ODSC - Open Data Science

FEBRUARY 22, 2024

Today we are excited to bring you just a few of the machine learning sessions you’ll be able to participate in if you attend. In this session, you’ll take a deep dive into the three distinct types of Feature Stores and their uses in the machine learning ecosystem. Check them out below. Who Wants to Live Forever?

Machine Learning 52

Machine Learning Data Science Python ML 52

Heartbeat

OCTOBER 30, 2023

Ensuring Long-Term Performance and Adaptability of Deployed Models Source: [link] Introduction When working on any machine learning problem, data scientists and machine learning engineers usually spend a lot of time on data gathering , efficient data preprocessing , and modeling to build the best model for the use case.

Machine Learning 52

Machine Learning Data Drift ML Data Scientist 52

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.

Data Science 144

Data Science DevOps Machine Learning Data Scientist 144

Towards AI

AUGUST 25, 2023

DL Artificial intelligence (AI) is the study of ways to build intelligent programs and machines that can creatively solve problems, which has always been considered a human prerogative. In ML, there are a variety of algorithms that can help solve problems. Any competent software engineer can implement any algorithm. 12, 2014. [3]

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Software Engineer Machine Learning Neural Network Artificial Intelligence 96

The MLOps Blog

NOVEMBER 14, 2023

Deep learning models are typically highly complex. While many traditional machine learning models make do with just a couple of hundreds of parameters, deep learning models have millions or billions of parameters. The reasons for this range from wrongly connected model components to misconfigured optimizers.

Deep Learning 111

Deep Learning Neural Network Convolutional Neural Networks Data Scientist 111

Unite.AI

FEBRUARY 28, 2024

The sheer scale of these models, combined with advanced deep learning techniques, enables them to achieve state-of-the-art performance on language tasks. Adversarial attacks Adversarial attacks involve specially crafted inputs designed to deceive machine learning models and trigger unintended behaviors.

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Large Language Models LLM Machine Learning Neural Network 162

ODSC - Open Data Science

MARCH 27, 2023

The Next Generation of Low-Code Machine Learning Devvret Rishi | Co-founder and Chief Product Officer | Predibase In this session, you’ll explore declarative machine learning, a configuration-based modeling interface, which provides more flexibility and simplicity when implementing cutting-edge machine learning.

Machine Learning 98

Machine Learning Data Ingestion Explainability ML 98

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

AWS Machine Learning Blog

NOVEMBER 21, 2023

By taking care of the undifferentiated heavy lifting, SageMaker allows you to focus on working on your machine learning (ML) models, and not worry about things such as infrastructure. These two crucial parameters influence the efficiency, speed, and accuracy of training deep learning models.

ML 82

ML Deep Learning Machine Learning DevOps 82

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

ODSC - Open Data Science

FEBRUARY 2, 2023

Machine Learning As machine learning is one of the most notable disciplines under data science, most employers are looking to build a team to work on ML fundamentals like algorithms, automation, and so on. This will lead to algorithm development for any machine or deep learning processes.

Data Science 98

Data Science Data Scientist Big Data Machine Learning 98

Towards AI

JANUARY 27, 2024

Image created with Microsoft Bing Image Maker AutoKeras AutoKeras is Python’s Keras-based AutoML library for developing Deep Learning models. pub.towardsai.net Conclusion From the page, it is evident that the AutoKeras library facilitates the automation of developing deep learning models with minimal code.I

Deep Learning 93

Deep Learning Explainability ML Engineer Data Scientist 93

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.

Software Engineer 92

Software Engineer Neural Network Algorithm AI Engineer 92

Flipboard

SEPTEMBER 4, 2023

Home Table of Contents Getting Started with Docker for Machine Learning Overview: Why the Need? How Do Containers Differ from Virtual Machines? Finally, we will top it off by installing Docker on our local machine with simple and easy-to-follow steps. How Do Containers Differ from Virtual Machines?

Machine Learning 109

Machine Learning Computer Vision Deep Learning Python 109

ODSC - Open Data Science

JANUARY 10, 2024

Using Graphs for Large Feature Engineering Pipelines Wes Madrigal | ML Engineer | Mad Consulting This talk will outline the complexity of feature engineering from raw entity-level data, the reduction in complexity that comes with composable compute graphs, and an example of the working solution.

Large Language Models 52

Large Language Models LLM Deep Learning ML 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. Improve the quality and time to market for deep learning models in diagnostic medical imaging.

Data Scientist 89

Data Scientist ML Data Science Automation 89

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. Techniques such as decision trees, support vector machines, and neural networks gained popularity.

Neural Network 52

Neural Network Software Engineer Artificial Intelligence Artificial Intelligence 52

AWS Machine Learning Blog

SEPTEMBER 20, 2023

In these scenarios, as you start to embrace generative AI, large language models (LLMs) and machine learning (ML) technologies as a core part of your business, you may be looking for options to take advantage of AWS AI and ML capabilities outside of AWS in a multicloud environment.

ML 99

ML Python Deep Learning Machine Learning 99

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.

