ODSC - Open Data Science

FEBRUARY 9, 2023

It is often too much to ask for the data scientist to become a domain expert. However, in all cases the data scientist must develop strong domain empathy to help define and solve the right problems. Nina Zumel and John Mount, Practical Data Science with R, 2nd Ed.

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Data Scientist Machine Learning ML Engineer ML 105

Unite.AI

JUNE 19, 2024

Master's Degree : Pursuing a Master's degree in Computer Science, Data Science, or a related field can further enhance your knowledge and skills, particularly in areas like ML, AI, and advanced software engineering concepts. Krish Naik : Focuses on machine learning, data science, and MLOps.

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Software Engineer DevOps Machine Learning ML 182

ODSC - Open Data Science

SEPTEMBER 21, 2023

Secondly, to be a successful ML engineer in the real world, you cannot just understand the technology; you must understand the business. After all, this is what machine learning really is; a series of algorithms rooted in mathematics that can iterate some internal parameters based on data.

Machine Learning 98

Machine Learning ML Engineer ML Data Science 98

Towards AI

AUGUST 25, 2023

Machine learning (ML) is a subset of AI that provides computer systems the ability to automatically learn and improve from experience without being explicitly programmed. In ML, there are a variety of algorithms that can help solve problems. Any competent software engineer can implement any algorithm. 12, 2021. [6]

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Software Engineer Machine Learning Neural Network Artificial Intelligence 90

Flipboard

NOVEMBER 24, 2023

With that, the need for data scientists and machine learning (ML) engineers has grown significantly. Data scientists and ML engineers require capable tooling and sufficient compute for their work. Data scientists and ML engineers require capable tooling and sufficient compute for their work.

ML 153

ML DevOps Data Scientist ML Engineer 153

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

JANUARY 10, 2024

Learn more about our first-announced sessions coming to the event this April 23rd-25th below. 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.

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Large Language Models LLM Deep Learning Data Science 52

ODSC - Open Data Science

FEBRUARY 22, 2024

By the end of this session, you’ll have a practical blueprint to efficiently harness feature stores within ML workflows. Using Graphs for Large Feature Engineering Pipelines Wes Madrigal | ML Engineer | Mad Consulting Feature engineering from raw entity-level data is complex, but there are ways to reduce that complexity.

Machine Learning 52

Machine Learning Data Science Python Data Ingestion 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.

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Data Scientist Machine Learning Big Data Algorithm 52

ODSC - Open Data Science

JULY 19, 2023

Machine Learning Operations (MLOps) can significantly accelerate how data scientists and ML engineers meet organizational needs. A well-implemented MLOps process not only expedites the transition from testing to production but also offers ownership, lineage, and historical data about ML artifacts used within the team.

Machine Learning 52

Machine Learning Data Drift Data Science Data Scientist 52

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. Thanks for reading!

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Machine Learning Deep Learning Auto-complete Data Scientist 52

Dlabs.ai

OCTOBER 21, 2024

#InsideAI Frequency: Monthly Best for: Business leaders, AI professionals, entrepreneurs, and those interested in practical AI applications Content: Comprehensive coverage of AI, machine learning, and data science developments This newsletter provided by DLabs.AI delivers the most important news to you every month.

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AI AI Deep Learning Machine Learning 52

AWS Machine Learning Blog

NOVEMBER 16, 2023

Amazon SageMaker provides purpose-built tools for machine learning operations (MLOps) to help automate and standardize processes across the ML lifecycle. In this post, we describe how Philips partnered with AWS to develop AI ToolSuite—a scalable, secure, and compliant ML platform on SageMaker.

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Data Scientist ML Data Science Automation 104

Pickl AI

MAY 30, 2023

For instance, Data Science Course with Placement Guarantee is one of the most in-demand courses that aspirants are looking for. Instances of Professionals courses include Data Science Bootcamp Job Guarantee, Python for Data Science, Data Analytics, Business Analytics, etc. Lakhs annually.

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Data Mining Data Science Deep Learning Machine Learning 52

The MLOps Blog

JUNE 27, 2023

With built-in components and integration with Google Cloud services, Vertex AI simplifies the end-to-end machine learning process, making it easier for data science teams to build and deploy models at scale. Metaflow Metaflow helps data scientists and machine learning engineers build, manage, and deploy data science projects.

Machine Learning 134

Machine Learning Metadata Data Scientist Data Quality 134

Towards AI

AUGUST 24, 2023

Since this is an AI website, I will assume that most readers will have the following goals: You are interested in becoming an AI/ML engineer. You are interested in learning software engineering best practices [1][2]. Thus, deep learning models should be your last choice. Know when not to use AI.

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Neural Network Software Engineer Algorithm AI Engineer 84

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. In the industry, deep learning is not always the preferred approach. This is even more common for first-time baseline models.

