Towards AI

APRIL 4, 2023

SAN JOSE, CA (April 4, 2023) — Edge Impulse, the leading edge AI platform, today announced Bring Your Own Model (BYOM), allowing AI teams to leverage their own bespoke ML models and optimize them for any edge device. Praise Edge Impulse and its new features are garnering accolades from industry leaders.

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Towards AI

FEBRUARY 21, 2023

We observe that the main agents at the moment for AI progression are people working in machine learning as engineers and researchers. A sensible proxy sub-question might then be: Can ChatGPT function as a competent machine learning engineer? ChatGPT’s job as our ML engineer […]

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Marktechpost

JANUARY 31, 2024

Data scientists and ML engineers often need help to build full-stack applications. Still, they may need more skills or time to learn new languages or frameworks to create user-friendly web applications. It is a Python-based framework for data scientists and machine learning engineers.

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Data Scientist Machine Learning Python ML Engineer 135

Pickl AI

APRIL 19, 2023

Machine Learning is the part of Artificial Intelligence and computer science that emphasizes on the use of data and algorithms, imitating the way humans learn and improving accuracy. Machine Learning is becoming increasingly popular and crucial in today’s market for effective productivity and decision-making processes.

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Flipboard

MAY 31, 2023

Learn how to easily build any AI model and customize your own LLM in just a few lines of code with a declarative approach to machine learning.

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Marktechpost

JANUARY 10, 2024

Automated Machine Learning (AutoML) has been introduced to address the pressing need for proactive and continual learning in content moderation defenses on the LinkedIn platform. It is a framework for automating the entire machine-learning process, specifically focusing on content moderation classifiers.

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Machine Learning Continuous Learning ML Engineer Automation 104

ODSC - Open Data Science

FEBRUARY 9, 2023

That responsibility usually falls in the hands of a role called Machine Learning (ML) Engineer. Having empathy for your ML Engineering colleagues means helping them meet operational constraints. To continue with this analogy, you might think of the ML Engineer as the data scientist’s “editor.”

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Data Scientist Machine Learning ML Engineer ML 105

AWS Machine Learning Blog

FEBRUARY 27, 2024

In this post, we share how Axfood, a large Swedish food retailer, improved operations and scalability of their existing artificial intelligence (AI) and machine learning (ML) operations by prototyping in close collaboration with AWS experts and using Amazon SageMaker. This is a guest post written by Axfood AB.

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Marktechpost

AUGUST 8, 2023

In the ever-evolving landscape of machine learning, feature management has emerged as a key pain point for ML Engineers at Airbnb. Chronon enables users to generate thousands of features to power ML models effortlessly by simplifying feature engineering.

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Machine Learning ML Engineer Data Ingestion Metadata 98

Pickl AI

APRIL 5, 2023

Machine Learning is one of the transforming technologies that has had a ripple effect across the industry domain. ML forms the underlying platform for several new developments. Hence, it has also triggered the demand for ML experts. Acquiring Machine Learning skills can have catalytic impact on your professional growth.

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Analytics Vidhya

JANUARY 28, 2022

Introduction A Machine Learning solution to an unambiguously defined business problem is developed by a Data Scientist ot ML Engineer. This article was published as a part of the Data Science Blogathon.

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ML ML Engineer Data Scientist Machine Learning 339

Snorkel AI

APRIL 27, 2023

Here at Snorkel AI, we devote our time to building and maintaining our machine-learning development platform, Snorkel Flow. Snorkel Flow handles intense machine learning workloads, and we’ve built our infrastructure on a foundation of Kubernetes—which was not designed with machine learning in mind.

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Snorkel AI

APRIL 27, 2023

Here at Snorkel AI, we devote our time to building and maintaining our machine-learning development platform, Snorkel Flow. Snorkel Flow handles intense machine learning workloads, and we’ve built our infrastructure on a foundation of Kubernetes—which was not designed with machine learning in mind.

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AI News

MARCH 8, 2024

Hugging Face , the startup behind the popular open source machine learning codebase and ChatGPT rival Hugging Chat, is venturing into new territory with the launch of an open robotics project. Until now, Hugging Face has primarily focused on software offerings like its machine learning codebase and open-source chatbot.

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Robotics Big Data Deep Learning ML Engineer 230

Flipboard

JUNE 20, 2023

Machine learning (ML) engineers have traditionally focused on striking a balance between model training and deployment cost vs. performance. This is important because training ML models and then using the trained models to make predictions (inference) can be highly energy-intensive tasks.

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Towards AI

AUGUST 7, 2024

From Solo Notebooks to Collaborative Powerhouse: VS Code Extensions for Data Science and ML Teams Photo by Parabol | The Agile Meeting Toolbox on Unsplash In this article, we will explore the essential VS Code extensions that enhance productivity and collaboration for data scientists and machine learning (ML) engineers.

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Data Science ML ML Engineer Software Engineer 113

Heartbeat

SEPTEMBER 6, 2023

Experimenting with Comet, a machine learning platform Photo by Donny Jiang on Unsplash Creating a machine learning model is easy, but that’s not what machine learning is all about. There are machine learning platforms that can perform all these tasks, and Comet is one such platform.

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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?

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Marktechpost

OCTOBER 11, 2023

The team started with a collection of 15 ML engineering projects spanning various fields, with experiments that are quick and cheap to run. The post Researchers from Stanford University Propose MLAgentBench: A Suite of Machine Learning Tasks for Benchmarking AI Research Agents appeared first on MarkTechPost.