ML 52

ML Data Scientist ML Engineer Machine Learning 52

Pickl AI

MAY 30, 2023

Top Job-Oriented Courses for Higher Salaries: Business Analytics Certification Program The program has been specifically designed for the aspirants interested in Analytics which includes to expand their skills in Statistics, Predictive Modelling and Machine Learning. Through the Data Science Job Guarantee Program by Pickl.AI

Data Mining 52

Data Mining Data Science Deep Learning Machine Learning 52

Heartbeat

OCTOBER 5, 2023

Unsurprisingly, Machine Learning (ML) has seen remarkable progress, revolutionizing industries and how we interact with technology. This is where the world of operations steps in, and while MLOps (Machine Learning Operations) has been a guiding light, a new paradigm is emerging — LLMOps (Large Language Model Operations).

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Large Language Models DevOps Prompt Engineer Prompt Engineering 52

AWS Machine Learning Blog

FEBRUARY 9, 2023

SageMaker now supports geospatial machine learning (ML), making it easier for data scientists and ML engineers to build, train, and deploy models using geospatial data. In this post, we showed how to acquire data, perform analysis, and visualize the changes with SageMaker geospatial AI/ML services.

Machine Learning 55

Machine Learning ML Deep Learning Robotics 55

The MLOps Blog

JANUARY 26, 2024

Training and evaluating models is just the first step toward machine-learning success. For this, we have to build an entire machine-learning system around our models that manages their lifecycle, feeds properly prepared data into them, and sends their output to downstream systems. But what is an ML pipeline?

Machine Learning 73

Machine Learning Metadata ML Python 73

Towards AI

JULY 19, 2023

Lifecycle of a Machine Learning Project. This shows that scoping the projects is one of the most important steps before starting a machine learning project. What can you recommend to him as an ML Engineer? A better search engine for his site. Let’s take a look at the lifecycle once.

ML 80

ML Machine Learning ML Engineer Explainability 80

AWS Machine Learning Blog

JUNE 8, 2023

LLMs are based on the Transformer architecture , a deep learning neural network introduced in June 2017 that can be trained on a massive corpus of unlabeled text. This enables you to begin machine learning (ML) quickly. It includes the FLAN-T5-XL model , an LLM deployed into a deep learning container.

Generative AI 73

Generative AI LLM Large Language Models Machine Learning 73

Viso.ai

JANUARY 30, 2024

During machine learning model training, there are seven common errors that engineers and data scientists typically run into. One of the most common problems in machine learning happens when the training data is not well fitted to what the machine learning model is supposed to learn.

Data Drift 52

Data Drift Machine Learning Computer Vision Algorithm 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. In the industry, not every problem that comes your way needs machine learning modeling to solve.

Data Scientist 52

Data Scientist Data Science ML Machine Learning 52

Heartbeat

JULY 17, 2023

Comet, a cloud-based machine learning platform, offers a powerful solution for tracking, comparing, and benchmarking fine-tuned models, allowing users to easily analyze and visualize their performance. Comet allows ML engineers to track these metrics in real-time and visualize their performance using interactive dashboards.

Computer Vision 52

Computer Vision Auto-classification Deep Learning Machine Learning 52

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.

Machine Learning 52

Machine Learning ML Automation Data Scientist 52

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.

Machine Learning 116

Machine Learning Computer Vision Deep Learning Python 116

Snorkel AI

MAY 4, 2023

Join us on June 7-8 to learn how to use your data to build your AI moat at The Future of Data-Centric AI 2023. The free virtual conference is the largest annual gathering of the data-centric AI community.

Computer Vision 52

Computer Vision Large Language Models AI AI 52

Snorkel AI

MAY 4, 2023

Join us on June 7-8 to learn how to use your data to build your AI moat at The Future of Data-Centric AI 2023. The free virtual conference is the largest annual gathering of the data-centric AI community.

Computer Vision 52

Computer Vision Large Language Models AI AI 52

AWS Machine Learning Blog

SEPTEMBER 1, 2023

ML operationalization summary As defined in the post MLOps foundation roadmap for enterprises with Amazon SageMaker , ML and operations (MLOps) is the combination of people, processes, and technology to productionize machine learning (ML) solutions efficiently.

Generative AI 103

Generative AI Prompt Engineer Prompt Engineering AI 103

TheSequence

JULY 12, 2023

👤 Quick bio Tell us a bit about yourself: your background, current role, and how you got started in machine learning. At Cruise, we noticed a wide gap between the complexity of cloud infrastructure, and the needs of the ML workforce. What are the key differences between these two approaches to ML orchestration?

ML 97

ML Python Machine Learning Metadata 97

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 ML Engineer 52

Pickl AI

JANUARY 23, 2023

Introduction to Machine Learning Frameworks In the present world, almost every organization is making use of machine learning and artificial intelligence in order to stay ahead of the competition. So, let us see the most popular and best machine learning frameworks and their uses.

Machine Learning 52

Machine Learning ML Neural Network Auto-classification 52

BAIR

MARCH 11, 2024

You can also turn on Disqus comments, but we recommend disabling this feature. --> Every year, the Berkeley Artificial Intelligence Research (BAIR) Lab graduates some of the most talented and innovative minds in artificial intelligence and machine learning. Currently, I am working on Large Language Model (LLM) based autonomous agents.

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