article thumbnail

The Sequence Chat: Hugging Face's Leandro von Werra on StarCoder and Code Generating LLMs

TheSequence

data or auto-generated files). cell outputs) for code completion in Jupyter notebooks (see this Jupyter plugin ). Were there any research breakthroughs in StarCoder, or would you say it was more of a crafty ML engineering effort? In addition we labelled a PII dataset for code to train a PII detector.

article thumbnail

Promote pipelines in a multi-environment setup using Amazon SageMaker Model Registry, HashiCorp Terraform, GitHub, and Jenkins CI/CD

AWS Machine Learning Blog

Create a KMS key in the dev account and give access to the prod account Complete the following steps to create a KMS key in the dev account: On the AWS KMS console, choose Customer managed keys in the navigation pane. Under Advanced Project Options , for Definition , select Pipeline script from SCM. Choose Create key. Choose Save.

professionals

Sign Up for our Newsletter

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

article thumbnail

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

The MLOps Blog

Machine Learning Operations (MLOps): Overview, Definition, and Architecture” By Dominik Kreuzberger, Niklas Kühl, Sebastian Hirschl Great stuff. If you haven’t read it yet, definitely do so. How about the ML engineer? MLOps engineer today is either an ML engineer (building ML-specific software) or a DevOps engineer.

DevOps 59
article thumbnail

How Forethought saves over 66% in costs for generative AI models using Amazon SageMaker

AWS Machine Learning Blog

This post is co-written with Jad Chamoun, Director of Engineering at Forethought Technologies, Inc. and Salina Wu, Senior ML Engineer at Forethought Technologies, Inc. In addition, deployments are now as simple as calling Boto3 SageMaker APIs and attaching the proper auto scaling policies. 2xlarge instances.

article thumbnail

Orchestrate Ray-based machine learning workflows using Amazon SageMaker

AWS Machine Learning Blog

ML engineers must handle parallelization, scheduling, faults, and retries manually, requiring complex infrastructure code. In this post, we discuss the benefits of using Ray and Amazon SageMaker for distributed ML, and provide a step-by-step guide on how to use these frameworks to build and deploy a scalable ML workflow.

article thumbnail

Deploying Conversational AI Products to Production With Jason Flaks

The MLOps Blog

You need to have a structured definition around what you’re trying to do so your data annotators can label information for you. In our early days, we definitely landed on the notion that there are really two critical pieces to all meeting notes. Machines don’t deal well with ambiguity. Now, we’re not perfect.

article thumbnail

Deploying ML Models on GPU With Kyle Morris

The MLOps Blog

People will auto-scale up to 10 GPUs to handle the traffic. Navigating through current ML frameworks Stephen: Right. Kyle, you definitely touched upon this already. Pietra, in chat, also notes that before ML frameworks like TensorFlow, you had to go really low-level and code in a native CUDA. So, you definitely can.

ML 52