Towards AI

AUGUST 16, 2023

Using dependency management tool like pip or Poetry serve as a game-changer here by automating the package building and installation process. Developers would rely on it to define project metadata, dependencies, and installation instructions. ', py_modules=['my_module'], # Other metadata.)

Python 78

Python Metadata Software Development Automation 78

AWS Machine Learning Blog

MAY 15, 2023

We outline how we built an automated demand forecasting pipeline using Forecast and orchestrated by AWS Step Functions to predict daily demand for SKUs. Forecast automates much of the time-series forecasting process, enabling you to focus on preparing your datasets and interpreting your predictions.

Convolutional Neural Networks 78

Convolutional Neural Networks Neural Network Metadata Data Scientist 78

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

DECEMBER 13, 2023

To deliver value, they must integrate into existing production systems and infrastructure, which necessitates considering the entire ML lifecycle during design and development. ML operations, known as MLOps, focus on streamlining, automating, and monitoring ML models throughout their lifecycle.

ML 122

ML Automation Metadata Software Development 122

AWS Machine Learning Blog

MAY 10, 2023

Now with the integration with the model registry, you can store all model artifacts, including metadata and performance metrics baselines, to a central repository and plug them into your existing model deployment CI/CD processes. Huayuan(Alice) Wu is a Software Development Engineer at Amazon SageMaker.

ML 83

ML Metadata Software Development Data Scientist 83

AWS Machine Learning Blog

OCTOBER 2, 2023

It is architected to automate the entire machine learning (ML) process, from data labeling to model training and deployment at the edge. Automating data labeling Data labeling is an inherently labor-intensive task that involves humans (labelers) to label the data. If you haven’t read it yet, we recommend checking out Part 1.

DevOps 114

DevOps Automation Machine Learning Metadata 114

AWS Machine Learning Blog

APRIL 18, 2024

Solution overview The LMA sample solution captures speaker audio and metadata from your browser-based meeting app (as of this writing, Zoom and Chime are supported), or audio only from any other browser-based meeting app, softphone, or audio source. You can also create your own custom prompts and corresponding options.

Metadata 126

Metadata LLM Automation Large Language Models 126

ODSC - Open Data Science

APRIL 17, 2023

This shift in thinking has led us to DevSecOps , a novel methodology that integrates security into the software development/ MLOps process. DevSecOps includes all the characteristics of DevOps, such as faster deployment, automated pipelines for build and deployment, extensive testing, etc.,

Data Science 97

Data Science DevOps Deep Learning Automation 97

Viso.ai

MARCH 29, 2024

MLflow is an open-source platform designed to manage the entire machine learning lifecycle, making it easier for ML Engineers, Data Scientists, Software Developers, and everyone involved in the process. Machine learning operations (MLOps) are a set of practices that automate and simplify machine learning (ML) workflows and deployments.

Machine Learning 52

Machine Learning ML Automation Data Scientist 52

AssemblyAI

DECEMBER 18, 2023

Artificial Intelligence like Speech AI is part of that ecosystem more and more: AI can automate repetitive tasks, help predict student outcomes, and generate educational content. It can also be an ecosystem of educators, learners, and innovative technology that elevates learning opportunities.

AI 147

AI AI Artificial Intelligence Artificial Intelligence 147

AWS Machine Learning Blog

JUNE 21, 2024

eSentire used gigabytes of additional human investigation metadata to perform supervised fine-tuning on Llama 2. All of this was entirely automated with the software development lifecycle (SDLC) using Terraform and GitHub, which is only possible through SageMaker ecosystem.

Generative AI 118

Generative AI LLM AI AI 118

AWS Machine Learning Blog

NOVEMBER 15, 2023

As a first step, they wanted to transcribe voice calls and analyze those interactions to determine primary call drivers, including issues, topics, sentiment, average handle time (AHT) breakdowns, and develop additional natural language processing (NLP)-based analytics. He has 20 years of enterprise software development experience.

Data Ingestion 124

Data Ingestion Metadata NLP Data Scientist 124

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. SageMaker deployment guardrails Guardrails are an essential part of software development.

