ODSC - Open Data Science

MARCH 27, 2023

Learn about the flow, difficulties, and tools for performing ML clustering at scale Ori Nakar | Principal Engineer, Threat Research | Imperva Given that there are billions of daily botnet attacks from millions of different IPs, the most difficult challenge of botnet detection is choosing the most relevant data.

Machine Learning 98

Machine Learning Data Ingestion Explainability Deep Learning 98

ODSC - Open Data Science

FEBRUARY 22, 2024

Andre Franca | CTO | connectedFlow Join this session to demystify the world of Causal AI, with a focus on understanding cause-and-effect relationships within data to drive optimal decisions. By the end of this session, you’ll have a practical blueprint to efficiently harness feature stores within ML workflows.

Machine Learning 52

Machine Learning Data Science Python Data Ingestion 52

Towards AI

MARCH 28, 2024

Elymsyr wants to develop new projects to improve their ML, RL, computer vision, and co-working skills. In this article, the author aims to share his experience as a software engineer using a question-driven approach to designing an LLM-powered application. Dianasanimals is looking for students to test several free chatbots.

Large Language Models 70

Large Language Models Prompt Engineer Prompt Engineering AI 70

ODSC - Open Data Science

SEPTEMBER 17, 2024

Large Model Quality and Evaluation Anoop Sinha | Research Director, AI & Future Technologies | Google Large model development faces many challenges when it comes to ML quality and evaluation, including the coverage, scale, and wide use cases for what LLMs are used for. Check out a few of them below.

LLM 52

LLM Large Language Models Metadata Data Ingestion 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

JULY 19, 2023

Machine Learning Operations (MLOps) can significantly accelerate how data scientists and ML engineers meet organizational needs. A well-implemented MLOps process not only expedites the transition from testing to production but also offers ownership, lineage, and historical data about ML artifacts used within the team.

Machine Learning 52

Machine Learning Data Drift Data Science ML Engineer 52

The MLOps Blog

JUNE 27, 2023

Alignment to other tools in the organization’s tech stack Consider how well the MLOps tool integrates with your existing tools and workflows, such as data sources, data engineering platforms, code repositories, CI/CD pipelines, monitoring systems, etc. and Pandas or Apache Spark DataFrames.

Metadata 134

Metadata Machine Learning Data Scientist Data Quality 134

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.

ML 98

ML Machine Learning Metadata Automation 98

AWS Machine Learning Blog

OCTOBER 23, 2024

At its core, Amazon Bedrock provides the foundational infrastructure for robust performance, security, and scalability for deploying machine learning (ML) models. 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.

Generative AI 82

Generative AI IDP LLM Automation 82

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. SageMaker Training is a comprehensive, fully managed ML service that enables scalable model training. Customizable environment – We use custom containers in training jobs.

ML 94

ML Large Language Models Machine Learning Data Ingestion 94

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.

Machine Learning 69

Machine Learning Data Scientist ML Metadata 69