Snorkel AI

JUNE 22, 2023

Jacomo Corbo is a Partner and Chief Scientist, and Bryan Richardson is an Associate Partner and Senior Data Scientist, for QuantumBlack AI by McKinsey. They presented “Automating Data Quality Remediation With AI” at Snorkel AI’s The Future of Data-Centric AI Summit in 2022.

Data Quality 52

Data Quality Automation Data Scientist ML 52

Snorkel AI

JUNE 22, 2023

Jacomo Corbo is a Partner and Chief Scientist, and Bryan Richardson is an Associate Partner and Senior Data Scientist, for QuantumBlack AI by McKinsey. They presented “Automating Data Quality Remediation With AI” at Snorkel AI’s The Future of Data-Centric AI Summit in 2022.

Data Quality 52

Data Quality Automation Data Scientist ML 52

Snorkel AI

JUNE 22, 2023

Jacomo Corbo is a Partner and Chief Scientist, and Bryan Richardson is an Associate Partner and Senior Data Scientist, for QuantumBlack AI by McKinsey. They presented “Automating Data Quality Remediation With AI” at Snorkel AI’s The Future of Data-Centric AI Summit in 2022.

Data Quality 52

Data Quality Automation Data Scientist ML 52

AWS Machine Learning Blog

JULY 8, 2024

TWCo data scientists and ML engineers took advantage of automation, detailed experiment tracking, integrated training, and deployment pipelines to help scale MLOps effectively. Amazon CloudWatch – Collects and visualizes real-time logs that provide the basis for automation. Used to deploy training and inference code.

Data Scientist 129

Data Scientist ML Engineer Machine Learning Data Science 129

The MLOps Blog

JUNE 27, 2023

This includes features for hyperparameter tuning, automated model selection, and visualization of model metrics. Automated pipelining and workflow orchestration: Platforms should provide tools for automated pipelining and workflow orchestration, enabling you to define and manage complex ML pipelines.

Machine Learning 134

Machine Learning Metadata Data Scientist Data Quality 134

Flipboard

NOVEMBER 22, 2024

It serves as the hub for defining and enforcing data governance policies, data cataloging, data lineage tracking, and managing data access controls across the organization. Data lake account (producer) – There can be one or more data lake accounts within the organization.

ML 132

ML Data Science Metadata DevOps 132

AWS Machine Learning Blog

APRIL 21, 2023

See the following code: # Configure the Data Quality Baseline Job # Configure the transient compute environment check_job_config = CheckJobConfig( role=role_arn, instance_count=1, instance_type="ml.c5.xlarge", These are key files calculated from raw data used as a baseline.

Data Drift 115

Data Drift Metadata Data Quality ML 115

AWS Machine Learning Blog

AUGUST 29, 2023

In this post, we describe how to create an MLOps workflow for batch inference that automates job scheduling, model monitoring, retraining, and registration, as well as error handling and notification by using Amazon SageMaker , Amazon EventBridge , AWS Lambda , Amazon Simple Notification Service (Amazon SNS), HashiCorp Terraform, and GitLab CI/CD.

Data Scientist 96

Data Scientist Data Quality Python ML 96

AWS Machine Learning Blog

APRIL 26, 2023

Ensuring data quality, governance, and security may slow down or stall ML projects. This includes AWS Identity and Access Management (IAM) or single sign-on (SSO) access, security guardrails, Amazon SageMaker Studio provisioning, automated stop/start to save costs, and Amazon Simple Storage Service (Amazon S3) set up.

ML 126

ML Machine Learning Data Science Data Drift 126

AWS Machine Learning Blog

OCTOBER 28, 2024

Early and proactive detection of deviations in model quality enables you to take corrective actions, such as retraining models, auditing upstream systems, or fixing quality issues without having to monitor models manually or build additional tooling. Data Scientist with AWS Professional Services. Raju Patil is a Sr.

ML 123

ML Metadata Data Scientist DevOps 123

DataRobot Blog

FEBRUARY 11, 2022

In the first part of the “Ever-growing Importance of MLOps” blog, we covered influential trends in IT and infrastructure, and some key developments in ML Lifecycle Automation. These agents apply the concept familiar in the DevOps world—to run models in their preferred environments while monitoring all models centrally.

Data Drift 52

Data Drift DevOps Machine Learning Data Scientist 52

AWS Machine Learning Blog

FEBRUARY 21, 2023

Amazon SageMaker for MLOps provides purpose-built tools to automate and standardize steps across the ML lifecycle, including capabilities to deploy and manage new models using advanced deployment patterns. Similar to traditional CI/CD systems, we want to automate software tests, integration testing, and production deployments.

ML 95

ML Software Development Automation Metadata 95

AWS Machine Learning Blog

FEBRUARY 28, 2023

Solution overview This post shows that by anonymizing sensitive data from Amazon HealthLake and making it available to SageMaker Canvas, organizations can empower more stakeholders to use ML models that can generate predictions for multi-modal problems, such as stroke prediction, without writing ML code, while limiting access to sensitive data.

ML 101

ML Machine Learning Categorization NLP 101

Pickl AI

APRIL 27, 2023

Data & Analytics leaders must count on these trends to plan future strategies and implement the same to make business operations more effective. How can automation transform the business, optimizing resources and driving innovative measures to make business more competitive? Wrapping it up !!!

Data Science 52

Data Science Artificial Intelligence Artificial Intelligence Python 52

Marktechpost

JULY 17, 2023

The advantages of using synthetic data include easing restrictions when using private or controlled data, adjusting the data requirements to specific circumstances that cannot be met with accurate data, and producing datasets for DevOps teams to use for software testing and quality assurance.

Machine Learning 52

Machine Learning Data Scientist Computer Vision Deep Learning 52

The MLOps Blog

APRIL 17, 2023

Robustness You need an elastic data model to support: Varying team sizes and structures (a single data scientist only, or maybe a team of one data scientist, 4 machine learning engineers, 2 DevOps engineers, etc.). Using the right tools and automation makes it user-friendly and efficient.

Metadata 52

Metadata Data Scientist Explainability ML 52

The MLOps Blog

MAY 9, 2023

The pipelines let you orchestrate the steps of your ML workflow that can be automated. The orchestration here implies that the dependencies and data flow between the workflow steps must be completed in the proper order. Reduce the time it takes for data and models to move from the experimentation phase to the production phase.

ML 98

ML Machine Learning Metadata Data Science 98

The MLOps Blog

MARCH 21, 2023

It should be able to version the project assets of your data scientists, such as the data, the model parameters, and the metadata that comes out of your workflow. 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.

Machine Learning 69

Machine Learning Data Scientist ML Metadata 69

Pickl AI

MAY 15, 2024

Data Quality and Standardization The adage “garbage in, garbage out” holds true. Inconsistent data formats, missing values, and data bias can significantly impact the success of large-scale Data Science projects. This builds trust in model results and enables debugging or bias mitigation strategies.

Data Scientist 52

Data Scientist Data Science Machine Learning Data Quality 52

Unite.AI

MARCH 21, 2024

After that, I worked for startups for a few years and then spent a decade at Palo Alto Networks, eventually becoming a VP responsible for development, QA, DevOps, and data science. That led me to pursue engineering at Sharif University of Technology in Iran and later get my Ph.D.

DevOps 147

DevOps Data Quality Machine Learning Generative AI 147