AWS Machine Learning Blog

FEBRUARY 27, 2024

Axfood has a structure with multiple decentralized data science teams with different areas of responsibility. Together with a central data platform team, the data science teams bring innovation and digital transformation through AI and ML solutions to the organization.

Machine Learning 126

Machine Learning DevOps Data Scientist Data Quality 126

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. during the forecast period.

Machine Learning 52

Machine Learning Neural Network ML Engineer Algorithm 52

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 Explainability Deep Learning 98

ODSC - Open Data Science

FEBRUARY 22, 2024

Andre Franca | CTO | connectedFlow Join this session to demystify the world of Causal AI, with a focus on understanding cause-and-effect relationships within data to drive optimal decisions. By the end of this session, you’ll have a practical blueprint to efficiently harness feature stores within ML workflows.

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ODSC - Open Data Science

FEBRUARY 24, 2023

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

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Data Analysis Data Science Business Intelligence Python 126

AWS Machine Learning Blog

OCTOBER 20, 2023

Data scientists search and pull features from the central feature store catalog, build models through experiments, and select the best model for promotion. Data scientists create and share new features into the central feature store catalog for reuse.

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ML Data Scientist ML Engineer Data Science 116

Towards AI

MAY 30, 2024

Ken Jee, Head of Data Science and Podcast host (Ken’s Nearest Neighbors, Exponential Athlete) “For whoever interested in getting started with LLMs and all that comes with it, this is the book for you. This book provides practical insights and real-world applications of, inter alia, RAG systems and prompt engineering.

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Prompt Engineer Prompt Engineering LLM AI 78

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. A popular focus of a majority of Data Science courses, degrees, and online competitions is on creating a model that has the highest accuracy or best fit.

Data Scientist 52

Data Scientist Data Science ML Machine Learning 52

ODSC - Open Data Science

JANUARY 10, 2024

Causal AI: from Data to Action Dr. Andre Franca | CTO | connectedFlow Explore the world of Causal AI for data science practitioners, with a focus on understanding cause-and-effect relationships within data to drive optimal decisions. Register for ODSC East today to save 60% on any pass.

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Large Language Models LLM Deep Learning Data Science 52

Heartbeat

OCTOBER 23, 2023

Revolutionizing Healthcare through Data Science and Machine Learning Image by Cai Fang on Unsplash Introduction In the digital transformation era, healthcare is experiencing a paradigm shift driven by integrating data science, machine learning, and information technology.

Data Scientist 52

Data Scientist Machine Learning Big Data Algorithm 52

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

The MLOps Blog

OCTOBER 20, 2023

Jupyter notebooks have been one of the most controversial tools in the data science community. Nevertheless, many data scientists will agree that they can be really valuable – if used well. I’ll show you best practices for using Jupyter Notebooks for exploratory data analysis. Imports: Library imports and settings.

Data Scientist 52

Data Scientist Python Explainability ETL 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. Conduct exploratory analysis and data preparation.

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AWS Machine Learning Blog

FEBRUARY 28, 2024

medium instance with the Python 3 (Data Science) kernel. About the Authors Sanjeeb Panda is a Data and ML engineer at Amazon. Outside of his work as a Data and ML engineer at Amazon, Sanjeeb Panda is an avid foodie and music enthusiast. text_content=False, json_lines=False).load()

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AWS Machine Learning Blog

NOVEMBER 13, 2024

With the unification of SageMaker Model Cards and SageMaker Model Registry, architects, data scientists, ML engineers, or platform engineers (depending on the organization’s hierarchy) can now seamlessly register ML model versions early in the development lifecycle, including essential business details and technical metadata.

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Metadata ML Software Engineer Machine Learning 95

Heartbeat

OCTOBER 5, 2023

Illustration from the book —  Effective Data Science Infrastructure The Future of LLMOps As we look ahead, LLMOps promises exciting advancements in various areas: Privacy-Preserving and Federated Learning: LLMOps will focus on preserving privacy while training models on decentralized data.

Large Language Models 52

Large Language Models DevOps Prompt Engineering Prompt Engineer 52

DataRobot Blog

JANUARY 10, 2023

ML model builders spend a ton of time running multiple experiments in a data science notebook environment before moving the well-tested and robust models from those experiments to a secure, production-grade environment for general consumption. 42% of data scientists are solo practitioners or on teams of five or fewer people.

