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.

Machine Learning 52

Machine Learning ML Data Drift Data Quality 52

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.

Machine Learning 52

Machine Learning ML Data Drift Data Quality 52

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.

Machine Learning 52

Machine Learning ML Data Drift Data Quality 52

AWS Machine Learning Blog

JANUARY 28, 2025

Furthermore, evaluation processes are important not only for LLMs, but are becoming essential for assessing prompt template quality, input data quality, and ultimately, the entire application stack. He holds a PhD in Telecommunications Engineering and has experience in software engineering.

LLM 114

LLM Large Language Models ML Algorithm 114

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. Data Management – Efficient data management is crucial for AI/ML platforms. Outside work, she enjoys spending time with her family, traveling and reading.

Data Scientist 124

Data Scientist ML Data Science Automation 124

AWS Machine Learning Blog

APRIL 26, 2023

Do you need help to move your organization’s Machine Learning (ML) journey from pilot to production? Most executives think ML can apply to any business decision, but on average only half of the ML projects make it to production. Ensuring data quality, governance, and security may slow down or stall ML projects.

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ML Machine Learning Data Science Data Drift 114

AWS Machine Learning Blog

NOVEMBER 13, 2024

You can now register machine learning (ML) models in Amazon SageMaker Model Registry with Amazon SageMaker Model Cards , making it straightforward to manage governance information for specific model versions directly in SageMaker Model Registry in just a few clicks. Prepare the data to build your model training pipeline.

Metadata 112

Metadata ML Software Engineer Machine Learning 112

ODSC - Open Data Science

FEBRUARY 24, 2023

There are many well-known libraries and platforms for data analysis such as Pandas and Tableau, in addition to analytical databases like ClickHouse, MariaDB, Apache Druid, Apache Pinot, Google BigQuery, Amazon RedShift, etc. With these data exploration tools, you can determine if your data is accurate, consistent, and reliable.

Data Analysis 126

Data Analysis Data Science Business Intelligence Python 126

AWS Machine Learning Blog

OCTOBER 28, 2024

Real-world applications vary in inference requirements for their artificial intelligence and machine learning (AI/ML) solutions to optimize performance and reduce costs. SageMaker Model Monitor provides monitoring capabilities for data quality, model quality, bias drift in a model’s predictions, and drift in feature attribution.

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ML Metadata Data Scientist DevOps 113

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. After all, aren’t deep learning models closely related to their predecessors?

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Deep Learning Neural Network Convolutional Neural Networks Data Scientist 111

AWS Machine Learning Blog

JANUARY 17, 2024

In this first post, we introduce mobility data, its sources, and a typical schema of this data. We then discuss the various use cases and explore how you can use AWS services to clean the data, how machine learning (ML) can aid in this effort, and how you can make ethical use of the data in generating visuals and insights.

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ETL ML Machine Learning Data Scientist 131

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

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.

LLM 69

LLM Data Scientist Machine Learning Large Language Models 69

TensorFlow

MARCH 10, 2023

For example, the data engineering process, which is owned by IT teams, is revised. Data Contracts Define the Expected Form and Shape of Data Data Contracts in machine learning are a set of rules that define the structure, data types, and constraints of the data that your models are trained on.

Data Drift 52

Data Drift Data Scientist ML Engineer Machine Learning 52

Mlearning.ai

MAY 23, 2023

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. While product mindset focuses on the end data product: the minimum viable product. What is the Difference? The model is the core.

Machine Learning 52

Machine Learning Data Scientist ML Data Science 52

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.

LLM 59

LLM Data Scientist Machine Learning Large Language Models 59

The MLOps Blog

MAY 17, 2023

From data processing to quick insights, robust pipelines are a must for any ML system. Often the Data Team, comprising Data and ML Engineers , needs to build this infrastructure, and this experience can be painful. However, efficient use of ETL pipelines in ML can help make their life much easier.

ETL 59

ETL ML Machine Learning Data Scientist 59

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.

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Data Drift Machine Learning Computer Vision Algorithm 52

Snorkel AI

JUNE 9, 2023

Accelerate ML Adoption by Addressing Hidden Needs Max Williams, AI platform product manager at Wells Fargo , discussed the challenges of achieving a return on investment in machine learning as well as the hidden needs an organization must address for ML to gain widespread adoption and deliver attractive returns.

Large Language Models 52

Large Language Models Data Scientist Machine Learning Computer Vision 52

Snorkel AI

JUNE 9, 2023

Accelerate ML Adoption by Addressing Hidden Needs Max Williams, AI platform product manager at Wells Fargo , discussed the challenges of achieving a return on investment in machine learning as well as the hidden needs an organization must address for ML to gain widespread adoption and deliver attractive returns.

Large Language Models 52

Large Language Models Data Scientist Machine Learning Computer Vision 52

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.

Machine Learning 40

Machine Learning Algorithm Business Intelligence Data Quality 40

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.

