IBM Journey to AI blog

MAY 9, 2023

It offers its users advanced machine learning, data management , and generative AI capabilities to train, validate, tune and deploy AI systems across the business with speed, trusted data, and governance. It helps facilitate the entire data and AI lifecycle, from data preparation to model development, deployment and monitoring.

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AWS Machine Learning Blog

MAY 31, 2024

Data preparation Before creating a knowledge base using Knowledge Bases for Amazon Bedrock, it’s essential to prepare the data to augment the FM in a RAG implementation. You can help safeguard sensitive data that’s ingested by CloudWatch Logs by using log group data protection policies.

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AWS Machine Learning Blog

NOVEMBER 22, 2023

And although generative AI has appeared in previous events, this year we’re taking it to the next level. Explore examples to estimate the severity and likelihood of potential events that could be harmful.

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Alation

FEBRUARY 13, 2020

Data Literacy—Many line-of-business people have responsibilities that depend on data analysis but have not been trained to work with data. Their tendency is to do just enough data work to get by, and to do that work primarily in Excel spreadsheets. People – Which data stakeholders will you bring on board at what times?

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Data Governance Data Analysis Data Analysis Data Preparation 52

Pickl AI

NOVEMBER 4, 2024

Role of Data Engineers in the Data Ecosystem Data Engineers play a crucial role in the data ecosystem by bridging the gap between raw data and actionable insights. They are responsible for building and maintaining data architectures, which include databases, data warehouses, and data lakes.

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The MLOps Blog

JUNE 27, 2023

See also Thoughtworks’s guide to Evaluating MLOps Platforms End-to-end MLOps platforms End-to-end MLOps platforms provide a unified ecosystem that streamlines the entire ML workflow, from data preparation and model development to deployment and monitoring.

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Snorkel AI

MARCH 2, 2023

Data preparation, train and tune, deploy and monitor. We have data pipelines and data preparation. Because that’s the data that’s going to be training the model. And if the data has those biases in them, the trained model will also have those biases embedded in it. It can cover the gamut.

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Snorkel AI

MARCH 2, 2023

Data preparation, train and tune, deploy and monitor. We have data pipelines and data preparation. Because that’s the data that’s going to be training the model. And if the data has those biases in them, the trained model will also have those biases embedded in it. It can cover the gamut.

Machine Learning 52

Machine Learning Machine Learning Data Preparation AI 52

Pickl AI

SEPTEMBER 5, 2024

Storage Solutions: Secure and scalable storage options like Azure Blob Storage and Azure Data Lake Storage. Key features and benefits of Azure for Data Science include: Scalability: Easily scale resources up or down based on demand, ideal for handling large datasets and complex computations.

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AWS Machine Learning Blog

JANUARY 26, 2024

Plan for rollback and recovery from production security events and service disruptions such as prompt injection, training data poisoning, model denial of service, and model theft early on, and define the mitigations you will use as you define application requirements.

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