ODSC - Open Data Science

SEPTEMBER 21, 2023

Secondly, to be a successful ML engineer in the real world, you cannot just understand the technology; you must understand the business. The other tendency to watch out for in the real world (to go along with let’s use ML for everything ) is the worry that people have that ML is coming for their job and should not be trusted.

Machine Learning 98

Machine Learning ML Engineer ML Data Science 98

AWS Machine Learning Blog

MARCH 14, 2024

In this post we highlight how the AWS Generative AI Innovation Center collaborated with the AWS Professional Services and PGA TOUR to develop a prototype virtual assistant using Amazon Bedrock that could enable fans to extract information about any event, player, hole or shot level details in a seamless interactive manner.

Generative AI 94

Generative AI LLM AI AI 94

TheSequence

MAY 21, 2023

📢 Event: apply(risk), the ML Engineering Community Conference for Building Risk & Fraud Detection Systems Want to connect with the ML engineering community and learn best practices from ML practitioners at Affirm, Remitly, Block, Tide, and more, on how to build risk and fraud detection systems? .

Generative AI 64

Generative AI ML Engineer ETL LLM 64

AWS Machine Learning Blog

OCTOBER 20, 2023

This framework considers multiple personas and services to govern the ML lifecycle at scale. An ML engineer deploys the model pipeline into the ML team test environment using a shared services CI/CD process. After stakeholder validation, the ML model is deployed to the team’s production environment.

ML 95

ML Data Scientist ML Engineer Data Science 95

TheSequence

NOVEMBER 12, 2023

📌 ML Engineering Event: Join Meta, PepsiCo, RiotGames, Uber & more at apply(ops) apply(ops) is in two days! 🤖 Cool AI Tech Releases OpenAI Announcements OpenAI unveiled a series of new capabilities in terms of models, tools and developer toolkits at their DevDay event —> Read more.

OpenAI 64

OpenAI Generative AI ML Prompt Engineer 64

Heartbeat

FEBRUARY 1, 2023

Machine Learning Operations (MLOps) are the aspects of ML that deal with the creation and advancement of these models. In this article, we’ll learn everything there is to know about these operations and how ML engineers go about performing them. They may also be involved in the deployment process’s automation.

DevOps 96

DevOps Machine Learning Data Scientist Software Engineer 96

Heartbeat

OCTOBER 23, 2023

The Role of Data Scientists and ML Engineers in Health Informatics At the heart of the Age of Health Informatics are data scientists and ML engineers who play a critical role in harnessing the power of data and developing intelligent algorithms. Another exciting advancement is the integration of genomic and clinical data.

Data Scientist 52

Data Scientist Machine Learning Big Data Algorithm 52

AWS Machine Learning Blog

NOVEMBER 16, 2023

Amazon SageMaker provides purpose-built tools for machine learning operations (MLOps) to help automate and standardize processes across the ML lifecycle. In this post, we describe how Philips partnered with AWS to develop AI ToolSuite—a scalable, secure, and compliant ML platform on SageMaker.

Data Scientist 83

Data Scientist ML Data Science Automation 83

AWS Machine Learning Blog

FEBRUARY 27, 2024

Apply the trained model to make predictions of future events. Automation of building new projects based on the template is streamlined through AWS Service Catalog , where a portfolio is created, serving as an abstraction for multiple products. This is made possible by automating tedious, repetitive MLOps tasks as part of the template.

Machine Learning 106

Machine Learning DevOps Data Quality Data Scientist 106

AWS Machine Learning Blog

DECEMBER 13, 2023

To deliver value, they must integrate into existing production systems and infrastructure, which necessitates considering the entire ML lifecycle during design and development. ML operations, known as MLOps, focus on streamlining, automating, and monitoring ML models throughout their lifecycle.

ML 94

ML Automation Metadata Software Development 94

Heartbeat

OCTOBER 30, 2023

Ideally, the responsibilities of the ML engineering team should be completed once the model is deployed. Unforeseen events, interventions, or policy changes can impact the data distribution. But, as a data scientist or an ML engineer, you focus on the solutions rather than problems, right?

Machine Learning 52

Machine Learning Data Drift ML Data Scientist 52

BAIR

MARCH 11, 2024

Jobs Interested In: Research Scientist, ML Engineer Alicia Tsai Email: aliciatsai@berkeley.edu Website: [link] Advisor(s): Laurent El Ghaoui Research Blurb: My research delves into the theoretical aspects of deep implicit models, beginning with a unified "state-space" representation that simplifies notation.

Robotics 100

Robotics Natural Language Processing Machine Learning Computer Vision 100

Flipboard

MARCH 13, 2023

With terabytes of data generated by the product, the security analytics team focuses on building machine learning (ML) solutions to surface critical attacks and spotlight emerging threats from noise. In the event of failure in either of the pipelines, users are notified over the same communication channels.

