AI LLM Data Ingestion 106