ODSC - Open Data Science

FEBRUARY 2, 2023

Much of what we found was to be expected, though there were definitely a few surprises. Machine Learning As machine learning is one of the most notable disciplines under data science, most employers are looking to build a team to work on ML fundamentals like algorithms, automation, and so on.

Data Science 98

Data Science Data Scientist Big Data Machine Learning 98

The MLOps Blog

JUNE 13, 2024

I started as a full-stack developer but have gradually moved toward data and ML engineering. My current role is MLOps engineer at Arbetsförmedlingen , Sweden’s largest employment agency. Automating just a single step in a previously entirely manual process already provides substantial value.

ML 59

ML Data Scientist Automation Software Development 59

Pickl AI

JANUARY 29, 2024

Opportunities abound in sectors like healthcare, finance, and automation. 2024 Tech breakdown: Understanding Data Science vs ML vs AI Quoting Eric Schmidt , the former CEO of Google, ‘There were 5 exabytes of information created between the dawn of civilisation through 2003, but that much information is now created every two days.’

Data Science 52

Data Science ML Machine Learning Artificial Intelligence 52

AWS Machine Learning Blog

AUGUST 20, 2024

Amazon SageMaker Ground Truth significantly reduces the cost and time required for labeling data by integrating human annotators with machine learning to automate the labeling process. Abhinay Sandeboina is a Engineering Manager at AWS Human In The Loop (HIL).

Software Engineer 110

Software Engineer ML Machine Learning Metadata 110

The MLOps Blog

JANUARY 23, 2023

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. Not a fork: – The MLOps team should consist of a DevOps engineer, a backend software engineer, a data scientist, + regular software folks.

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DevOps Metadata Software Engineer Data Scientist 59

ODSC - Open Data Science

JULY 19, 2023

Machine Learning Operations (MLOps) can significantly accelerate how data scientists and ML engineers meet organizational needs. A well-implemented MLOps process not only expedites the transition from testing to production but also offers ownership, lineage, and historical data about ML artifacts used within the team.

Machine Learning 52

Machine Learning Data Drift Data Science Data Scientist 52

AWS Machine Learning Blog

AUGUST 22, 2024

In the notebook, we already added the @step decorator at the beginning of each function definition in the cell where the function was defined, as shown in the following code. Data scientists, ML engineers, IT staff, and DevOps teams must work together to operationalize models from research to deployment and maintenance.

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Generative AI Metadata Python ML 119

AWS Machine Learning Blog

SEPTEMBER 1, 2023

After the completion of the research phase, the data scientists need to collaborate with ML engineers to create automations for building (ML pipelines) and deploying models into production using CI/CD pipelines. Only prompt engineering is necessary for better results.

Generative AI 126

Generative AI Prompt Engineer Prompt Engineering AI 126

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.

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Machine Learning ML Python Auto-complete 124

AWS Machine Learning Blog

SEPTEMBER 20, 2023

This approach is beneficial if you use AWS services for ML for its most comprehensive set of features, yet you need to run your model in another cloud provider in one of the situations we’ve discussed. Organizations can also use AWS Trainium and AWS Inferentia for better price-performance for running ML training jobs or inference.

ML 124

ML Python Deep Learning Machine Learning 124

The MLOps Blog

MARCH 28, 2023

The DevOps and Automation Ops departments are under the infrastructure team. The AI/ML teams are in the services department under infrastructure teams but related to AI, and a few AI teams are working on ML-based solutions that clients can consume. On top of the teams, they also have departments.

Machine Learning 59

Machine Learning Automation Data Scientist ML 59

Snorkel AI

JULY 5, 2023

RC : I have had ML engineers tell me, “You didn’t need to do feature selection anymore, and that you could just throw everything at the model and it will figure out what to keep and what to throw away.” But in other cases, as much as you can automate, the better you are. That is definitely a problem.

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Large Language Models Metadata Machine Learning AI 52

Snorkel AI

JULY 5, 2023

RC : I have had ML engineers tell me, “You didn’t need to do feature selection anymore, and that you could just throw everything at the model and it will figure out what to keep and what to throw away.” But in other cases, as much as you can automate, the better you are. That is definitely a problem.

Large Language Models 52

Large Language Models Metadata Machine Learning AI 52

Snorkel AI

JULY 5, 2023

RC : I have had ML engineers tell me, “You didn’t need to do feature selection anymore, and that you could just throw everything at the model and it will figure out what to keep and what to throw away.” But in other cases, as much as you can automate, the better you are. That is definitely a problem.

Large Language Models 52

Large Language Models Metadata Machine Learning AI 52

AWS Machine Learning Blog

NOVEMBER 9, 2023

Automated retraining mechanism – The training pipeline built with SageMaker Pipelines is triggered whenever a data drift is detected in the inference pipeline. Under Advanced Project Options , for Definition , select Pipeline script from SCM. This will enable us to test the pattern to trigger automated retraining of the model.

