Pickl AI

JANUARY 29, 2024

Data Science Vs Machine Learning Vs AI Aspect Data Science Artificial Intelligence Machine Learning Definition Data Science is the field that deals with the extraction of knowledge and insights from data through various processes. AI Engineer, Machine Learning Engineer, and Robotics Engineer are prominent roles in AI.

Data Science 52

Data Science ML Machine Learning Artificial Intelligence 52

Mlearning.ai

FEBRUARY 9, 2023

In industrial applications of Data Science, model complexity, model explainability, efficiency, and ease of deployment play a large role, even if that means you’re settling for a slightly less accurate model. Model explainability is an important skill for a Data Scientist’s job. This is even more common for first-time baseline models.

Data Scientist 52

Data Scientist Data Science ML Machine Learning 52

TheSequence

MAY 24, 2023

Could you explain the data curation and training process required for building such a model? Were there any research breakthroughs in StarCoder, or would you say it was more of a crafty ML engineering effort? StarChat (alpha) is even better at that since it was specifically fine-tuned on conversations and instructions.

Auto-complete 59

Auto-complete Python ML BERT 59

O'Reilly Media

OCTOBER 19, 2021

This is both frustrating for companies that would prefer making ML an ordinary, fuss-free value-generating function like software engineering, as well as exciting for vendors who see the opportunity to create buzz around a new category of enterprise software. Feature Engineering. The new category is often called MLOps.

DevOps 140

DevOps Software Engineer Data Scientist ML 140

O'Reilly Media

JANUARY 17, 2023

This mindset has followed me into my work in ML/AI. Because if companies use code to automate business rules, they use ML/AI to automate decisions. Given that, what would you say is the job of a data scientist (or ML engineer, or any other such title)? But first, let’s talk about the typical ML workflow.

Automation 132

Automation Robotics Data Scientist Software Development 132

The MLOps Blog

AUGUST 11, 2023

At that point, the Data Scientists or ML Engineers become curious and start looking for such implementations. But some of these queries are still recurrent and haven’t been explained well. Here, the DAGs represent workflows comprising units embodying job definitions for operations to be carried out, known as Steps.

ML 52

ML Machine Learning Data Ingestion Neural Network 52

AWS Machine Learning Blog

NOVEMBER 9, 2023

Under Advanced Project Options , for Definition , select Pipeline script from SCM. This collaboration ensures that your MLOps platform can adapt to evolving business needs and accelerates the adoption of ML across teams. Machine Learning Engineer with AWS Professional Services. Select This project is parameterized.

Data Drift 125

Data Drift Auto-complete ML Automation 125

The MLOps Blog

OCTOBER 3, 2023

Mikiko Bazeley: You definitely got the details correct. I joined FeatureForm last October, and before that, I was with Mailchimp on their ML platform team. I definitely don’t think I’m an influencer. I see so many of these job seekers, especially on the MLOps side or the ML engineer side.

ML 52

ML Data Scientist Machine Learning Data Science 52

The MLOps Blog

JULY 18, 2023

Sabine: Right, so, Jason, to kind of warm you up a bit… In 1 minute, how would you explain conversational AI? You need to have a structured definition around what you’re trying to do so your data annotators can label information for you. Jason: Yeah, that’s really true. What is conversational AI?

Conversational AI 52

Conversational AI Natural Language Processing AI AI 52

The MLOps Blog

MARCH 15, 2023

ML model explainability: Make sure the ML model is interpretable and understandable by the developers as well as other stakeholders and that the value addition provided can be easily quantified. For an experienced Data Scientist/ML engineer, that shouldn’t come as so much of a problem.

ETL 52

ETL Data Drift Machine Learning ML 52

The MLOps Blog

MARCH 21, 2023

As the number of ML-powered apps and services grows, it gets overwhelming for data scientists and ML engineers to build and deploy models at scale. In this comprehensive guide, we’ll explore everything you need to know about machine learning platforms, including: Components that make up an ML platform.

Machine Learning 69

Machine Learning Data Scientist ML Metadata 69

Unite.AI

FEBRUARY 22, 2024

The very definition of ethical AI is subjective, giving rise to crucial questions about who should have the authority to decide what constitutes Responsible AI. This legal requirement underscores the significance of explainability in the financial sector , where accurate predictions shape investment choices and economic trajectories.

Responsible AI 279

Responsible AI Explainability AI AI 279

AWS Machine Learning Blog

JANUARY 13, 2023

SageMaker hosting services are used to deploy models, while SageMaker Model Monitor and SageMaker Clarify are used to monitor models for drift, bias, custom metric calculators, and explainability. A traditional ML project lifecycle starts with finding data. The following sections describe these services in detail. Data service.

ML 88

ML Data Scientist Machine Learning Metadata 88

The MLOps Blog

DECEMBER 29, 2022

How would you explain deploying models on GPU in one minute? 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

ML Auto-complete Machine Learning Python 52