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Data Drift

Data Scientist

DevOps

AIOps vs. MLOps: Harnessing big data for “smarter” ITOPs

IBM Journey to AI blog

AUGUST 12, 2024

Consequently, AIOps is designed to harness data and insight generation capabilities to help organizations manage increasingly complex IT stacks. MLOps platforms are primarily used by data scientists, ML engineers, DevOps teams and ITOps personnel who use them to automate and optimize ML models and get value from AI initiatives faster.

Big Data

Big Data DevOps Automation Machine Learning

Modernizing data science lifecycle management with AWS and Wipro

AWS Machine Learning Blog

JANUARY 5, 2024

Many organizations have been using a combination of on-premises and open source data science solutions to create and manage machine learning (ML) models. Data science and DevOps teams may face challenges managing these isolated tool stacks and systems.

Data Science

Data Science Data Drift DevOps Auto-complete

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How to Achieve High-Accuracy Results When Using LLMs

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Trending Sources

Top MLOps Tools Guide: Weights & Biases, Comet and More

Unite.AI

JUNE 24, 2024

It combines principles from DevOps, such as continuous integration, continuous delivery, and continuous monitoring, with the unique challenges of managing machine learning models and datasets. Model Training Frameworks This stage involves the process of creating and optimizing predictive models with labeled and unlabeled data.

Data Drift

Data Drift Machine Learning Data Scientist ML

Webinars

How to Achieve High-Accuracy Results When Using LLMs

Relevance, Reach, Revenue: How to Turn Marketing Trends From Hype to High-Impact

MORE WEBINARS

How Axfood enables accelerated machine learning throughout the organization using Amazon SageMaker

AWS Machine Learning Blog

FEBRUARY 27, 2024

Each product translates into an AWS CloudFormation template, which is deployed when a data scientist creates a new SageMaker project with our MLOps blueprint as the foundation. These are essential for monitoring data and model quality, as well as feature attributions. Alerts are raised whenever anomalies are detected.

Machine Learning

Machine Learning DevOps Data Scientist Data Quality

MLOps Landscape in 2023: Top Tools and Platforms

The MLOps Blog

JUNE 27, 2023

Some popular end-to-end MLOps platforms in 2023 Amazon SageMaker Amazon SageMaker provides a unified interface for data preprocessing, model training, and experimentation, allowing data scientists to collaborate and share code easily. Check out the Kubeflow documentation.

Machine Learning

Machine Learning Metadata Data Scientist Data Quality

Create SageMaker Pipelines for training, consuming and monitoring your batch use cases

AWS Machine Learning Blog

APRIL 21, 2023

If the model performs acceptably according to the evaluation criteria, the pipeline continues with a step to baseline the data using a built-in SageMaker Pipelines step. For the data drift Model Monitor type, the baselining step uses a SageMaker managed container image to generate statistics and constraints based on your training data.

Data Drift

Data Drift Metadata Data Quality ML

Deliver your first ML use case in 8–12 weeks

AWS Machine Learning Blog

APRIL 26, 2023

The first is by using low-code or no-code ML services such as Amazon SageMaker Canvas , Amazon SageMaker Data Wrangler , Amazon SageMaker Autopilot , and Amazon SageMaker JumpStart to help data analysts prepare data, build models, and generate predictions. Monitoring setup (model, data drift).

ML Machine Learning Data Science Data Drift

The Ever-growing Importance of MLOps: The Transformative Effect of DataRobot

DataRobot Blog

FEBRUARY 11, 2022

These agents apply the concept familiar in the DevOps world—to run models in their preferred environments while monitoring all models centrally. DataRobot’s MLOps product offers a host of features designed to transform organizations’ user experience, firstly, through its model-monitoring agents.

Data Drift

Data Drift Machine Learning DevOps Data Scientist

Machine Learning Operations (MLOPs) with Azure Machine Learning

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

Machine Learning Data Drift Data Science Data Scientist

MLOps for batch inference with model monitoring and retraining using Amazon SageMaker, HashiCorp Terraform, and GitLab CI/CD

AWS Machine Learning Blog

AUGUST 29, 2023

This architecture design represents a multi-account strategy where ML models are built, trained, and registered in a central model registry within a data science development account (which has more controls than a typical application development account). The data scientist can review and approve the new version of the model independently.

Data Scientist

Data Scientist Data Quality Python ML

Promote pipelines in a multi-environment setup using Amazon SageMaker Model Registry, HashiCorp Terraform, GitHub, and Jenkins CI/CD

AWS Machine Learning Blog

NOVEMBER 9, 2023

Building out a machine learning operations (MLOps) platform in the rapidly evolving landscape of artificial intelligence (AI) and machine learning (ML) for organizations is essential for seamlessly bridging the gap between data science experimentation and deployment while meeting the requirements around model performance, security, and compliance.

Data Drift

Data Drift Auto-complete ML Automation

Learnings From Building the ML Platform at Stitch Fix

The MLOps Blog

AUGUST 3, 2023

Stefan is a software engineer, data scientist, and has been doing work as an ML engineer. He also ran the data platform in his previous company and is also co-creator of open-source framework, Hamilton. To a junior data scientist, it doesn’t matter if you’re using Airflow, Prefect , Dexter.

ML Data Scientist Software Engineer Machine Learning

Real-World MLOps Examples: End-To-End MLOps Pipeline for Visual Search at Brainly

The MLOps Blog

MARCH 28, 2023

The DevOps and Automation Ops departments are under the infrastructure team. Brainly’s journey toward MLOps Since the early days of ML at Brainly, infrastructure, and engineering teams have encouraged data scientists and machine learning engineers working on projects to use best practices for structuring their projects and code bases.

Machine Learning

Machine Learning Data Scientist Automation ML

How to Build an End-To-End ML Pipeline

The MLOps Blog

MAY 9, 2023

The platform typically includes components for the ML ecosystem like data management, feature stores, experiment trackers, a model registry, a testing environment, model serving, and model management. It checks the data for quality issues and detects outliers and anomalies. The staging environment is for integration testing.

ML Machine Learning Metadata Data Science

AIOps vs. MLOps: Harnessing big data for “smarter” ITOPs

Modernizing data science lifecycle management with AWS and Wipro

Webinars

Trending Sources

Top MLOps Tools Guide: Weights & Biases, Comet and More

Webinars

How Axfood enables accelerated machine learning throughout the organization using Amazon SageMaker

MLOps Landscape in 2023: Top Tools and Platforms

Create SageMaker Pipelines for training, consuming and monitoring your batch use cases

Deliver your first ML use case in 8–12 weeks

The Ever-growing Importance of MLOps: The Transformative Effect of DataRobot

Machine Learning Operations (MLOPs) with Azure Machine Learning

MLOps for batch inference with model monitoring and retraining using Amazon SageMaker, HashiCorp Terraform, and GitLab CI/CD

Promote pipelines in a multi-environment setup using Amazon SageMaker Model Registry, HashiCorp Terraform, GitHub, and Jenkins CI/CD

Learnings From Building the ML Platform at Stitch Fix

Real-World MLOps Examples: End-To-End MLOps Pipeline for Visual Search at Brainly

How to Build an End-To-End ML Pipeline

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