ML 78

ML Software Development Automation Metadata 78

AWS Machine Learning Blog

JANUARY 10, 2024

The large machine learning (ML) model development lifecycle requires a scalable model release process similar to that of software development. Model developers often work together in developing ML models and require a robust MLOps platform to work in. ML Engineer at Tiger Analytics.

ML 120

ML Machine Learning ETL Data Scientist 120

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. Ziwen Ning is a software development engineer at AWS. AWS Systems Manager Parameter Store support Neuron 2.18

Deep Learning 117

Deep Learning ML Automation Auto-complete 117

deepsense.ai

JULY 30, 2023

Those tools and practices not only help to integrate consecutive steps (see Figure 1) together and make them work smoothly; they also make sure that the whole process is reproducible, automated and properly monitored at each stage – model training as well as model inference. Not the best combination, right?

Large Language Models 98

Large Language Models LLM Machine Learning Automation 98

The MLOps Blog

APRIL 17, 2023

Building a tool for managing experiments can help your data scientists; 1 Keep track of experiments across different projects, 2 Save experiment-related metadata, 3 Reproduce and compare results over time, 4 Share results with teammates, 5 Or push experiment outputs to downstream systems.

Metadata 52

Metadata Data Scientist Explainability ML 52

AWS Machine Learning Blog

AUGUST 16, 2023

Amazon SageMaker Pipelines , a feature of Amazon SageMaker , is a purpose-built workflow orchestration service for ML that helps you automate end-to-end ML workflows at scale. Software Development Engineer with Amazon Stores. He enjoys playing Table Tennis and is a sports fan. Nishant Krishnamoorthy is a Sr.

Metadata 80

Metadata Data Scientist Python ML 80

AWS Machine Learning Blog

AUGUST 28, 2024

GitHub (Cloud) is a popular development platform that helps teams build, scale, and deliver software used by more than 100 million developers and over 4 million organizations worldwide. Other options for automated periodic sync runs are also supported. In the Field Mappings section, keep the default settings.

Generative AI 121

Generative AI AI AI Metadata 121

AWS Machine Learning Blog

JULY 25, 2024

Automate workflows and tasks – Amazon Q can be configured to complete routine tasks and queries (such as generating status reports, answering FAQs, or requesting information) by interacting with the relevant SharePoint data and applications. This is an automated process that takes in the inputs and configures the required permissions.

IDP 101

IDP Metadata Generative AI Software Development 101

AWS Machine Learning Blog

MAY 15, 2024

The image is then uploaded into an Amazon Simple Storage Services (Amazon S3) bucket for images and the metadata about the image is stored in an Amazon DynamoDB table (step 6). Christopher de Beer is a senior software development engineer at Amazon located in Edinburgh, UK. With a background in visual design.

Generative AI 123

Generative AI AI AI Machine Learning 123

AWS Machine Learning Blog

JANUARY 17, 2023

The AWS Cloud Development Kit (AWS CDK) is an open-source software development framework to create AWS CloudFormation stacks through automatic CloudFormation template generation. cdk.json – Contains metadata, and feature flags. app.py – Where the AWS CDK stack is defined. AWS CDK scripts. and sagemaker_studio_stack.py.

Software Engineer 89

Software Engineer ML Engineer ML Machine Learning 89

AWS Machine Learning Blog

SEPTEMBER 28, 2023

To effectively train this model, we establish an MLOps infrastructure to streamline the model development process by automating data preprocessing, feature engineering, hyperparameter tuning, and model selection. This automation reduces human error, improves reproducibility, and accelerates the model development cycle.

Machine Learning 111

Machine Learning Automation ML Natural Language Processing 111

AWS Machine Learning Blog

AUGUST 26, 2024

We envision a future where AI seamlessly integrates into our teams’ workflows, automating repetitive tasks, providing intelligent recommendations, and freeing up time for more strategic, high-value interactions. We encourage you to explore these possibilities, experiment with AWS AI services, and embark on your own transformation journey.

Generative AI 121

Generative AI LLM AI AI 121

Pickl AI

JUNE 28, 2023

Increased Efficiency And hands work productivity is the key objective of any developer. Moreover, these tools are designed to automate tasks like generating SQL scripts, documenting metadata and others. This automation boosts productivity and also saves time. Data Dictionary A data dictionary is a repository of metadata.