Data Scientist 102

Data Scientist Auto-complete ML Machine Learning 102

Snorkel AI

JUNE 9, 2023

Snorkel Co-Founder and CEO Alex Ratner kicked off the day’s events by giving attendees a peek into Snorkel’s new Foundation Model Data Platform, which includes solutions to develop and adapt large language models and foundation models. If you registered for the event but didn't see all the sessions you wanted, you can now catch up.

Large Language Models 52

Large Language Models Data Scientist Machine Learning Computer Vision 52

Snorkel AI

JUNE 9, 2023

Snorkel Co-Founder and CEO Alex Ratner kicked off the day’s events by giving attendees a peek into Snorkel’s new Foundation Model Data Platform, which includes solutions to develop and adapt large language models and foundation models. If you registered for the event but didn't see all the sessions you wanted, you can now catch up.

Large Language Models 52

Large Language Models Data Scientist Machine Learning Computer Vision 52

Snorkel AI

MAY 24, 2023

From generative modeling to automated product tagging, cloud computing, predictive analytics, and deep learning, the speakers present a diverse range of expertise. Our speakers lead their fields and embody the desire to create revolutionary ML experiences by leveraging the power of data-centric AI to drive innovation and progress.

Machine Learning 52

Machine Learning Data Scientist Computer Vision ML 52

Snorkel AI

MAY 24, 2023

From generative modeling to automated product tagging, cloud computing, predictive analytics, and deep learning, the speakers present a diverse range of expertise. Our speakers lead their fields and embody the desire to create revolutionary ML experiences by leveraging the power of data-centric AI to drive innovation and progress.

Machine Learning 52

Machine Learning Data Scientist Computer Vision ML 52

AWS Machine Learning Blog

OCTOBER 19, 2023

Customers increasingly want to use deep learning approaches such as large language models (LLMs) to automate the extraction of data and insights. For many industries, data that is useful for machine learning (ML) may contain personally identifiable information (PII). The following diagram illustrates the SageMaker MLOps workflow.

Machine Learning 82

Machine Learning Python ML Automation 82

The MLOps Blog

JUNE 27, 2023

This includes features for hyperparameter tuning, automated model selection, and visualization of model metrics. They should also offer version control capabilities to manage the changes and revisions of ML artifacts, ensuring reproducibility and facilitating effective teamwork.

Machine Learning 134

Machine Learning Metadata Data Scientist Data Quality 134

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 116

Generative AI LLM ML AI 116

AWS Machine Learning Blog

JANUARY 6, 2023

Growth opportunities – AWS actively contributes to open-source projects such as Gluon and engages with regional developer and student communities through many events, online courses, and Amazon SageMaker Studio Lab , a no-cost SageMaker notebook environment. SageMaker launches at re:Invent 2022.

Data Scientist 67

Data Scientist ML Machine Learning Explainability 67

Snorkel AI

OCTOBER 12, 2023

The two-day event brought together researchers, practitioners, and industry leaders to discuss the latest trends and advances in data-centric AI, and we recorded each session as a video. We have now made all of the videos from the event publicly available on YouTube.

Data Scientist 64

Data Scientist AI AI ML 64

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. All the events throughout this process are tracked and logged for auditability and compliance.

ML 73

ML Data Scientist Machine Learning Metadata 73

Heartbeat

OCTOBER 5, 2023

Automation is critical, with techniques like pre-trained models, active learning, or weak supervision methods. Feature Engineering and Model Experimentation MLOps: Involves improving ML performance through experiments and feature engineering.

Large Language Models 52

Large Language Models DevOps Prompt Engineer Prompt Engineering 52

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

OCTOBER 12, 2023

The two-day event brought together researchers, practitioners, and industry leaders to discuss the latest trends and advances in data-centric AI, and we recorded each session as a video. We have now made all of the videos from the event publicly available on YouTube.

Data Scientist 59

Data Scientist AI AI ML 59

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

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. Policy 6 – Attach CloudWatchEventsFullAccess , which is an AWS managed policy that grants full access to CloudWatch Events. data/ mammo-train-dataset-part2.csv csv dataset.

Data Drift 94

Data Drift Auto-complete ML Automation 94

AWS Machine Learning Blog

APRIL 19, 2023

An event time feature is also required, which enables the feature store to track the history of feature values over time. For each new transaction event, the Lambda function first retrieves the batch and streaming features from Feature Store. The feature groups for our use case are shown in the following table.

ML 75

ML Data Scientist Big Data Machine Learning 75

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 93

Machine Learning ML Python Auto-complete 93

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. Robert, maybe you can lead that one.

Large Language Models 52

Large Language Models Metadata AI 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. Robert, maybe you can lead that one.

Large Language Models 52

Large Language Models Metadata AI AI 52

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. The original GPT3 was from 2018 and before, so it’s unaware of modern events.

Conversational AI 52

Conversational AI Natural Language Processing AI AI 52