AWS Machine Learning Blog

MARCH 13, 2024

The traditional method of training an in-house classification model involves cumbersome processes such as data annotation, training, testing, and model deployment, requiring the expertise of data scientists and ML engineers. LLMs, in contrast, offer a high degree of flexibility.

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Natural Language Processing LLM Prompt Engineering Prompt Engineer 124

AWS Machine Learning Blog

FEBRUARY 12, 2024

The Ranking team is now able choose between four different automatic tuning strategies for their hyperparameter selection: Grid search – AMT will expect all hyperparameters to be categorical values, and it will launch training jobs for each distinct categorical combination, exploring the entire hyperparameter space.

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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. million by 2030, with a remarkable CAGR of 44.8%

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AWS Machine Learning Blog

FEBRUARY 29, 2024

Configuration files (YAML and JSON) allow ML practitioners to specify undifferentiated code for orchestrating training pipelines using declarative syntax. The following are the key benefits of this solution: Automation – The entire ML workflow, from data preprocessing to model registry, is orchestrated with no manual intervention.

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Automation Python Machine Learning ML 109

TensorFlow

MARCH 10, 2023

It can also include constraints on the data, such as: Minimum and maximum values for numerical columns Allowed values for categorical columns. Before a model is productionized, the Contract is agreed upon by the stakeholders working on the pipeline, such as the ML Engineers, Data Scientists and Data Owners.

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Data Drift Data Scientist ML Engineer Machine Learning 52

Mlearning.ai

APRIL 17, 2023

image by author Introduction Error analysis is a vital process in diagnosing errors made by an ML model during its training and testing steps. It enables data scientists or ML engineers to evaluate their models’ performance and identify areas for improvement. If you’re interested, you can find more information in the repository.

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Machine Learning Categorization Data Analysis Neural Network 52

Snorkel AI

OCTOBER 27, 2023

How to fine-tune and customize LLMs Hoang Tran, ML Engineer at Snorkel AI, outlined how he saw LLMs creating value in enterprise environments. The first categorizes instructions, while the second assesses the quality of responses.

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AWS Machine Learning Blog

JUNE 9, 2023

This allows GuardDuty to categorize previously unseen domains as highly likely to be malicious or benign based on their association to known malicious domains. For example, Amazon GuardDuty , the native AWS threat detection service, uses a graph with billions of edges to improve the coverage and accuracy of its threat intelligence.

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AWS Machine Learning Blog

APRIL 12, 2023

Our ML models include emotion detection, transcription, and NLP-powered conversational analysis that categorizes violations and provides a rank score to determine how confident it is that a violation has occurred. To be able to iterate quickly, we needed a compute environment that was familiar to our data scientists and ML engineers.

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Neural Network Machine Learning ML ML Engineer 73

AWS Machine Learning Blog

SEPTEMBER 23, 2024

Throughout this exercise, you use Amazon Q Developer in SageMaker Studio for various stages of the development lifecycle and experience firsthand how this natural language assistant can help even the most experienced data scientists or ML engineers streamline the development process and accelerate time-to-value.

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AWS Machine Learning Blog

JANUARY 13, 2023

Based on experimental results, the collaborative models demonstrated a 4% improvement in categorizing molecules as either pharmacologically or toxicologically active or inactive. The FedML open-source library supports federated ML use cases for edge as well as cloud.

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Machine Learning ML Algorithm Data Scientist 84

Snorkel AI

OCTOBER 27, 2023

How to fine-tune and customize LLMs Hoang Tran, ML Engineer at Snorkel AI, outlined how he saw LLMs creating value in enterprise environments. The first categorizes instructions, while the second assesses the quality of responses.

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LLM Data Scientist Machine Learning Large Language Models 59

AWS Machine Learning Blog

SEPTEMBER 1, 2023

After the completion of the research phase, the data scientists need to collaborate with ML engineers to create automations for building (ML pipelines) and deploying models into production using CI/CD pipelines. Security SMEs review the architecture based on business security policies and needs.