Metadata 52

Metadata Data Integration Automation Software Development 52

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 107

Software Engineer ML Machine Learning Metadata 107

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 109

Data Scientist ML Data Science Automation 109

AWS Machine Learning Blog

OCTOBER 8, 2024

In this blog post, we explore a comprehensive approach to time series forecasting using the Amazon SageMaker AutoMLV2 Software Development Kit (SDK). SageMaker AutoMLV2 is part of the SageMaker Autopilot suite, which automates the end-to-end machine learning workflow from data preparation to model deployment.

Machine Learning 92

Machine Learning Auto-complete Auto-classification Metadata 92

AWS Machine Learning Blog

NOVEMBER 14, 2024

In AWS, these model lifecycle activities can be performed over multiple AWS accounts (for example, development, test, and production accounts) at the use case or business unit level. It also helps achieve data, project, and team isolation while supporting software development lifecycle best practices. Madhubalasri B.

ML 90

ML Auto-complete Machine Learning Auto-classification 90

AWS Machine Learning Blog

NOVEMBER 7, 2024

It also lets you automate your evaluation process in your pre-production environments. You can set up a test application and a red team with your own employees or automate it against a known set of vulnerabilities. He has over 15 years of combined work experience in machine learning, software development and data science on the cloud.

Generative AI 109

Generative AI AI AI Responsible AI 109

AWS Machine Learning Blog

APRIL 7, 2023

Next, using an AWS Cloud9 environment or interactive development environment (IDE) of choice, download the requisite SageMaker packages and Docker Compose , a key dependency of JumpStart. About the Authors Robert Belson is a Developer Advocate in the AWS Worldwide Telecom Business Unit, specializing in AWS Edge Computing. Instances[*].

BERT 91

BERT Metadata Natural Language Processing ML 91

AWS Machine Learning Blog

NOVEMBER 19, 2024

Bedrock agents can streamline workflows and provide AI automation to boost productivity. It allows users to perform searches using specific queries, retrieve article metadata, and handle the complexities of API interactions. For examples to get started with Amazon Bedrock Agents, check out the Amazon Bedrock Agents GitHub repository.

Data Analysis 63

Data Analysis Machine Learning Large Language Models ML 63

The MLOps Blog

MARCH 15, 2023

Automation : Automating as many tasks to reduce human error and increase efficiency. AWS Sagemeaker is in fact a great tool for machine learning operations (MLOps) to automate and standardize processes across the ML lifecycle. Licensing costs: Oftentimes, we need third-party software libraries to power our solutions.

ETL 52

ETL Data Drift Machine Learning ML 52

AWS Machine Learning Blog

JUNE 3, 2024

For example, you can embed all image metadata and additional text descriptions into the same vector space. Almost every part of his home is automated, from light bulbs and blinds to vacuum cleaning and mopping. Close vectors indicate that the images and text are semantically related.

ML 105

ML Natural Language Processing Metadata Large Language Models 105

The MLOps Blog

APRIL 5, 2023

Source Model packaging is a process that involves packaging model artifacts, dependencies, configuration files, and metadata into a single format for effortless distribution, installation, and reuse. These teams may include but are not limited to data scientists, software developers, machine learning engineers, and DevOps engineers.

ML 69

ML Machine Learning Deep Learning Metadata 69

AWS Machine Learning Blog

AUGUST 4, 2023

The Amazon customer reviews dataset contains product reviews and metadata from Amazon, including 142.8 On a high level, we use SageMaker Data Wrangler to manage this large dataset and perform the following actions: Develop an ML model in SageMaker Autopilot using all of the dataset, not just a sample.

Auto-complete 81

Auto-complete ML Python Metadata 81

deepsense.ai

SEPTEMBER 11, 2023

Chart 1 – Occupations at risk of being automated. The yellow dot represents programmers and software engineers. This newfound knowledge of generative AI tools will stand you in good stead in the dynamic landscape of software development. Think of it as a lifelong learning process, not a sprint to the finish line.

AI Developer 105

AI Developer AI Development Generative AI Software Engineer 105