ODSC - Open Data Science

MARCH 27, 2023

The Next Generation of Low-Code Machine Learning Devvret Rishi | Co-founder and Chief Product Officer | Predibase In this session, you’ll explore declarative machine learning, a configuration-based modeling interface, which provides more flexibility and simplicity when implementing cutting-edge machine learning.

Machine Learning 98

Machine Learning Data Ingestion Explainability ML 98

AWS Machine Learning Blog

JANUARY 17, 2023

Amazon SageMaker Studio is a fully integrated development environment (IDE) for machine learning (ML) partly based on JupyterLab 3. Studio provides a web-based interface to interactively perform ML development tasks required to prepare data and build, train, and deploy ML models. AWS CDK scripts.

Software Engineer 79

Software Engineer ML Engineer ML Machine Learning 79

AWS Machine Learning Blog

NOVEMBER 16, 2023

Amazon SageMaker provides purpose-built tools for machine learning operations (MLOps) to help automate and standardize processes across the ML lifecycle. In this post, we describe how Philips partnered with AWS to develop AI ToolSuite—a scalable, secure, and compliant ML platform on SageMaker.

Data Scientist 92

Data Scientist ML Data Science Automation 92

AWS Machine Learning Blog

DECEMBER 13, 2023

Machine learning (ML) models do not operate in isolation. To deliver value, they must integrate into existing production systems and infrastructure, which necessitates considering the entire ML lifecycle during design and development. GitHub serves as a centralized location to store, version, and manage your ML code base.

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ML Automation Metadata Software Development 105

AWS Machine Learning Blog

MAY 30, 2024

Additionally, VitechIQ includes metadata from the vector database (for example, document URLs) in the model’s output, providing users with source attribution and enhancing trust in the generated answers. Prompt engineering Prompt engineering is crucial for the knowledge retrieval system.

Chatbots 107

Chatbots Prompt Engineer Prompt Engineering Large Language Models 107

TheSequence

JULY 12, 2023

👤 Quick bio Tell us a bit about yourself: your background, current role, and how you got started in machine learning. In 2018, I joined Cruise and cofounded the ML Infrastructure team there. In May 2022, I started Sematic to bring my experience in ML infrastructure to the industry in an open-source manner.

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ML Python Machine Learning Metadata 97

AWS Machine Learning Blog

MAY 5, 2023

Solution overview Ground Truth is a fully self-served and managed data labeling service that empowers data scientists, machine learning (ML) engineers, and researchers to build high-quality datasets. For our example use case, we work with the Fashion200K dataset , released at ICCV 2017. Then import the relevant modules.

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Metadata Computer Vision Machine Learning Data Scientist 80

The MLOps Blog

JANUARY 26, 2024

Training and evaluating models is just the first step toward machine-learning success. For this, we have to build an entire machine-learning system around our models that manages their lifecycle, feeds properly prepared data into them, and sends their output to downstream systems. But what is an ML pipeline?

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Machine Learning Metadata ML Python 72

AWS Machine Learning Blog

OCTOBER 20, 2023

Customers of every size and industry are innovating on AWS by infusing machine learning (ML) into their products and services. Recent developments in generative AI models have further sped up the need of ML adoption across industries.

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ML Data Scientist ML Engineer Data Science 106

AWS Machine Learning Blog

SEPTEMBER 1, 2023

ML operationalization summary As defined in the post MLOps foundation roadmap for enterprises with Amazon SageMaker , ML and operations (MLOps) is the combination of people, processes, and technology to productionize machine learning (ML) solutions efficiently.

Generative AI 107

Generative AI Prompt Engineer Prompt Engineering AI 107

AWS Machine Learning Blog

MARCH 30, 2023

Metadata of the match is processed within the AWS Lambda function MetaDataIngestion , while positional data is ingested using the AWS Fargate container called MatchLink. His skills and areas of expertise include application development, data science, machine learning, and big data.

Machine Learning 76

Machine Learning Data Scientist Metadata Data Science 76

The MLOps Blog

MARCH 28, 2023

In this second installment of the series “Real-world MLOps Examples,” Paweł Pęczek , Machine Learning Engineer at Brainly , will walk you through the end-to-end Machine Learning Operations (MLOps) process in the Visual Search team at Brainly. quality attributes) and metadata enrichment (e.g.,

Machine Learning 59

Machine Learning Automation Data Scientist ML 59

AWS Machine Learning Blog

JUNE 8, 2023

This enables you to begin machine learning (ML) quickly. It includes the FLAN-T5-XL model , an LLM deployed into a deep learning container. A SageMaker real-time inference endpoint enables fast, scalable deployment of ML models for predicting events. He leads the NYC machine learning and AI meetup.