LLM 52

LLM Data Scientist Machine Learning Large Language Models 52

The MLOps Blog

MARCH 15, 2023

Based on the McKinsey survey , 56% of orgs today are using machine learning in at least one business function. Some of these services aren’t specifically meant for ML models, but we managed to adeptly repurpose them for our model deployment. This includes data quality, privacy, and compliance.

ETL 52

ETL Data Drift Machine Learning ML 52

Snorkel AI

OCTOBER 12, 2023

Leveraging Data-Centric AI for Document Intelligence and PDF Extraction Extracting entities from semi-structured documents is often a challenging task, requiring complex and time-consuming manual processes. Poster Competition: JoinBoost: Tree Training with Just SQL Data and machine learning (ML) are crucial for enterprise operations.

Data Scientist 64

Data Scientist ML Computer Vision AI 64

Snorkel AI

JULY 5, 2023

I lead product marketing efforts for TensorFlow and multiple open-source and machine learning initiatives at Google. Robert Crowe : I’m Robert Crowe, I’m a developer engineer in the TensorFlow team. Is more data always better? More features mean more data consumed upstream.

Large Language Models 52

Large Language Models Metadata Machine Learning AI 52

Snorkel AI

JULY 5, 2023

I lead product marketing efforts for TensorFlow and multiple open-source and machine learning initiatives at Google. Robert Crowe : I’m Robert Crowe, I’m a developer engineer in the TensorFlow team. Is more data always better? More features mean more data consumed upstream.

Large Language Models 52

Large Language Models Metadata Machine Learning AI 52

Snorkel AI

JULY 5, 2023

I lead product marketing efforts for TensorFlow and multiple open-source and machine learning initiatives at Google. Robert Crowe : I’m Robert Crowe, I’m a developer engineer in the TensorFlow team. Is more data always better? More features mean more data consumed upstream.

Large Language Models 52

Large Language Models Metadata Machine Learning AI 52

Snorkel AI

OCTOBER 12, 2023

Leveraging Data-Centric AI for Document Intelligence and PDF Extraction Extracting entities from semi-structured documents is often a challenging task, requiring complex and time-consuming manual processes. Poster Competition: JoinBoost: Tree Training with Just SQL Data and machine learning (ML) are crucial for enterprise operations.

Data Scientist 59

Data Scientist ML Computer Vision AI 59

Snorkel AI

OCTOBER 12, 2023

Leveraging Data-Centric AI for Document Intelligence and PDF Extraction Extracting entities from semi-structured documents is often a challenging task, requiring complex and time-consuming manual processes. Poster Competition: JoinBoost: Tree Training with Just SQL Data and machine learning (ML) are crucial for enterprise operations.

Data Scientist 52

Data Scientist NLP ML Computer Vision 52

The MLOps Blog

JULY 18, 2023

Was that your first touch with speech recognition, a machine learning application? Fast, iterative data labeling is absolutely critical to our stack. I think another huge component, as I kind of was mentioning earlier, is Conversational AI tends to require large pipelines of machine learning. Sabine: Absolutely.

Conversational AI 52

Conversational AI Natural Language Processing Machine Learning AI 52

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. We want to stop the pain and suffering people feel with maintaining machine learning pipelines in production.

ML 52

ML Data Scientist Software Engineer Machine Learning 52

AWS Machine Learning Blog

JANUARY 26, 2024

The goal of this post is to empower AI and machine learning (ML) engineers, data scientists, solutions architects, security teams, and other stakeholders to have a common mental model and framework to apply security best practices, allowing AI/ML teams to move fast without trading off security for speed.

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Generative AI LLM ML AI 136

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.

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ML Machine Learning Metadata Data Science 98

AWS Machine Learning Blog

NOVEMBER 14, 2024

We recently announced the general availability of cross-account sharing of Amazon SageMaker Model Registry using AWS Resource Access Manager (AWS RAM) , making it easier to securely share and discover machine learning (ML) models across your AWS accounts. Siamak Nariman is a Senior Product Manager at AWS. Madhubalasri B.

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ML Auto-complete Machine Learning Auto-classification 104

The MLOps Blog

AUGUST 11, 2023

At that point, the Data Scientists or ML Engineers become curious and start looking for such implementations. Many questions regarding building machine learning pipelines and systems have already been answered and come from industry best practices and patterns. How should the machine learning pipeline operate?

ML 52

ML Machine Learning Data Ingestion Deep Learning 52

The MLOps Blog

MARCH 21, 2023

Moving across the typical machine learning lifecycle can be a nightmare. 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. How to understand your users (data scientists, ML engineers, etc.).

Machine Learning 69

Machine Learning Data Scientist ML Metadata 69

Towards AI

NOVEMBER 8, 2024

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. Because of how ML practitioners were initially trained.

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LLM ML Engineer Data Quality Data Scientist 126