AWS Machine Learning Blog

NOVEMBER 14, 2023

For this, we will use the pre-trained 7 billion parameter Llama2 model and fine-tune it on the databricks-dolly-15k dataset. This is why we will use the QLoRA method to quantize the weights during finetuning to reduce this computation cost. Next, we fine-tune Llama2 on the databricks-dolly-15k dataset using the QLoRA method.

LLM 89

LLM ML Natural Language Processing Machine Learning 89

Dlabs.ai

FEBRUARY 24, 2021

This article will show you how to: Setup and deploy MLflow on the Google Cloud Platform Introduce fresh, simple methods for tracking ML experiments Stop storing 100s of charts, files, and metrics locally Help your teammates optimize workflows Take your teamwork to the next level Why MLflow? But why focus on MLflow? What’s Next?

Auto-complete 52

Auto-complete Machine Learning ML Explainability 52

The MLOps Blog

OCTOBER 3, 2023

It was very interesting because by that point, I had approximately six years working with executive level leadership for companies like Autodesk, Teladoc, and other companies that are either very well known globally – or at least very, very well known domestically, within the US. How was my code?” There are three types of feature stores.

ML 52

ML Data Scientist Machine Learning Data Science 52

BAIR

FEBRUARY 17, 2024

In enterprises, our colleagues at Databricks found that 60% of LLM applications use some form of retrieval-augmented generation (RAG) , and 30% use multi-step chains. Why are developers building compound systems? Why Use Compound AI Systems? Why is that? Is this paradigm here to stay as models improve?

LLM 364

LLM Neural Network AI AI 364

AWS Machine Learning Blog

NOVEMBER 30, 2023

Databricks is getting up to 40% better price-performance with Trainium-based instances to train large-scale deep learning models. Trainium2’s performance, scale, and energy efficiency are some of the reasons why Anthropic has chosen to train its models on AWS, and will use Trainium2 for its future models. thousands of text documents).

Generative AI 125

Generative AI AI AI ML 125

BAIR

FEBRUARY 18, 2024

In enterprises, our colleagues at Databricks found that 60% of LLM applications use some form of retrieval-augmented generation (RAG) , and 30% use multi-step chains. Why are developers building compound systems? Why Use Compound AI Systems? Why is that? Is this paradigm here to stay as models improve?

LLM 100

LLM Neural Network AI AI 100

The MLOps Blog

JUNE 6, 2023

Bookmark for later Building MLOps Pipeline for NLP: Machine Translation Task [Tutorial] Building MLOps Pipeline for Time Series Prediction [Tutorial] Why do we need a model training pipeline? These stages form a directed acyclic graph (DAG) to represent the order of execution. So let’s begin with a quick overview of all of these.

ML 52

ML Machine Learning Auto-classification Auto-complete 52

Unite.AI

JUNE 28, 2023

While I had a strong foundation in understanding pain points, creating a roadmap, executing plans, building a team, and managing budgets, I found that crafting the right messaging that truly resonated with our target audience was one of my biggest obstacles. Notebooks facilitate experimentation, while feature sharing and reuse save time.

Data Scientist 264

Data Scientist Machine Learning Data Science ML Engineer 264

The MLOps Blog

MARCH 21, 2023

Why are you building an ML platform? ” — Conor Murphy , Lead Data Scientist at Databricks, in “Survey of Production ML Tech Stacks” at the Data+AI Summit 2022 Your team should be motivated by MLOps to show everything that goes into making a machine learning model, from getting the data to deploying and monitoring the model.

Machine Learning 69

Machine Learning Data Scientist ML Metadata 69