Generative AI 77

Generative AI LLM Large Language Models Machine Learning 77

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. Many machine learning projects fail to deliver practical results due to difficulties in automation and deployment.

Machine Learning 52

Machine Learning ML Automation Data Scientist 52

AWS Machine Learning Blog

SEPTEMBER 29, 2023

In this post, we illustrate how to use a segmentation machine learning (ML) model to identify crop and non-crop regions in an image. Identifying crop regions is a core step towards gaining agricultural insights, and the combination of rich geospatial data and ML can lead to insights that drive decisions and actions.

Machine Learning 116

Machine Learning Data Scientist ML Python 116

The MLOps Blog

MARCH 15, 2023

Based on the McKinsey survey , 56% of orgs today are using machine learning in at least one business function. This includes the tools and techniques we used to streamline the ML model development and deployment processes, as well as the measures taken to monitor and maintain models in a production environment. S3 buckets.

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ETL Data Drift Machine Learning ML 52

The MLOps Blog

OCTOBER 3, 2023

This article was originally an episode of the ML Platform Podcast , a show where Piotr Niedźwiedź and Aurimas Griciūnas, together with ML platform professionals, discuss design choices, best practices, example tool stacks, and real-world learnings from some of the best ML platform professionals.

ML 52

ML Data Scientist Machine Learning Data Science 52

AWS Machine Learning Blog

FEBRUARY 28, 2024

Structured Query Language (SQL) is a complex language that requires an understanding of databases and metadata. Third, despite the larger adoption of centralized analytics solutions like data lakes and warehouses, complexity rises with different table names and other metadata that is required to create the SQL for the desired sources.

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Metadata LLM Generative AI NLP 116

The MLOps Blog

JANUARY 23, 2023

Machine Learning Operations (MLOps): Overview, Definition, and Architecture” By Dominik Kreuzberger, Niklas Kühl, Sebastian Hirschl Great stuff. Came to ML from software. Founded neptune.ai , a modular MLOps component for ML metadata store , aka “experiment tracker + model registry”.

DevOps 59

DevOps Metadata Software Engineer Data Scientist 59

The MLOps Blog

MAY 10, 2023

When working on real-world machine learning (ML) use cases, finding the best algorithm/model is not the end of your responsibilities. Reusability & reproducibility: Building ML models is time-consuming by nature. Save vs package vs store ML models Although all these terms look similar, they are not the same.

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Python Metadata ML Deep Learning 105

The MLOps Blog

AUGUST 3, 2023

This article was originally an episode of the ML Platform Podcast , a show where Piotr Niedźwiedź and Aurimas Griciūnas, together with ML platform professionals, discuss design choices, best practices, example tool stacks, and real-world learnings from some of the best ML platform professionals. Stefan: Yeah.

ML 52

ML Data Scientist Software Engineer Machine Learning 52

The MLOps Blog

JUNE 27, 2023

How to evaluate MLOps tools and platforms Like every software solution, evaluating MLOps (Machine Learning Operations) tools and platforms can be a complex task as it requires consideration of varying factors. For example, if you use AWS, you may prefer Amazon SageMaker as an MLOps platform that integrates with other AWS services.

Machine Learning 134

Machine Learning Metadata Data Scientist Data Quality 134

The MLOps Blog

MARCH 14, 2023

When working on real-world ML projects , you come face-to-face with a series of obstacles. The ml model reproducibility problem is one of them. You’ll learn: 1 Why is reproducibility important in machine learning? Why is reproducibility important in machine learning? 3 What was the solution?

ML 52

ML Machine Learning Metadata ML Engineer 52

AWS Machine Learning Blog

JUNE 27, 2023

When machine learning (ML) models are deployed into production and employed to drive business decisions, the challenge often lies in the operation and management of multiple models. That is where Provectus , an AWS Premier Consulting Partner with competencies in Machine Learning, Data & Analytics, and DevOps, stepped in.

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DevOps ML Machine Learning Automation 99

AWS Machine Learning Blog

MAY 15, 2024

Next, we present the solution architecture and process flows for machine learning (ML) model building, deployment, and inferencing. We end with lessons learned. Here, Amazon SageMaker Ground Truth allowed ML engineers to easily build the human-in-the-loop workflow (step v). with minimal effort.

