The MLOps Blog

JANUARY 23, 2023

Founded neptune.ai , a modular MLOps component for ML metadata store , aka “experiment tracker + model registry”. We need both automated continuous monitoring AND periodic manual inspection. There will be only one type of ML metadata store (model-first), not three. Some feedback loops can be automated, but some cannot.

DevOps 59

DevOps Metadata Software Engineer Data Scientist 59

AWS Machine Learning Blog

MARCH 1, 2023

This requires not only well-designed features and ML architecture, but also data preparation and ML pipelines that can automate the retraining process. To solve this problem, we make the ML solution auto-deployable with a few configuration changes. ML engineers no longer need to manage this training metadata separately.

Automation 87

Automation ETL Data Drift ML 87

AWS Machine Learning Blog

MAY 7, 2024

With a decade of enterprise AI experience, Veritone supports the public sector, working with US federal government agencies, state and local government, law enforcement agencies, and legal organizations to automate and simplify evidence management, redaction, person-of-interest tracking, and eDiscovery.

Metadata 88

Metadata Generative AI Machine Learning Large Language Models 88

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 Quality Data Scientist 134

The MLOps Blog

FEBRUARY 27, 2023

You would address it in a completely different way, depending on what’s the problem. This is more about picking, for some active learning or for knowing where the data comes from and knowing the metadata to focus on the data that are the most relevant to start with. This is a much smaller scale than Auto ML.

Computer Vision 52

Computer Vision Auto-classification Auto-complete ML 52

AWS Machine Learning Blog

JUNE 13, 2023

The integration of large language models helps humanize the interaction with automated agents, creating a more engaging and satisfying support experience. In addition, deployments are now as simple as calling Boto3 SageMaker APIs and attaching the proper auto scaling policies. The following diagram illustrates our legacy architecture.

Generative AI 65

Generative AI Auto-complete AI Modeling Machine Learning 65

AWS Machine Learning Blog

MARCH 2, 2023

script will create the VPC, subnets, auto scaling groups, the EKS cluster, its nodes, and any other necessary resources. W&B Sweeps is a powerful tool to automate hyperparameter optimization. W&B Sweeps will automate this kind of exploration. Unless you specify Spot Instances in conf, instances will be created on demand.

BERT 75

BERT Deep Learning Metadata Auto-complete 75

AWS Machine Learning Blog

AUGUST 4, 2023

With SageMaker Data Wrangler, you can simplify the process of data preparation and feature engineering and complete each step of the data preparation workflow, including data selection, cleansing, exploration, and visualization from a single visual interface. million reviews spanning May 1996 to July 2014. Next, select a training method.

Auto-complete 73

Auto-complete ML Metadata Python 73

The MLOps Blog

MARCH 12, 2024

Model management Teams typically manage their models, including versioning and metadata. Monitoring Monitor model performance for data drift and model degradation, often using automated monitoring tools. Feedback loops: Use automated and human feedback to improve prompt design continuously. using techniques like RLHF.)

Prompt Engineer 52

Prompt Engineer Prompt Engineering LLM Large Language Models 52

Snorkel AI

MARCH 2, 2023

Others, toward language completion and further downstream tasks. In terms of technology: generating code snippets, code translation, and automated documentation. In media and gaming: designing game storylines, scripts, auto-generated blogs, articles and tweets, and grammar corrections and text formatting.

Large Language Models 52

Large Language Models Prompt Engineer Prompt Engineering Neural Network 52

Snorkel AI

MARCH 2, 2023

Others, toward language completion and further downstream tasks. In terms of technology: generating code snippets, code translation, and automated documentation. In media and gaming: designing game storylines, scripts, auto-generated blogs, articles and tweets, and grammar corrections and text formatting.

Large Language Models 52

Large Language Models Prompt Engineer Prompt Engineering Neural Network 52

AWS Machine Learning Blog

SEPTEMBER 21, 2023

In this post, we discuss how United Airlines, in collaboration with the Amazon Machine Learning Solutions Lab , build an active learning framework on AWS to automate the processing of passenger documents. “In We used Amazon Textract to automate information extraction from specific document fields such as name and passport number.

Auto-complete 97

Auto-complete Machine Learning Computer Vision ML 97

AWS Machine Learning Blog

MARCH 6, 2023

FSx for Lustre uses distributed file storage (stripping) and physically separates file metadata from file content to achieve high-performance read/writes. and above, AWS contributed an automated communication algorithm selection logic for EFA networks ( NCCL_ALGO can be left unset). Note that effective in NCCL 2.12

Large Language Models 78

Large Language Models LLM Machine Learning ML 78

Dlabs.ai

JANUARY 16, 2023

That’s why the clinic wants to harness the power of deep learning in a bid to help healthcare professionals in an automated way. Using new_from_file only loads image metadata. But it’s not easy to spot the tell-tale signs in scans. Unfortunately, the competition rules prevent us from publishing competition data publicly.

Neural Network 52

Neural Network ML Auto-classification Convolutional Neural Networks 52

AWS Machine Learning Blog

MAY 2, 2023

Use Autopilot and Canvas Autopilot automates key tasks of an automatic ML (AutoML) process like exploring data, selecting the relevant algorithm for the problem type, and then training and tuning it. Complete the steps listed in the README file. Set the target column as churn. Let’s assume the role of a data scientist.

ML 67

ML Data Scientist Auto-complete Metadata 67

AWS Machine Learning Blog

FEBRUARY 19, 2024

As a result, an initial invocation to a model might see higher inference latency than the subsequent inferences, which are completed with low latency. To take advantage of automated model scaling in SageMaker, make sure you have instance auto scaling set up to provision additional instance capacity.

ML 93

ML Auto-complete Deep Learning Software Engineer 93