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Data Scientist Data Science ML Machine Learning 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.

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DevOps Machine Learning Data Scientist Software Engineer 96

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.

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Large Language Models DevOps Prompt Engineering Prompt Engineer 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.

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ML Data Scientist ML Engineer Machine Learning 52

AWS Machine Learning Blog

SEPTEMBER 20, 2023

SageMaker Studio allows data scientists, ML engineers, and data engineers to prepare data, build, train, and deploy ML models on one web interface. The code snippets in the following sections have been tested in the SageMaker Studio notebook environment using the Data Science 3.0

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ML Python Deep Learning Machine Learning 115

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.

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Neural Network Software Engineer Artificial Intelligence Artificial Intelligence 52

DataRobot Blog

SEPTEMBER 13, 2022

Once an organization has identified its AI use cases , data scientists informally explore methodologies and solutions relevant to the business’s needs in the hunt for proofs of concept. These might include—but are not limited to—deep learning, image recognition and natural language processing.

Data Scientist 52

Data Scientist Automation Data Ingestion ML 52

AWS Machine Learning Blog

SEPTEMBER 1, 2023

These data owners are focused on providing access to their data to multiple business units or teams. Data science team – Data scientists need to focus on creating the best model based on predefined key performance indicators (KPIs) working in notebooks.

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Generative AI Prompt Engineering Prompt Engineer AI 119

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.

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Machine Learning Data Drift ML Data Scientist 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.

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Computer Vision Auto-classification Deep Learning 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

O'Reilly Media

OCTOBER 19, 2021

This introduces further requirements: The scale of operations is often two orders of magnitude larger than in the earlier data-centric environments. Not only is data larger, but models—deep learning models in particular—are much larger than before. Adapted from the book Effective Data Science Infrastructure.

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DevOps Software Engineer Data Scientist ML 140

AWS Machine Learning Blog

SEPTEMBER 18, 2023

SageMaker geospatial capabilities make it easy for data scientists and machine learning (ML) engineers to build, train, and deploy models using geospatial data. Mark Hallows is a Remote Sensing Specialist at Arup, based in London.

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Data Scientist ML Machine Learning ML Engineer 109

AWS Machine Learning Blog

APRIL 25, 2023

This allows ML engineers and admins to configure these environment variables so data scientists can focus on ML model building and iterate faster. You can select the Data Science 3.0 SageMaker uses training jobs to launch this function as a managed job. SchemaVersion: '1.0'

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Machine Learning Data Scientist Python ML 85

AWS Machine Learning Blog

MAY 13, 2024

His expertise is in reproducible and end-to-end AI/ML methods, practical implementations, and helping global healthcare customers formulate and develop scalable solutions to interdisciplinary problems. He has two graduate degrees in physics and a doctorate in engineering.

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Generative AI Prompt Engineer Prompt Engineering LLM 120

AWS Machine Learning Blog

MARCH 1, 2023

These are all implemented as a single ML pipeline using Amazon SageMaker Pipelines , and all the ML trainings are managed via Amazon SageMaker Experiments. ML engineers no longer need to manage this training metadata separately. ML model We selected AutoGluon for model training implemented with SageMaker pipelines.

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Automation ETL Data Drift ML 94

AWS Machine Learning Blog

OCTOBER 2, 2024

About the Authors Akarsha Sehwag is a Data Scientist and ML Engineer in AWS Professional Services with over 5 years of experience building ML based services and products. Leveraging her expertise in Computer Vision and Deep Learning, she empowers customers to harness the power of the ML in AWS cloud efficiently.

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Generative AI Data Ingestion AI AI 111

AWS Machine Learning Blog

AUGUST 16, 2023

The original concept came out of an AI/ML Hackathon supported by Simone Zucchet (AWS Solutions Architect) and Tim Precious (AWS Account Manager) and was developed into production using AWS services in under 6 weeks with support from AWS. Prior to this role, he was the Head of Data Science for Amazon’s EU Customer Service.

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Large Language Models Machine Learning Generative AI LLM 79

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. I really enjoyed it.

ML 52

ML Data Scientist Machine Learning Data Science 52

AWS Machine Learning Blog

JANUARY 13, 2023

Based on this idea, the security protocol Flow Layer 1 and the ML algorithm process Flow Layer 2 can be easily separated so that security engineers and ML engineers can operate while maintaining a modular architecture. The FedML open-source library supports federated ML use cases for edge as well as cloud.

Machine Learning 81

Machine Learning ML Algorithm Data Scientist 81

Viso.ai

MARCH 29, 2024

MLflow is an open-source platform designed to manage the entire machine learning lifecycle, making it easier for ML Engineers, Data Scientists, Software Developers, and everyone involved in the process. Many machine learning projects fail to deliver practical results due to difficulties in automation and deployment.

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Machine Learning ML Automation Data Scientist 52