Generative AI 107

Generative AI AI AI Machine Learning 107

AWS Machine Learning Blog

MARCH 1, 2023

Statistical methods and machine learning (ML) methods are actively developed and adopted to maximize the LTV. These challenges are typically faced when we implement ML solutions and deploy them into a production environment. We can utilize external events as ML features along with the game-related logs and data.

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Automation ETL Data Drift ML 92

Snorkel AI

JULY 5, 2023

Abhishek Ratna, in AI ML marketing, and TensorFlow developer engineer Robert Crowe, both from Google, spoke as part of a panel entitled “Practical Paths to Data-Centricity in Applied AI” at Snorkel AI’s Future of Data-Centric AI virtual conference in August 2022. And it’s my absolute honor and pleasure to speak alongside Robert Crowe.

Large Language Models 52

Large Language Models Metadata AI AI 52

Snorkel AI

JULY 5, 2023

Abhishek Ratna, in AI ML marketing, and TensorFlow developer engineer Robert Crowe, both from Google, spoke as part of a panel entitled “Practical Paths to Data-Centricity in Applied AI” at Snorkel AI’s Future of Data-Centric AI virtual conference in August 2022. And it’s my absolute honor and pleasure to speak alongside Robert Crowe.

Large Language Models 52

Large Language Models Metadata AI AI 52

AWS Machine Learning Blog

AUGUST 31, 2023

As Artificial Intelligence (AI) and Machine Learning (ML) technologies have become mainstream, many enterprises have been successful in building critical business applications powered by ML models at scale in production. They provide a fact sheet of the model that is important for model governance.

ML 95

ML Data Scientist Machine Learning Automation 95

AWS Machine Learning Blog

NOVEMBER 30, 2023

It also integrates with Machine Learning and Operation (MLOps) workflows in Amazon SageMaker to automate and scale the ML lifecycle. Here you can provide the metadata for this model hosting information along with the input format/template your specific model expects. What is FMEval?

Large Language Models 93

Large Language Models Algorithm LLM Responsible AI 93

AWS Machine Learning Blog

JUNE 13, 2023

This post is co-written with Jad Chamoun, Director of Engineering at Forethought Technologies, Inc. and Salina Wu, Senior ML Engineer at Forethought Technologies, Inc. We defined logic that would take in model metadata, format the endpoint deterministically based on the metadata, and check whether the endpoint existed.

Generative AI 67

Generative AI Auto-complete AI Modeling Machine Learning 67

AWS Machine Learning Blog

AUGUST 31, 2023

As Artificial Intelligence (AI) and Machine Learning (ML) technologies have become mainstream, many enterprises have been successful in building critical business applications powered by ML models at scale in production. They provide a fact sheet of the model that is important for model governance.

ML 52

ML Data Scientist Machine Learning Automation 52

AWS Machine Learning Blog

JUNE 14, 2023

Amazon SageMaker is a fully managed service to prepare data and build, train, and deploy machine learning (ML) models for any use case with fully managed infrastructure, tools, and workflows. Autopilot eliminates the heavy lifting of building ML models. Projects are provisioned using AWS Service Catalog products.

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ML Automation Data Scientist DevOps 82

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.

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ML Machine Learning Metadata Automation 98

Dlabs.ai

JUNE 11, 2019

You know, when a company decides to “really get in on this ML stuff” by slapping together some free, existing solutions (maybe even some code on GitHub) because “someone must have had the same problem before.” A cautionary tale of cutting corners in machine learning. You’re doing ok, but ok is not what you were aiming for.

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AWS Machine Learning Blog

APRIL 19, 2024

For instructions on installing the SageMaker controller, refer to Machine Learning with the ACK SageMaker Controller. You need access to accelerated instances (GPUs) for hosting the LLMs. Refer to the guidance provided in the API documentation for more details. amazonaws.com/huggingface-pytorch-tgi-inference:2.0.1-tgi0.9.3-gpu-py39-cu118-ubuntu20.04

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Metadata LLM Software Development Machine Learning 101

AWS Machine Learning Blog

NOVEMBER 29, 2023

With these tools in hand, the next challenge is to integrate LLM evaluation into the Machine Learning and Operation (MLOps) lifecycle to achieve automation and scalability in the process. In this post, we show you how to integrate Amazon SageMaker Clarify LLM evaluation with Amazon SageMaker Pipelines to enable LLM evaluation at scale.

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LLM Data Scientist Algorithm Generative AI 87