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Machine Learning AI Research AI Researcher Convolutional Neural Networks 105

deepsense.ai

APRIL 18, 2023

As it does every year, the event is focused on the exchange of experiences between machine learning practitioners and, most importantly, an effective update of knowledge in the rapidly changing discipline of data analysis. Once again, deepsense.ai has become GHOST Day: AMLC conference platinum partner. The post deepsense.ai

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Machine Learning Data Scientist Large Language Models ML Engineer 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.

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IBM Journey to AI blog

AUGUST 12, 2024

Instead, businesses tend to rely on advanced tools and strategies—namely artificial intelligence for IT operations (AIOps) and machine learning operations (MLOps)—to turn vast quantities of data into actionable insights that can improve IT decision-making and ultimately, the bottom line.

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Mlearning.ai

JULY 29, 2023

Schedule Batch Inference of Machine Learning Model on Azure Cloud with Container Services and Logic App Photo by Victoire Joncheray on Unsplash I. This approach is heavily inspired by the book Designing Machine Learning Systems by Chip Huyen , a go-to resource for any ML Engineer.

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Machine Learning DevOps ML ML Engineer 52

TheSequence

JANUARY 26, 2024

Want to know how FanDuel engineered and built a powerful ML platform to handle hundreds of millions of data rows and evaluate millions of results—all to deliver personalized experiences to their users?

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Machine Learning ML Engineer ML 52

Marktechpost

JULY 19, 2023

In the actual world, machine learning (ML) systems can embed issues like societal prejudices and safety worries. Stakeholders such as ML engineers, designers, and domain experts must work together to identify a model’s expected and potential faults. Thus more improvements would greatly accelerate processing.

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TheSequence

JULY 7, 2023

Discover FanDuel's journey in building a powerful ML platform for personalized experiences. PT to learn how they scaled their platform and lessons learned. Join the webinar on July 11 at 9 a.m.

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Machine Learning ML Engineer ML 52

AI News

JULY 28, 2023

AI and machine learning are reshaping the job landscape, with higher incentives being offered to attract and retain expertise amid talent shortages. According to a recent report by Harnham , a leading data and analytics recruitment agency in the UK, the demand for ML engineering roles has been steadily rising over the past few years.

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ML ML Engineer Data Science Big Data 189

AWS Machine Learning Blog

MARCH 30, 2023

The result is a machine learning (ML)-powered insight that allows fans to easily evaluate and compare the goalkeepers’ proficiencies. He enables customers in the area of spectator sports to innovate and capitalize on their data, delivering high-quality user and fan experiences through machine learning and data science.

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Machine Learning Data Scientist ML Data Science 80

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.

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Analytics Vidhya

OCTOBER 6, 2023

Ray streamlines complex tasks for ML engineers, data scientists, and developers. Its versatility spans data processing, model training, hyperparameter tuning, deployment, and reinforcement learning. Python Ray is a dynamic framework revolutionizing distributed computing.

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Python ML Engineer Data Scientist ML 341

AWS Machine Learning Blog

JULY 8, 2024

As industries begin adopting processes dependent on machine learning (ML) technologies, it is critical to establish machine learning operations (MLOps) that scale to support growth and utilization of this technology. Managers lacked the visibility needed for ongoing monitoring of ML workflows.

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Data Scientist ML Engineer Machine Learning ML 106

Snorkel AI

JUNE 30, 2023

Jack Zhou, product manager at Arize , gave a lightning talk presentation entitled “How to Apply Machine Learning Observability to Your ML System” at Snorkel AI’s Future of Data-Centric AI virtual conference in August 2022. So this path on the right side of the production icon is what we’re calling ML observability.

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Snorkel AI

JUNE 30, 2023

Jack Zhou, product manager at Arize , gave a lightning talk presentation entitled “How to Apply Machine Learning Observability to Your ML System” at Snorkel AI’s Future of Data-Centric AI virtual conference in August 2022. So this path on the right side of the production icon is what we’re calling ML observability.

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Machine Learning Data Drift ML Data Quality 52

AWS Machine Learning Blog

SEPTEMBER 4, 2024

Its scalability and load-balancing capabilities make it ideal for handling the variable workloads typical of machine learning (ML) applications. In this post, we introduce an example to help DevOps engineers manage the entire ML lifecycle—including training and inference—using the same toolkit.

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Marktechpost

MAY 30, 2024

This article lists the top AI courses by Google that provide comprehensive training on various AI and machine learning technologies, equipping learners with the skills needed to excel in the rapidly evolving field of AI. Participants learn how to improve model accuracy and write scalable, specialized ML models.

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Artificial Intelligence Artificial Intelligence BERT Computer Vision 132

Mlearning.ai

APRIL 17, 2023

Developing a machine learning model involves several steps: problem formulation, data collection, preprocessing, feature engineering, model-building, deployment, and monitoring. image by author Introduction Error analysis is a vital process in diagnosing errors made by an ML model during its training and testing steps.

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Analytics Vidhya

FEBRUARY 21, 2022

Image designed by the author – Shanthababu Introduction Every ML Engineer and Data Scientist must understand the significance of “Hyperparameter Tuning (HPs-T)” while selecting your right machine/deep learning model and improving the performance of the model(s). Make it simple, for every […].

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ML Engineer Data Scientist Deep Learning Data Science 337