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Generative AI Prompt Engineering Prompt Engineer ML 120

Snorkel AI

JUNE 30, 2023

And usually what ends up happening is that some poor data scientist or ML engineer has to manually troubleshoot this in a Jupyter Notebook. So this path on the right side of the production icon is what we’re calling ML observability. This could lead to performance drifts.

Machine Learning 52

Machine Learning ML Data Drift Data Quality 52

Snorkel AI

OCTOBER 27, 2023

How to fine-tune and customize LLMs Hoang Tran, ML Engineer at Snorkel AI, outlined how he saw LLMs creating value in enterprise environments. The first categorizes instructions, while the second assesses the quality of responses.

LLM 52

LLM Data Scientist Machine Learning Large Language Models 52

Snorkel AI

JUNE 30, 2023

And usually what ends up happening is that some poor data scientist or ML engineer has to manually troubleshoot this in a Jupyter Notebook. So this path on the right side of the production icon is what we’re calling ML observability. This could lead to performance drifts.

Machine Learning 52

Machine Learning ML Data Drift Data Quality 52

Snorkel AI

JUNE 30, 2023

And usually what ends up happening is that some poor data scientist or ML engineer has to manually troubleshoot this in a Jupyter Notebook. So this path on the right side of the production icon is what we’re calling ML observability. This could lead to performance drifts.

Machine Learning 52

Machine Learning ML Data Drift Data Quality 52

AWS Machine Learning Blog

FEBRUARY 5, 2024

SageMaker geospatial capabilities make it easy for data scientists and machine learning (ML) engineers to build, train, and deploy models using geospatial data. In this post, we explore how HSR. fillna(0) df1['totalpixels'] = df1.sum(axis=1) fillna(0) allDf[col] = allDf.groupby(idCols + ['year'])[col].transform(lambda

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ML Machine Learning Python Software Engineer 99

Snorkel AI

MAY 26, 2023

The data scientists will start with experimentation, and then once they find some insights and the experiment is successful, then they hand over the baton to data engineers and ML engineers that help them put these models into production. And these are not really compute-intensive for most structured ML problems.

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ML Python Machine Learning Data Scientist 52

Snorkel AI

MAY 26, 2023

The data scientists will start with experimentation, and then once they find some insights and the experiment is successful, then they hand over the baton to data engineers and ML engineers that help them put these models into production. And these are not really compute-intensive for most structured ML problems.

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ML Python Machine Learning Data Scientist 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. Machine learning operations (MLOps) are a set of practices that automate and simplify machine learning (ML) workflows and deployments.

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Machine Learning ML Automation Data Scientist 52

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

AWS Machine Learning Blog

JUNE 14, 2023

Data Set Characteristics Multivariate Number of Instances 48842 Area Social Attribute Characteristics: Categorical, Integer Number of Attributes: 14 Date Donated 1996-05-01 Associated Tasks: Classification Missing Values? The following table summarizes the key components of the dataset.

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Bugra Akyildiz

MARCH 24, 2024

In the post, they talk about advantages and diadvantages of Metaflow: Advantages User-friendly API: Metaflow offers a human-readable API that simplifies the process of building and managing ML workflows.

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Machine Learning Algorithm Data Scientist LLM 52

AWS Machine Learning Blog

DECEMBER 4, 2024

Text classification : Build faster models for categorizing high volumes of concurrent support tickets, emails, or customer feedback at scale; or for efficiently routing requests to larger models when necessary. This allows you to categorize and filter your interactions later.

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Metadata Generative AI Categorization Data Scientist 109

AWS Machine Learning Blog

OCTOBER 11, 2024

Content categorization – Metadata can provide information about the content or category of a document, such as the subject matter, domain, or topic. Ginni Malik is a Senior Data & ML Engineer with AWS Professional Services. Outside of work, he enjoys playing adventure sports and spending time with family.

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