Auto-complete, Metadata and Software Engineer - Artificial Intelligence Zone

From Solo Notebooks to Collaborative Powerhouse: VS Code Extensions for Data Science and ML Teams

Towards AI

AUGUST 7, 2024

We are data wranglers at heart, not necessarily software engineers by training, and best practices for reproducibility can sometimes get pushed aside in the heat of exploration. As a result, I turned to VS Code, which offers a more robust environment for teamwork and adherence to software engineering principles.

Data Science

Data Science ML ML Engineer Data Scientist

Transforming financial analysis with CreditAI on Amazon Bedrock: Octus’s journey with AWS

AWS Machine Learning Blog

MARCH 10, 2025

Visit octus.com to learn how we deliver rigorously verified intelligence at speed and create a complete picture for professionals across the entire credit lifecycle. It also enables economies of scale with development velocity given that over 75 engineers at Octus already use AWS services for application development.

DevOps

DevOps Metadata Auto-complete Automation

Centralize model governance with SageMaker Model Registry Resource Access Manager sharing

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. It can take up to 20 minutes for the setup to complete.

ML

ML Machine Learning Auto-complete Auto-classification

Webinars

The Intersection of AI and Sales: Personalization Without Compromise

How to Achieve High-Accuracy Results When Using LLMs

Relevance, Reach, Revenue: How to Turn Marketing Trends From Hype to High-Impact

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Streamline diarization using AI as an assistive technology: ZOO Digital’s story

AWS Machine Learning Blog

FEBRUARY 20, 2024

This time-consuming process must be completed before content can be dubbed into another language. SageMaker asynchronous endpoints support upload sizes up to 1 GB and incorporate auto scaling features that efficiently mitigate traffic spikes and save costs during off-peak times. in a code subdirectory. in a code subdirectory.

Metadata

Metadata Auto-complete Machine Learning Deep Learning

MLOps Landscape in 2023: Top Tools and Platforms

The MLOps Blog

JUNE 27, 2023

When thinking about a tool for metadata storage and management, you should consider: General business-related items : Pricing model, security, and support. Flexibility, speed, and accessibility : can you customize the metadata structure? Can you see the complete model lineage with data/models/experiments used downstream?

Machine Learning

Machine Learning Metadata Data Scientist Data Quality

MLOps Is an Extension of DevOps. Not a Fork — My Thoughts on THE MLOPS Paper as an MLOps Startup CEO

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

DevOps Metadata Software Engineer Data Scientist

Get started quickly with AWS Trainium and AWS Inferentia using AWS Neuron DLAMI and AWS Neuron DLC

AWS Machine Learning Blog

JUNE 11, 2024

Launch the instance using Neuron DLAMI Complete the following steps: On the Amazon EC2 console, choose your desired AWS Region and choose Launch Instance. You can update your Auto Scaling groups to use new AMI IDs without needing to create new launch templates or new versions of launch templates each time an AMI ID changes.

Deep Learning

Deep Learning ML Automation Auto-complete

Host ML models on Amazon SageMaker using Triton: CV model with PyTorch backend

AWS Machine Learning Blog

MAY 31, 2023

Each model deployed with Triton requires a configuration file ( config.pbtxt ) that specifies model metadata, such as input and output tensors, model name, and platform. Set up your environment To set up your environment, complete the following steps: Launch a SageMaker notebook instance with a g5.xlarge xlarge instance.

ML

ML Auto-classification Auto-complete Natural Language Processing

Explore data with ease: Use SQL and Text-to-SQL in Amazon SageMaker Studio JupyterLab notebooks

AWS Machine Learning Blog

APRIL 16, 2024

To store information in Secrets Manager, complete the following steps: On the Secrets Manager console, choose Store a new secret. Complete the following steps: On the Secrets Manager console, choose Store a new secret. Varun Shah is a Software Engineer working on Amazon SageMaker Studio at Amazon Web Services.

Data Scientist

Data Scientist Generative AI Machine Learning Auto-complete

Google’s Dr. Arsanjani on Enterprise Foundation Model Challenges

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

Large Language Models Prompt Engineering Prompt Engineer Neural Network

Google’s Arsanjani on Enterprise Foundation Model Challenges

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

Large Language Models Prompt Engineering Prompt Engineer Neural Network

Managing Computer Vision Projects with Micha? Tadeusiak

The MLOps Blog

FEBRUARY 27, 2023

You would address it in a completely different way, depending on what’s the problem. 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 role have Auto ML models played in computer vision consultant capacity?

Computer Vision

Computer Vision Auto-classification Auto-complete ML

Run ML inference on unplanned and spiky traffic using Amazon SageMaker multi-model endpoints

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

ML Auto-complete Deep Learning Machine Learning

Bring your own ML model into Amazon SageMaker Canvas and generate accurate predictions

AWS Machine Learning Blog

MAY 2, 2023

Complete the following steps to use Autopilot AutoML to build, train, deploy, and share an ML model with a business analyst: Download the dataset , upload it to an Amazon S3 ( Amazon Simple Storage Service ) bucket, and make a note of the S3 URI. Complete the steps listed in the README file. Set the target column as churn.

ML

ML Data Scientist Auto-complete Metadata

Artificial Intelligence Zone

From Solo Notebooks to Collaborative Powerhouse: VS Code Extensions for Data Science and ML Teams

Transforming financial analysis with CreditAI on Amazon Bedrock: Octus’s journey with AWS

Webinars

Trending Sources

Centralize model governance with SageMaker Model Registry Resource Access Manager sharing

Webinars

Streamline diarization using AI as an assistive technology: ZOO Digital’s story

MLOps Landscape in 2023: Top Tools and Platforms

MLOps Is an Extension of DevOps. Not a Fork — My Thoughts on THE MLOPS Paper as an MLOps Startup CEO

Get started quickly with AWS Trainium and AWS Inferentia using AWS Neuron DLAMI and AWS Neuron DLC

Host ML models on Amazon SageMaker using Triton: CV model with PyTorch backend

Explore data with ease: Use SQL and Text-to-SQL in Amazon SageMaker Studio JupyterLab notebooks

Google’s Dr. Arsanjani on Enterprise Foundation Model Challenges

Google’s Arsanjani on Enterprise Foundation Model Challenges

Managing Computer Vision Projects with Micha? Tadeusiak

Run ML inference on unplanned and spiky traffic using Amazon SageMaker multi-model endpoints

Bring your own ML model into Amazon SageMaker Canvas and generate accurate predictions

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