AWS Machine Learning Blog

NOVEMBER 29, 2023

You can use this notebook job step to easily run notebooks as jobs with just a few lines of code using the Amazon SageMaker Python SDK. Data scientists currently use SageMaker Studio to interactively develop their Jupyter notebooks and then use SageMaker notebook jobs to run these notebooks as scheduled jobs.

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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.

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Heartbeat

JUNE 12, 2023

Key Challenges in ML Model Monitoring in Production Data Drift and Concept Drift Data and concept drift are two common types of drift that can occur in machine-learning models over time. Data drift refers to a change in the input data distribution that the model receives.

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AWS Machine Learning Blog

MARCH 1, 2023

Challenges In this section, we discuss challenges around various data sources, data drift caused by internal or external events, and solution reusability. For example, Amazon Forecast supports related time series data like weather, prices, economic indicators, or promotions to reflect internal and external related events.

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ODSC - Open Data Science

JULY 19, 2023

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. For the customer, this helps them reduce the time it takes to bootstrap a new data science project and get it to production.

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DataRobot Blog

FEBRUARY 28, 2023

A seamless user experience when deploying and monitoring DataRobot models to Snowflake Monitoring service health, drift, and accuracy of DataRobot models in Snowflake “Organizations are looking for mature data science platforms that can scale to the size of their entire business. launch event on March 16th.

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AWS Machine Learning Blog

AUGUST 29, 2023

GitLab CI/CD serves as the macro-orchestrator, orchestrating model build and model deploy pipelines, which include sourcing, building, and provisioning Amazon SageMaker Pipelines and supporting resources using the SageMaker Python SDK and Terraform. SageMaker Pipelines serves as the orchestrator for ML model training and inference workflows.

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Towards AI

APRIL 1, 2023

However, the data in the real world is constantly changing, and this can affect the accuracy of the model. This is known as data drift, and it can lead to incorrect predictions and poor performance. In this blog post, we will discuss how to detect data drift using the Python library TorchDrift.

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Snorkel AI

NOVEMBER 1, 2023

When Vertex Model Monitoring detects data drift, input feature values are submitted to Snorkel Flow, enabling ML teams to adapt labeling functions quickly, retrain the model, and then deploy the new model with Vertex AI. See what Snorkel can do to accelerate your data science and machine learning teams. Book a demo today.

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ODSC - Open Data Science

OCTOBER 11, 2023

Machine learning models are only as good as the data they are trained on. Even with the most advanced neural network architectures, if the training data is flawed, the model will suffer. Data issues like label errors, outliers, duplicates, data drift, and low-quality examples significantly hamper model performance.

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DataRobot Blog

MARCH 10, 2022

With Snowflake’s newest feature release, Snowpark , developers can now quickly build and scale data-driven pipelines and applications in their programming language of choice, taking full advantage of Snowflake’s highly performant and scalable processing engine that accelerates the traditional data engineering and machine learning life cycles.

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Bugra Akyildiz

MARCH 12, 2023

Uber wrote about how they build a data drift detection system. To quantify the impact of such data incidents, the Fares data science team has built a simulation framework that replicates corrupted data from real production incidents and assesses the impact on the fares data model performance.

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DataRobot Blog

DECEMBER 15, 2022

By simplifying Time Series Forecasting models and accelerating the AI lifecycle, DataRobot can centralize collaboration across the business—especially data science and IT teams—and maximize ROI. For code-first users, we offer a code experience too, using the AP—both in Python and R—for your convenience.

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Heartbeat

MARCH 21, 2023

There are several techniques used for model monitoring with time series data, including: Data Drift Detection: This involves monitoring the distribution of the input data over time to detect any changes that may impact the model’s performance. You can learn more about Comet here. You can get the full code here.

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The MLOps Blog

AUGUST 3, 2023

You essentially divide things up into large tasks and chunks, but the software engineering that goes within that task is the thing that you’re generally gonna be updating and adding to over time as your machine learning grows within your company or you have new data sources, you want to create new models, right? To figure it out.

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Snorkel AI

MAY 12, 2023

Three experts from Capital One ’s data science team spoke as a panel at our Future of Data-Centric AI conference in 2022. Please welcome to the stage, Senior Director of Applied ML and Research, Bayan Bruss; Director of Data Science, Erin Babinski; and Head of Data and Machine Learning, Kishore Mosaliganti.

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Snorkel AI

MAY 12, 2023

Three experts from Capital One ’s data science team spoke as a panel at our Future of Data-Centric AI conference in 2022. Please welcome to the stage, Senior Director of Applied ML and Research, Bayan Bruss; Director of Data Science, Erin Babinski; and Head of Data and Machine Learning, Kishore Mosaliganti.

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The MLOps Blog

MAY 9, 2023

Data validation This step collects the transformed data as input and, through a series of tests and validators, ensures that it meets the criteria for the next component. It checks the data for quality issues and detects outliers and anomalies. Kedro Kedro is a Python library for building modular data science pipelines.

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Topbots

JUNE 29, 2023

However, as of now, unleashing the full potential of organisational data is often a privilege of a handful of data scientists and analysts. Most employees don’t master the conventional data science toolkit (SQL, Python, R etc.). the changing distribution of the data to which the model is applied.

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