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Data Drift Auto-complete ML Automation 123

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. The new category is often called MLOps.

DevOps 140

DevOps Software Engineer Data Scientist ML 140

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. The integration of large language models helps humanize the interaction with automated agents, creating a more engaging and satisfying support experience.

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Generative AI Auto-complete AI Modeling Machine Learning 78

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

AWS Machine Learning Blog

AUGUST 1, 2023

Artificial intelligence (AI) and machine learning (ML) models have shown great promise in addressing these challenges. By automating the summarization process, doctors can quickly gain access to relevant information, allowing them to focus on patient care and make more informed decisions. No ML engineering experience required.

ML 86

ML Large Language Models NLP Machine Learning 86

The MLOps Blog

MARCH 15, 2023

Automation : Automating as many tasks to reduce human error and increase efficiency. Collaboration : Ensuring that all teams involved in the project, including data scientists, engineers, and operations teams, are working together effectively. But we chose not to go with the same in our deployment due to a couple of reasons.

ETL 52

ETL Data Drift Machine Learning ML 52

Snorkel AI

OCTOBER 12, 2023

In this session, Snorkel AI ML Engineer Ashwini Ramamoorthy explores how data-centric AI can be leveraged to simplify and streamline this process. To start, they highlight common but underrated challenges related to label schema definition, high cardinality, and multi-label problem formulations.

Data Scientist 64

Data Scientist AI AI Computer Vision 64

The MLOps Blog

JULY 18, 2023

It’s an automated chief of staff that automates conversational tasks. We are aiming to automate that functionality so that every worker in an organization can have access to that help, just like a CEO or someone else in the company would. Jason: Hi Sabine, how’s it going? Jason, you are the co-founder and CTO of Xembly.

Conversational AI 52

Conversational AI Natural Language Processing AI AI 52

Snorkel AI

OCTOBER 12, 2023

In this session, Snorkel AI ML Engineer Ashwini Ramamoorthy explores how data-centric AI can be leveraged to simplify and streamline this process. To start, they highlight common but underrated challenges related to label schema definition, high cardinality, and multi-label problem formulations.

Data Scientist 59

Data Scientist AI AI Computer Vision 59

Snorkel AI

OCTOBER 12, 2023

In this session, Snorkel AI ML Engineer Ashwini Ramamoorthy explores how data-centric AI can be leveraged to simplify and streamline this process. To start, they highlight common but underrated challenges related to label schema definition, high cardinality, and multi-label problem formulations.

Data Scientist 52

Data Scientist AI AI NLP 52

AWS Machine Learning Blog

NOVEMBER 29, 2023

With these tools in hand, the next challenge is to integrate LLM evaluation into the Machine Learning and Operation (MLOps) lifecycle to achieve automation and scalability in the process. Those metrics serve as a useful tool for automated evaluation, providing quantitative measures of lexical similarity between generated and reference text.

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LLM Data Scientist Algorithm Generative AI 107

AWS Machine Learning Blog

APRIL 29, 2024

Metaflow overview Metaflow was originally developed at Netflix to enable data scientists and ML engineers to build ML/AI systems quickly and deploy them on production-grade infrastructure. How Metaflow integrates with Trainium From a Metaflow developer perspective, using Trainium is similar to other accelerators.

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ML Python Data Scientist Deep Learning 123

AWS Machine Learning Blog

SEPTEMBER 26, 2024

Amazon SageMaker Studio provides a single web-based visual interface where different personas like data scientists, machine learning (ML) engineers, and developers can build, train, debug, deploy, and monitor their ML models. ML engineers require access to intermediate model artifacts stored in Amazon S3 from past training jobs.

ML Engineer 113

ML Engineer ML Big Data Data Scientist 113

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

AWS Machine Learning Blog

JANUARY 26, 2024

The goal of this post is to empower AI and machine learning (ML) engineers, data scientists, solutions architects, security teams, and other stakeholders to have a common mental model and framework to apply security best practices, allowing AI/ML teams to move fast without trading off security for speed.

Generative AI 139

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AWS Machine Learning Blog

JANUARY 13, 2023

Standardize building and reuse of AI solutions across business functions and AI practitioners’ personas, while ensuring adherence to enterprise best practices: Automate and standardize the repetitive undifferentiated engineering effort. Provide easy access to scalable computing resources. Model deployment.

ML 87

ML Data Scientist Machine Learning Metadata 87

The MLOps Blog

DECEMBER 29, 2022

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. Kyle: Yes, I can speak that you definitely can. So, you definitely can.

ML 52

ML Auto-complete Machine Learning Python 52