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. Key AI/ML use cases and platform requirements AI/ML-enabled propositions can transform healthcare by automating administrative tasks done by clinicians.

Data Scientist 88

Data Scientist ML Data Science Automation 88

Google Research AI blog

JUNE 23, 2023

Posted by Ramki Gummadi, Software Engineer, Google and Kevin Chen, Software Engineer, YouTube Caching is a ubiquitous idea in computer science that significantly improves the performance of storage and retrieval systems by storing a subset of popular items closer to the client based on request patterns.

Automation 79

Automation Neural Network Software Engineer Metadata 79

The MLOps Blog

JANUARY 23, 2023

Just so you know where I am coming from: I have a heavy software development background (15+ years in software). Came to ML from software. Founded two successful software services companies. 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

AWS Machine Learning Blog

APRIL 7, 2023

In our example, we have selected port 30,007 as our NodePort : # algo-1-ow3nv-service.yaml apiVersion: v1 kind: Service metadata: annotations: kompose.cmd: kompose convert kompose.version: 1.26.0 Mohammed holds a bachelor’s degree in Telecommunications Engineering from Concordia University. Instances[*].

BERT 80

BERT Metadata Natural Language Processing ML 80

IBM Journey to AI blog

APRIL 1, 2024

Automation and i ntegration for routine tasks through integration with various CI/CD and monitoring tools, including through a growing community of plug-ins. Visibility and g overnance into the software development lifecycle through insight to project status, dependencies and more.

Software Development 201

Software Development Software Engineer Automation Metadata 201

AWS Machine Learning Blog

APRIL 11, 2024

Advances in generative artificial intelligence (AI) have given rise to intelligent document processing (IDP) solutions that can automate the document classification, and create a cost-effective classification layer capable of handling diverse, unstructured enterprise documents. Categorizing documents is an important first step in IDP systems.

IDP 100

IDP Software Engineer Metadata Categorization 100

AWS Machine Learning Blog

APRIL 23, 2024

Here are some features which we will cover: AWS CloudFormation support Private network policies for Amazon OpenSearch Serverless Multiple S3 buckets as data sources Service Quotas support Hybrid search, metadata filters, custom prompts for the RetreiveAndGenerate API, and maximum number of retrievals.

Generative AI 103

Generative AI Automation Metadata Machine Learning 103

Viso.ai

MARCH 29, 2024

Machine learning operations (MLOps) are a set of practices that automate and simplify machine learning (ML) workflows and deployments. Many machine learning projects fail to deliver practical results due to difficulties in automation and deployment. ML Tasks – source Automating these ML lifecycle steps is extremely difficult.

Machine Learning 52

Machine Learning ML Automation Data Scientist 52

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

deepsense.ai

SEPTEMBER 11, 2023

In recent times, the rapid advancement of AI technologies like ChatGPT and other Large Language Models (LLMs) have sparked growing panic among the software engineering community. Don’t give up on being a developer According to a 2019 report by the UK Office for National Statistics, software engineers face a 27.4%

AI Developer 81

AI Developer AI Development Generative AI Software Engineer 81

AWS Machine Learning Blog

OCTOBER 25, 2023

format(as_iso_date(target_date)) } }, } #query raster data using SageMaker geospatial capabilities sentinel2_items = geospatial_client.search_raster_data_collection(**search_params) The response contains a list of matching Sentinel-2 items and their corresponding metadata. format(as_iso_date(target_date)), "EndTime": "{}T23:59:59Z".format(as_iso_date(target_date))

Metadata 86

Metadata Machine Learning ML Software Engineer 86

The MLOps Blog

OCTOBER 3, 2023

I started from tech, my first job was an internship at Google as a software engineer. I’m from Poland, and I remember when I got an offer from Google to join as a regular software engineer. There’s no component that stores metadata about this feature store? It was two or three times more. How awful are they?”

ML 52

ML Data Scientist Machine Learning Data Science 52

The MLOps Blog

FEBRUARY 27, 2023

What I mean is when data scientists are working hand in hand with software engineers or MLOps engineers, that would then take over or wrap up the solution. What we do, quite often or usually, and what I like a lot, is to have an interdisciplinary team pretty much as soon as it’s possible.

Computer Vision 52

Computer Vision Auto-classification Auto-complete ML 52

AWS Machine Learning Blog

APRIL 4, 2024

We perform media monitoring, where we add additional metadata to each segment, such as league scores, relevant teams, and players. Because we operate at a scale of over a thousand channels and tens of thousands of hours of video a year, we must have a scalable automation system for the analysis process.

Machine Learning 75

Machine Learning ML Artificial Intelligence Artificial Intelligence 75

AWS Machine Learning Blog

MAY 8, 2023

To automate the infrastructure deployment, we use the AWS Cloud Development Kit (AWS CDK). The AWS CDK is an open-source software development framework to create AWS CloudFormation stacks through automatic CloudFormation template generation. He holds a PhD in Telecommunication Engineering and has experience in software engineering.

Data Science 70

Data Science Machine Learning Python Data Scientist 70

Snorkel AI

MARCH 2, 2023

From a software engineering perspective, machine-learning models, if you look at it in terms of the number of parameters and in terms of size, started out from the transformer models. So the application started to go from the pure software-engineering/machine-learning domain to industry and the sciences, essentially.

Large Language Models 52

Large Language Models Prompt Engineer Prompt Engineering Neural Network 52

Snorkel AI

MARCH 2, 2023

From a software engineering perspective, machine-learning models, if you look at it in terms of the number of parameters and in terms of size, started out from the transformer models. So the application started to go from the pure software-engineering/machine-learning domain to industry and the sciences, essentially.

Large Language Models 52

Large Language Models Prompt Engineer Prompt Engineering Neural Network 52

AWS Machine Learning Blog

JULY 20, 2023

Fortunately, thanks to enormous advances in the world of computer vision and deep learning and the maturity and democratization of these technologies, it’s possible to automate this expensive process partially or even completely. This allows the clustering of ROIs referring to the same pole.

Automation 66

Automation Computer Vision Machine Learning ML 66

The MLOps Blog

MAY 9, 2023

The pipelines let you orchestrate the steps of your ML workflow that can be automated. Simplify the end-to-end orchestration of the multiple steps in the machine learning workflow for projects with little to no intervention (automation) from the ML team. For example: Is it too large to fit the infrastructure requirements?

ML 98

ML Machine Learning Metadata Automation 98

The MLOps Blog

MARCH 21, 2023

To make that possible, your data scientists would need to store enough details about the environment the model was created in and the related metadata so that the model could be recreated with the same or similar outcomes. Version control for code is common in software development, and the problem is mostly solved.

Machine Learning 69

Machine Learning Data Scientist ML Metadata 69

Flipboard

NOVEMBER 24, 2023

This emergent ability in LLMs has compelled software developers to use LLMs as an automation and UX enhancement tool that transforms natural language to a domain-specific language (DSL): system instructions, API requests, code artifacts, and more. in Software Engineering from the University of the West of England in Bristol, UK.

ETL 112

ETL Prompt Engineer Prompt Engineering Generative AI 112

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. Zichen Nie is the Senior Software Engineer at AWS SageMaker leading the project Bring Your Own Model to SageMaker Canvas last year.

ML 67

ML Data Scientist Auto-complete Metadata 67

AWS Machine Learning Blog

FEBRUARY 19, 2024

To take advantage of automated model scaling in SageMaker, make sure you have instance auto scaling set up to provision additional instance capacity. format that SageMaker Inference is expecting: model.joblib – For this implementation, we directly push the model metadata into the tarball.

ML 93

ML Auto-complete Deep Learning Software Engineer 93