article thumbnail

AI and coding: How Seattle tech companies are using generative AI for programming

Flipboard

Prompt: “A robot helping a software engineer develop code.” ” Generative AI is already changing the way software engineers do their jobs. The auto-complete and auto-suggestions in Visual Studio Code are pretty good, too, without being annoying. ” Kevin Leneway.

article thumbnail

MetaGPT: Complete Guide to the Best AI Agent Available Right Now

Unite.AI

To actualize an agile, flexible software architecture that can adapt to dynamic programming tasks. Agile Development SOPs act as a meta-function here, coordinating agents to auto-generate code based on defined inputs. The post MetaGPT: Complete Guide to the Best AI Agent Available Right Now appeared first on Unite.AI.

Python 328
professionals

Sign Up for our Newsletter

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

article thumbnail

Improved ML model deployment using Amazon SageMaker Inference Recommender

AWS Machine Learning Blog

A complete example is available in our GitHub notebook. To run the Inference Recommender job, complete the following steps: Create a SageMaker model by specifying the framework, version, and image scope: model = Model( model_data=model_url, role=role, image_uri = sagemaker.image_uris.retrieve(framework="xgboost", region=region, version="1.5-1",

ML 79
article thumbnail

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

AWS Machine Learning Blog

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. The way you craft a prompt can profoundly influence the nature and usefulness of the AI’s response.

article thumbnail

Google’s Dr. Arsanjani on Enterprise Foundation Model Challenges

Snorkel AI

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.

article thumbnail

Google’s Arsanjani on Enterprise Foundation Model Challenges

Snorkel AI

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.

article thumbnail

MLOps Landscape in 2023: Top Tools and Platforms

The MLOps Blog

The platform also offers features for hyperparameter optimization, automating model training workflows, model management, prompt engineering, and no-code ML app development. Can you see the complete model lineage with data/models/experiments used downstream? Is it fast and reliable enough for your workflow?