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. There are two approaches we see in MLOps. What is the Difference?

Machine Learning 52

Machine Learning Data Scientist ML Data Science 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.

Generative AI 122

Generative AI LLM ML AI 122

Snorkel AI

JUNE 9, 2023

Among other topics, he highlighted how visual prompts and parameter-efficient models enable rapid iteration for improved data quality and model performance. He also described a near future where large companies will augment the performance of their finance and tax professionals with large language models, co-pilots, and AI agents.

Large Language Models 52

Large Language Models Data Scientist Machine Learning Computer Vision 52

Snorkel AI

JUNE 9, 2023

Among other topics, he highlighted how visual prompts and parameter-efficient models enable rapid iteration for improved data quality and model performance. He also described a near future where large companies will augment the performance of their finance and tax professionals with large language models, co-pilots, and AI agents.

Large Language Models 52

Large Language Models Data Scientist Machine Learning Computer Vision 52

Viso.ai

JANUARY 30, 2024

This is a bigger deal with raw or unstructured data that engineers and developers might be using to feed the machine learning program. Data scientists know about the perils of unstructured data – but it’s not always something that ML engineers think about – until it’s too late.

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Data Drift Machine Learning Computer Vision Algorithm 52

AWS Machine Learning Blog

MARCH 9, 2023

As machine learning (ML) models have improved, data scientists, ML engineers and researchers have shifted more of their attention to defining and bettering data quality. Applying these techniques allows ML practitioners to reduce the amount of data required to train an ML model.

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ML Engineer Data Scientist Convolutional Neural Networks ML 77

The MLOps Blog

JUNE 27, 2023

Data quality control: Robust dataset labeling and annotation tools incorporate quality control mechanisms such as inter-annotator agreement analysis, review workflows, and data validation checks to ensure the accuracy and reliability of annotations. Data monitoring tools help monitor the quality of the data.

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Machine Learning Metadata Data Quality Data Scientist 134

The MLOps Blog

MARCH 15, 2023

For small-scale/low-value deployments, there might not be many items to focus on, but as the scale and reach of deployment go up, data governance becomes crucial. This includes data quality, privacy, and compliance. For an experienced Data Scientist/ML engineer, that shouldn’t come as so much of a problem.

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ETL Data Drift Machine Learning ML 52

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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Deep Learning Neural Network Convolutional Neural Networks Data Scientist 111

ODSC - Open Data Science

FEBRUARY 24, 2023

Its goal is to help with a quick analysis of target characteristics, training vs testing data, and other such data characterization tasks. Apache Superset GitHub | Website Apache Superset is a must-try project for any ML engineer, data scientist, or data analyst.

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Data Analysis Business Intelligence Data Science Python 126

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

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

AWS Machine Learning Blog

APRIL 26, 2023

You may have gaps in skills and technologies, including operationalizing ML solutions, implementing ML services, and managing ML projects for rapid iterations. Ensuring data quality, governance, and security may slow down or stall ML projects. We recognize that customers have different starting points.

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ML Machine Learning Data Science Data Drift 88

AWS Machine Learning Blog

JANUARY 17, 2024

Solution overview As mentioned earlier, the AWS services that you can use for analysis of mobility data are Amazon S3, Amazon Macie, AWS Glue, S3 Object Lambda, Amazon Comprehend, and Amazon SageMaker geospatial capabilities. Example 1 – The following screenshot shows all visits to the Macy’s store.

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ETL ML Machine Learning Data Scientist 103

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.

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ETL ML Machine Learning Data Scientist 59

Snorkel AI

JULY 5, 2023

Organizations struggle in multiple aspects, especially in modern-day data engineering practices and getting ready for successful AI outcomes. One of them is that it is really hard to maintain high data quality with rigorous validation. More features mean more data consumed upstream.

Large Language Models 52

Large Language Models Metadata AI AI 52

Snorkel AI

JULY 5, 2023

Organizations struggle in multiple aspects, especially in modern-day data engineering practices and getting ready for successful AI outcomes. One of them is that it is really hard to maintain high data quality with rigorous validation. More features mean more data consumed upstream.

Large Language Models 52

Large Language Models Metadata AI 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. She starts by discussing the challenges associated with extracting from PDFs and other semi-structured documents.

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Data Scientist AI AI ML 64

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. She starts by discussing the challenges associated with extracting from PDFs and other semi-structured documents.

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Data Scientist AI AI ML 59

The MLOps Blog

JULY 18, 2023

And even on the operation side of things, is there a separate operations team, and then you have your research or ml engineers doing these pipelines and stuff? How do you ensure data quality when building NLP products? How would you ensure that your data is high-quality throughout the life cycle of the product?

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Conversational AI Natural Language Processing AI AI 52

The MLOps Blog

AUGUST 11, 2023

Getting a workflow ready which takes your data from its raw form to predictions while maintaining responsiveness and flexibility is the real deal. At that point, the Data Scientists or ML Engineers become curious and start looking for such implementations. Synchronous training What is synchronous training architecture?

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ML Machine Learning Data Ingestion Deep Learning 52

The MLOps Blog

JANUARY 26, 2024

Then, we made an effort to engage data scientists through workshops and tailored support to transition smoothly to these better solutions. We also had ML engineers embedded in the data science teams that helped bridge gaps left by the tooling and infrastructure.

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ML Data Scientist Machine Learning ML Engineer 64

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. One of the features that Hamilton has is that it has a really lightweight data quality runtime check.

ML 52

ML Data Scientist Software Engineer Machine Learning 52

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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ML Machine Learning Metadata Automation 98

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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Machine Learning Data Scientist ML Metadata 69