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Auto-classification Auto-complete Data Scientist Data Science 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. It is beneficial for organizing and managing experiments and analyzing and visualizing the results of those data science experiments.

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?

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DevOps Machine Learning Data Scientist Software Engineer 96

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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Generative AI Data Ingestion AI AI 111

Flipboard

MARCH 7, 2023

Because of this difference, there are some specifics of how you create and manage virtual environments in Studio notebooks , for example usage of Conda environments or persistence of ML development environments between kernel restarts. Check that the SageMaker image selected is a Conda-supported first-party kernel image such as “Data Science.”

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Python Data Scientist Data Science Automation 123

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.

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ML Data Scientist ML Engineer Machine Learning 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.

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ML Computer Vision Data Scientist Software Development 77

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?

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DevOps Metadata Software Engineer Data Scientist 59

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.

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Neural Network Software Engineer Artificial Intelligence Artificial Intelligence 52

O'Reilly Media

OCTOBER 19, 2021

Similarly, it would be pointless to pretend that a data-intensive application resembles a run-off-the-mill microservice which can be built with the usual software toolchain consisting of, say, GitHub, Docker, and Kubernetes. Adapted from the book Effective Data Science Infrastructure. Data Science Layers.

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DevOps Software Engineer Data Scientist ML 140

Snorkel AI

JUNE 9, 2023

Fireside Chat: Journey of Data: Transforming the Enterprise with Data-Centric Workflows In a lively back and forth, Alex talked with Nurtekin Savas, head of enterprise data science at Capital One , about broadening the scope of being “data-centric.”

Large Language Models 52

Large Language Models Data Scientist Machine Learning Computer Vision 52

Snorkel AI

JUNE 9, 2023

Fireside Chat: Journey of Data: Transforming the Enterprise with Data-Centric Workflows In a lively back and forth, Alex talked with Nurtekin Savas, head of enterprise data science at Capital One , about broadening the scope of being “data-centric.”

Large Language Models 52

Large Language Models Data Scientist Machine Learning Computer Vision 52

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. enable data scientists and ML engineers to track and plot gradients during training.

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Deep Learning Neural Network Convolutional Neural Networks Data Scientist 111

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.

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Computer Vision Auto-classification Deep Learning Machine Learning 52

AWS Machine Learning Blog

NOVEMBER 14, 2024

Transparency and explainability : Making sure that AI systems are transparent, explainable, and accountable. However, explaining why that decision was made requires next-level detailed reports from each affected model component of that AI system. Mitigation strategies : Implementing measures to minimize or eliminate risks.

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AWS Machine Learning Blog

APRIL 11, 2023

These customers need to balance governance, security, and compliance against the need for machine learning (ML) teams to quickly access their data science environments in a secure manner. We explain the process and network flow, and how to easily scale this architecture to multiple accounts and Amazon SageMaker domains.

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IDP Explainability Big Data DevOps 74

Heartbeat

OCTOBER 30, 2023

Once the best model is identified, it is usually deployed in production to make accurate predictions on real-world data (similar to the one on which the model was trained initially). Ideally, the responsibilities of the ML engineering team should be completed once the model is deployed. But this is only sometimes the case.

Machine Learning 52

Machine Learning Data Drift ML Data Scientist 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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The MLOps Blog

OCTOBER 3, 2023

So I was able to get from growth hacking to data analytics, then data analytics to data science, and then data science to MLOps. I switched from analytics to data science, then to machine learning, then to data engineering, then to MLOps. How do I get this model in production?

ML 52

ML Data Scientist Machine Learning Data Science 52

The MLOps Blog

MAY 9, 2023

One of the most prevalent complaints we hear from ML engineers in the community is how costly and error-prone it is to manually go through the ML workflow of building and deploying models. Building end-to-end machine learning pipelines lets ML engineers build once, rerun, and reuse many times. Kale v0.7.0.

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AWS Machine Learning Blog

APRIL 11, 2023

Amazon SageMaker Studio can help you build, train, debug, deploy, and monitor your models and manage your machine learning (ML) workflows. In this post, we explain how to run PySpark processing jobs within a pipeline. A SageMaker pipeline can use processing steps to process data or perform model evaluation.

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Data Scientist ML Python ML Engineer 80