ODSC - Open Data Science

MARCH 27, 2023

Scaling AI/ML Workloads with Ray Kai Fricke | Senior Software Engineer | Anyscale Inc. If so, when and who should perform them? And, Most importantly, what is the point of all this governance, and how much is too much?

Machine Learning 98

Machine Learning Data Ingestion Data Science Deep Learning 98

AWS Machine Learning Blog

DECEMBER 11, 2024

By moving our core infrastructure to Amazon Q, we no longer needed to choose a large language model (LLM) and optimize our use of it, manage Amazon Bedrock agents, a vector database and semantic search implementation, or custom pipelines for data ingestion and management.

Generative AI 108

Generative AI Data Ingestion Chatbots Software Engineer 108

AWS Machine Learning Blog

APRIL 26, 2023

Data engineering – Identifies the data sources, sets up data ingestion and pipelines, and prepares data using Data Wrangler. Data science – The heart of ML EBA and focuses on feature engineering, model training, hyperparameter tuning, and model validation. Connect with him on LinkedIn.

ML 114

ML Machine Learning Data Science Data Drift 114

ODSC - Open Data Science

FEBRUARY 22, 2024

Tutorial: Introduction to Apache Arrow and Apache Parquet, using Python and Pyarrow Andrew Lamb | Chair of the Apache Arrow Program Management Committee | Staff Software Engineer | InfluxData Build new skills in Apache Arrow and Apache Parquet in this upcoming ODSC East tutorial.

Machine Learning 52

Machine Learning Data Science Python ML 52

Pickl AI

APRIL 6, 2023

The following blog will help you know about the Azure Data Engineering Job Description, salary, and certification course. Data Engineering is one of the most productive job roles today because it imbibes both the skills required for software engineering and programming and advanced analytics needed by Data Scientists.

ETL 52

ETL Big Data Data Ingestion Software Engineer 52

ODSC - Open Data Science

SEPTEMBER 17, 2024

Streamlining Unstructured Data for Retrieval Augmented Generation Matt Robinson | Open Source Tech Lead | Unstructured In this talk, you’ll explore the complexities of handling unstructured data, and offer practical strategies for extracting usable text and metadata from unstructured data.

LLM 52

LLM Large Language Models Metadata Data Science 52

The MLOps Blog

FEBRUARY 13, 2025

The solution lies in systems that can handle high-throughput data ingestion while providing accurate, real-time insights. For example, live tracking of GPU utilization or memory usage can reveal early signs of bottlenecks or out-of-memory errors, allowing engineers to proactively adjust course. Tools like neptune.ai

Data Ingestion 52

Data Ingestion Automation Software Engineer Metadata 52

ODSC - Open Data Science

JULY 19, 2023

Personas associated with this phase may be primarily Infrastructure Team but may also include all of Data Engineers, Machine Learning Engineers, and Data Scientists. Model Development (Inner Loop): The inner loop element consists of your iterative data science workflow.

Machine Learning 52

Machine Learning Data Drift Data Science Data Scientist 52

The MLOps Blog

JUNE 27, 2023

Core features of end-to-end MLOps platforms End-to-end MLOps platforms combine a wide range of essential capabilities and tools, which should include: Data management and preprocessing : Provide capabilities for data ingestion, storage, and preprocessing, allowing you to efficiently manage and prepare data for training and evaluation.

Machine Learning 134

Machine Learning Metadata Data Scientist Data Quality 134

The MLOps Blog

MAY 9, 2023

The components comprise implementations of the manual workflow process you engage in for automatable steps, including: Data ingestion (extraction and versioning). Data validation (writing tests to check for data quality). Data preprocessing. Let’s briefly go over each of the components below. CSV, Parquet, etc.)

ML 98

ML Machine Learning Metadata Data Science 98

AWS Machine Learning Blog

OCTOBER 23, 2024

Data flow Here is an example of this data flow for an Agent Creator pipeline that involves data ingestion, preprocessing, and vectorization using Chunker and Embedding Snaps. David Dellsperger is a Senior Staff Software Engineer and Technical Lead of the Agent Creator product at SnapLogic.

Generative AI 89

Generative AI IDP LLM Automation 89

Unite.AI

JANUARY 13, 2025

My journey in the database field spans over 15 years, including six years as a software engineer at Oracle, where I was a founding member of the Oracle 12c Multitenant Database team. By leveraging GPU acceleration, we've dramatically reduced index-building time, enabling faster data ingestion and improved data visibility.

Data Scarcity 147

Data Scarcity ETL Data Ingestion Software Engineer 147

AWS Machine Learning Blog

SEPTEMBER 19, 2024

This helps address the requirements of the generative AI fine-tuning lifecycle, from data ingestion and multi-node fine-tuning to inference and evaluation. Special thanks to Kartikay Khandelwal (Software Engineer at Meta), Eli Uriegas (Engineering Manager at Meta), Raj Devnath (Sr.

ML 94

ML Large Language Models Machine Learning Data Ingestion 94

The MLOps Blog

MARCH 21, 2023

Automation You want the ML models to keep running in a healthy state without the data scientists incurring much overhead in moving them across the different lifecycle phases. It would make sure that all development and deployment workflows use good software engineering practices. My Story DevOps Engineers Who they are?

Machine Learning 69

Machine Learning Data Scientist ML Metadata 69

AWS Machine Learning Blog

NOVEMBER 20, 2024

It involves two key workflows: data ingestion and text generation. The data ingestion workflow creates semantic embeddings for documents and questions, storing document embeddings in a vector database. This bucket is designated as the knowledge base data source.

Automation 110

Automation Data Ingestion Auto-complete Software Engineer 110

AWS Machine Learning Blog

DECEMBER 4, 2024

Amazon Kendra GenAI Index addresses common challenges in building retrievers for generative AI assistants, including data ingestion, model selection, and integration with various generative AI tools. Aakash Upadhyay is a Senior Software Engineer at AWS, specializing in building scalable NLP and Generative AI cloud services.

Metadata 113

Metadata Generative AI Data Ingestion Software Engineer 113

AWS Machine Learning Blog

NOVEMBER 7, 2024

When inference data is ingested on Amazon S3, EventBridge automatically runs the inference pipeline. This automated workflow streamlines the entire process, from data ingestion to inference, reducing manual interventions and minimizing the risk of errors. In his spare time, Mones enjoys operatic singing and scuba diving.

ML 112

ML Machine Learning Automation Data Scientist 112