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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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.

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Mlearning.ai

MAY 23, 2023

Product First versus Model First mindset is a important concept as you mature in data science. It’s critical for beginners learn this, since it affects everything: workflows, data quality requirements, etc. Model mindset prioritizes the ML model that you are building. There are two approaches we see in MLOps.

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Snorkel AI

OCTOBER 27, 2023

Instead of exclusively relying on a singular data development technique, leverage a variety of techniques such as promoting, RAG, and fine-tuning for the most optimal outcome. Focus on improving data quality and transforming manual data development processes into programmatic operations to scale fine-tuning.

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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. Data scientists and ML engineers: Creating and training deep learning models is no easy feat.

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Snorkel AI

OCTOBER 27, 2023

Instead of exclusively relying on a singular data development technique, leverage a variety of techniques such as promoting, RAG, and fine-tuning for the most optimal outcome. Focus on improving data quality and transforming manual data development processes into programmatic operations to scale fine-tuning.

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LLM Data Scientist Machine Learning Large Language Models 59

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.”

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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.”

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Large Language Models Data Scientist Machine Learning Computer Vision 52

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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Snorkel AI

OCTOBER 12, 2023

Chief Data Scientist In this fireside chat as Snorkel AI CEO and co-founder Alex Ratner and DJ Patil, the Former U.S. Chief Data Scientist dive into data science’s history, impact, and challenges in the United States government.

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Snorkel AI

OCTOBER 12, 2023

Chief Data Scientist In this fireside chat as Snorkel AI CEO and co-founder Alex Ratner and DJ Patil, the Former U.S. Chief Data Scientist dive into data science’s history, impact, and challenges in the United States government.

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Data Scientist ML Computer Vision AI 59

Pickl AI

JUNE 6, 2024

Data scrubbing is often used interchangeably but there’s a subtle difference. Cleaning is broader, improving data quality. This is a more intensive technique within data cleaning, focusing on identifying and correcting errors. Data scrubbing is a powerful tool within this cleaning service.

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Machine Learning Algorithm Business Intelligence Data Quality 40

Snorkel AI

OCTOBER 12, 2023

Chief Data Scientist In this fireside chat as Snorkel AI CEO and co-founder Alex Ratner and DJ Patil, the Former U.S. Chief Data Scientist dive into data science’s history, impact, and challenges in the United States government.

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The MLOps Blog

AUGUST 3, 2023

This is Piotr Niedźwiedź and Aurimas Griciūnas from neptune.ai , and you’re listening to ML Platform Podcast. Stefan is a software engineer, data scientist, and has been doing work as an ML engineer. As you’ve been running the ML data platform team, how do you do that? Stefan: Yeah.

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ML Data Scientist Software Engineer Machine Learning 52

AWS Machine Learning Blog

NOVEMBER 14, 2024

Model governance involves overseeing the development, deployment, and maintenance of ML models to help ensure that they meet business objectives and are accurate, fair, and compliant with regulations. Runs are executions of some piece of data science code and record metadata and generated artifacts.

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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. Data preprocessing.

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Towards AI

NOVEMBER 8, 2024

. — Peter Norvig, The Unreasonable Effectiveness of Data. Edited Photo by Taylor Vick on Unsplash In ML engineering, data quality isn’t just critical — it’s foundational. Since 2011, Peter Norvig’s words underscore the power of a data-centric approach in machine learning. Using biased or low-quality data?

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The MLOps Blog

MARCH 21, 2023

From gathering and processing data to building models through experiments, deploying the best ones, and managing them at scale for continuous value in production—it’s a lot. 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.

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The MLOps Blog

JANUARY 26, 2024

They still have their infrastructure in physical data centers and server racks. Mailchimp had decided, “We’ll move the burgeoning data science and machine learning initiatives in batches, including any data engineers needed to support those. This process created a latency of approximately one day for the data.

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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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