AWS Machine Learning Blog

JUNE 13, 2023

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.

Generative AI 66

Generative AI Auto-complete AI Modeling Machine Learning 66

AWS Machine Learning Blog

APRIL 27, 2023

Regular CPU systems are completely memory bound for these calculations, and performance is limited by the time required to move the data into the CPU. In Trainium, the GP-SIMD engines are tightly coupled with the on-chip caches using a high bandwidth streaming interface, which can sustain 2 TB/sec of memory bandwidth.

Auto-complete 74

Auto-complete Deep Learning ML Machine Learning 74

The MLOps Blog

JANUARY 23, 2023

I believe the team will look something like this: Software delivery reliability: DevOps engineers and SREs ( DevOps vs SRE here ) ML-specific software: software engineers and data scientists Non-ML-specific software: software engineers Product: product people and subject matter experts Wait, where is the MLOps engineer?

DevOps 59

DevOps Metadata Software Engineer Data Scientist 59

AWS Machine Learning Blog

MARCH 1, 2023

The ETL pipeline, MLOps pipeline, and ML inference should be rebuilt in a different AWS account. To solve this problem, we make the ML solution auto-deployable with a few configuration changes. ML engineers no longer need to manage this training metadata separately. We define another pipeline step, step_cond.

Automation 89

Automation ETL Data Drift ML 89

Pickl AI

JANUARY 23, 2023

Provides modularity as a series of completely configurable, independent modules that can be combined with the fewest restrictions possible. It is used by programmers to handle multidimensional arrays and gives users the option to optimize mathematical representations in ML applications. It is very fast and supports GPU.

Machine Learning 52

Machine Learning ML Neural Network Auto-classification 52

AWS Machine Learning Blog

SEPTEMBER 18, 2023

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.

Machine Learning 104

Machine Learning ML Python Auto-complete 104

The MLOps Blog

JUNE 27, 2023

Can you see the complete model lineage with data/models/experiments used downstream? Some of its features include a data labeling workforce, annotation workflows, active learning and auto-labeling, scalability and infrastructure, and so on. MLOps workflows for computer vision and ML teams Use-case-centric annotations.

Machine Learning 134

Machine Learning Metadata Data Quality Data Scientist 134

The MLOps Blog

JULY 18, 2023

But ideally, we strive for complete independence of the models in our system so that we can update them without then having to go update every other model in the pipeline – that’s a danger that you can run into. But it’s absolutely critical for most people in our space that you do some type of auto-scaling.

Conversational AI 52

Conversational AI Natural Language Processing AI AI 52

Mlearning.ai

MAY 3, 2023

autogpt : Auto-GPT is an “Autonomous AI agent” that given a goal in natural language, will allow Large Language Models (LLMs) to think, plan, and execute actions for us autonomously. The complete code of the APP can be found here. It is built on top of OpenAI’s Generative Pretrained Transformer (GPT-3.5 If you liked the blog post pls.

Large Language Models 52

Large Language Models Python Auto-complete Natural Language Processing 52

The MLOps Blog

DECEMBER 29, 2022

People will auto-scale up to 10 GPUs to handle the traffic. Does it mean that the production code has to be rewritten by, for example, ML engineers manually to be optimized for GPU with each update? Each of them may be with separate resource constraints, auto-scaling policies, and such. That’ll be easier short term.

ML 52

ML Auto-complete Machine Learning Python 52