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

SEPTEMBER 4, 2024

SageMaker provides rich features to build automated workflows for deploying models at scale. To keep track of models, versions, and associated metadata, SageMaker Model Registry can be used as an inventory of models. Saumitra Vikaram is a Senior Software Engineer at AWS. Siamak Nariman is a Senior Product Manager at AWS.

Generative AI 105

Generative AI Metadata ML AI 105

AWS Machine Learning Blog

DECEMBER 19, 2023

Just like DevOps combines development and operations for software engineering, MLOps combines ML engineering and IT operations. With the rapid growth in ML systems and in the context of ML engineering, MLOps provides capabilities that are needed to handle the unique complexities of the practical application of ML systems.

Machine Learning 129

Machine Learning ML Engineer DevOps ML 129

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 129

Generative AI Metadata Automation Machine Learning 129

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 124

IDP Software Engineer Metadata Categorization 124

AWS Machine Learning Blog

FEBRUARY 20, 2024

Through automation, ZOO Digital aims to achieve localization in under 30 minutes. However, the supply of skilled people is being outstripped by the increasing demand for content, requiring automation to assist with localization workflows. This time-consuming process must be completed before content can be dubbed into another language.

Metadata 121

Metadata Auto-complete Machine Learning Deep Learning 121

Google Research AI blog

FEBRUARY 24, 2023

Posted by Yang Li, Research Scientist, and Gang Li, Software Engineer, Google Research The computational understanding of user interfaces (UI) is a key step towards achieving intelligent UI behaviors. This metadata has given previous models advantages over their vision-only counterparts. their types, text content and positions).

Metadata 122

Metadata Neural Network Software Engineer Automation 122

ODSC - Open Data Science

SEPTEMBER 27, 2023

Metadata Management: Tracking metadata , such as data schema, data sources, and data transformation processes, aids in understanding the evolution of datasets and the context of changes. About Author – Kruti Chapaneri is an aspiring software engineer and tech writer with a strong interest in the intersection of technology and business.

Big Data 98

Big Data Metadata ETL Business Intelligence 98

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

AWS Machine Learning Blog

JANUARY 17, 2023

This development approach can be used in combination with other common software engineering best practices such as automated code deployments, tests, and CI/CD pipelines. The AWS CDK reduces the time required to perform typical infrastructure deployment tasks while shrinking the surface area for human error through automation.

Software Engineer 94

Software Engineer ML Engineer ML Machine Learning 94

AWS Machine Learning Blog

AUGUST 20, 2024

Amazon SageMaker Ground Truth significantly reduces the cost and time required for labeling data by integrating human annotators with machine learning to automate the labeling process. You can call the SageMaker ListWorkteams or DescribeWorkteam APIs to view workteams’ metadata, including the WorkerAccessConfiguration.

Software Engineer 112

Software Engineer ML Machine Learning Metadata 112

AWS Machine Learning Blog

JUNE 11, 2024

This feature streamlines the process of launching new instances with the most up-to-date Neuron SDK, enabling you to automate your deployment workflows and make sure you’re always using the latest optimizations. Anant Sharma is a software engineer at AWS Annapurna Labs specializing in DevOps.

Deep Learning 122

Deep Learning ML Automation Auto-complete 122

Unite.AI

JUNE 24, 2024

MLOps , or Machine Learning Operations, is a multidisciplinary field that combines the principles of ML, software engineering, and DevOps practices to streamline the deployment, monitoring, and maintenance of ML models in production environments.

Data Drift 124

Data Drift Machine Learning Data Scientist DevOps 124

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

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 114

Data Scientist ML Data Science Automation 114

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 96

Machine Learning Artificial Intelligence Artificial Intelligence ML 96

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 105

AI Developer AI Development Generative AI Software Engineer 105

AWS Machine Learning Blog

NOVEMBER 14, 2024

However, model governance functions in an organization are centralized and to perform those functions, teams need access to metadata about model lifecycle activities across those accounts for validation, approval, auditing, and monitoring to manage risk and compliance. An experiment collects multiple runs with the same objective.

ML 95

ML Auto-complete Machine Learning Auto-classification 95

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 113

Metadata Machine Learning ML Software Engineer 113

AWS Machine Learning Blog

JULY 8, 2024

This automated solution helps you get started quickly by providing all the code and configuration necessary to generate your unique images—all you need is images of your subject. However, this solution uses the equivalent GUI parameters as a pre-configured TOML file to automate the entire Stable Diffusion XL fine-tuning process.

Automation 123

Automation ML DevOps Software Engineer 123

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 96

BERT Metadata Natural Language Processing ML 96

AWS Machine Learning Blog

JUNE 21, 2024

Administrators can check the metadata of a specific model using the hub.describe_model(model_name= ) command. Bhaskar Pratap is a Senior Software Engineer with the Amazon SageMaker team. Choose one of model hubs you have access to. If the user has access to only one hub, you’ll go straight to the model list.

Python 113

Python Machine Learning ML Software Engineer 113

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 83

Data Science Machine Learning Python Data Scientist 83

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

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

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

Unite.AI

OCTOBER 18, 2024

As companies grapple with the complexities of cloud-native architectures, microservices, and the need for rapid deployment, IDPs offer a solution that streamlines workflows, automates repetitive tasks, and empowers developers to focus on what they do best – writing code.

IDP 162

IDP DevOps Automation Software Development 162

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 78

Automation Computer Vision Machine Learning ML 78

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 155

ETL Prompt Engineer Prompt Engineering Generative AI 155

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 78

ML Data Scientist Auto-complete Metadata 78

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 119

ML Auto-complete Deep Learning Software Engineer 119