AWS Machine Learning Blog

FEBRUARY 26, 2024

About the Authors Bruno Klein is a Senior Machine Learning Engineer with AWS Professional Services Analytics Practice. He helps customers implement big data and analytics solutions. Rushabh Lokhande is a Senior Data & ML Engineer with AWS Professional Services Analytics Practice.

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Large Language Models LLM Big Data Natural Language Processing 112

AWS Machine Learning Blog

OCTOBER 20, 2023

Customers of every size and industry are innovating on AWS by infusing machine learning (ML) into their products and services. Recent developments in generative AI models have further sped up the need of ML adoption across industries.

ML 101

ML Data Scientist ML Engineer Data Science 101

TheSequence

NOVEMBER 5, 2023

Frank Liu, head of AI & ML at Zilliz, the company behind widely adopted open source vector database Milvus, shares his red hot takes on the latest topics in AI, ML, LLMs and more! 🛠 Real World ML LLM Architectures at GitHub GitHub ML engineers discuss the architecture of LLMs apps —> Read more.

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ML ML Engineer Chatbots Big Data 52

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.

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Generative AI LLM ML AI 122

Pickl AI

JANUARY 30, 2023

The role of Python is not just limited to Data Science. It’s a universal programming language that finds application in different technologies like AI, ML, Big Data and others. Data Visualization: Use libraries such as Matplotlib, Seaborn, Plotly, etc., to visualize and understand data and model performance.

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Python DevOps Machine Learning Automation 97

Flipboard

NOVEMBER 24, 2023

With that, the need for data scientists and machine learning (ML) engineers has grown significantly. Data scientists and ML engineers require capable tooling and sufficient compute for their work. Data scientists and ML engineers require capable tooling and sufficient compute for their work.

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ML DevOps Data Scientist ML Engineer 95

Pickl AI

JULY 14, 2023

However, symbolic AI faced limitations in handling uncertainty and dealing with large-scale data. Machine Learning and Neural Networks (1990s-2000s): Machine Learning (ML) became a focal point, enabling systems to learn from data and improve performance without explicit programming.

Neural Network 52

Neural Network Software Engineer Artificial Intelligence Artificial Intelligence 52

Mlearning.ai

FEBRUARY 9, 2023

Working as a Data Scientist — Expectation versus Reality! 11 key differences in 2023 Photo by Jan Tinneberg on Unsplash Working in Data Science and Machine Learning (ML) professions can be a lot different from the expectation of it. There’s a lot more to a data science role than building ML models.

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Data Scientist Data Science ML Machine Learning 52

AWS Machine Learning Blog

MARCH 30, 2023

The result is a machine learning (ML)-powered insight that allows fans to easily evaluate and compare the goalkeepers’ proficiencies. An ML model is trained through Amazon SageMaker , using data from four seasons of the first and second Bundesliga, encompassing all shots that landed on target (either resulting in a goal or being saved).

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Machine Learning Data Scientist ML Data Science 70

AWS Machine Learning Blog

NOVEMBER 9, 2023

Building out a machine learning operations (MLOps) platform in the rapidly evolving landscape of artificial intelligence (AI) and machine learning (ML) for organizations is essential for seamlessly bridging the gap between data science experimentation and deployment while meeting the requirements around model performance, security, and compliance.

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Data Drift Auto-complete ML Automation 99

AWS Machine Learning Blog

MARCH 30, 2023

His skills and areas of expertise include application development, data science, machine learning, and big data. He supports customers in developing data-driven applications within the cloud. Fotinos Kyriakides is an ML Engineer with AWS Professional Services.

Machine Learning 72

Machine Learning Data Scientist Metadata Data Science 72

AWS Machine Learning Blog

APRIL 19, 2023

Businesses are increasingly using machine learning (ML) to make near-real-time decisions, such as placing an ad, assigning a driver, recommending a product, or even dynamically pricing products and services. As a result, some enterprises have spent millions of dollars inventing their own proprietary infrastructure for feature management.

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ML Data Scientist Big Data Machine Learning 77

AWS Machine Learning Blog

FEBRUARY 28, 2024

About the Authors Sanjeeb Panda is a Data and ML engineer at Amazon. With the background in AI/ML, Data Science and Big Data, Sanjeeb design and develop innovative data and ML solutions that solve complex technical challenges and achieve strategic goals for global 3P sellers managing their businesses on Amazon.

Metadata 111

Metadata LLM Generative AI NLP 111

AWS Machine Learning Blog

JUNE 26, 2023

Machine learning (ML) administrators play a critical role in maintaining the security and integrity of ML workloads. ML administrators can tailor the roles to meet specific requirements by modifying the permissions associated with each persona. SageMaker Role Manager also allows for fine-grained customization.

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ML Data Scientist Automation Machine Learning 71

The MLOps Blog

AUGUST 11, 2023

There comes a time when every ML practitioner realizes that training a model in Jupyter Notebook is just one small part of the entire project. Getting a workflow ready which takes your data from its raw form to predictions while maintaining responsiveness and flexibility is the real deal.

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ML Machine Learning Data Ingestion Deep Learning 52

AWS Machine Learning Blog

JANUARY 6, 2023

The vendors evaluated for this MarketScape offer various software tools needed to support end-to-end machine learning (ML) model development, including data preparation, model building and training, model operation, evaluation, deployment, and monitoring. AI life-cycle tools are essential to productize AI/ML solutions.

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Data Scientist ML Machine Learning Explainability 69

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 ML Engineer 52

The MLOps Blog

JUNE 27, 2023

Alignment to other tools in the organization’s tech stack Consider how well the MLOps tool integrates with your existing tools and workflows, such as data sources, data engineering platforms, code repositories, CI/CD pipelines, monitoring systems, etc. and Pandas or Apache Spark DataFrames.

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Machine Learning Metadata Data Quality Data Scientist 134

DataRobot Blog

SEPTEMBER 13, 2022

What Do Data Scientists Do? Data scientists drive business outcomes. Many implement machine learning and artificial intelligence to tackle challenges in the age of Big Data. What data scientists do is directly tied to an organization’s AI maturity level. BARC ANALYST REPORT. Download Now. Systemization.

Data Scientist 52

Data Scientist Automation ML Data Ingestion 52

AWS Machine Learning Blog

AUGUST 31, 2023

As Artificial Intelligence (AI) and Machine Learning (ML) technologies have become mainstream, many enterprises have been successful in building critical business applications powered by ML models at scale in production.

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ML Data Scientist Machine Learning Automation 91

AWS Machine Learning Blog

FEBRUARY 29, 2024

Creating scalable and efficient machine learning (ML) pipelines is crucial for streamlining the development, deployment, and management of ML models. Configuration files (YAML and JSON) allow ML practitioners to specify undifferentiated code for orchestrating training pipelines using declarative syntax.

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Automation Python Machine Learning ML 92

AWS Machine Learning Blog

APRIL 11, 2023

These customers need to balance governance, security, and compliance against the need for machine learning (ML) teams to quickly access their data science environments in a secure manner. Rajesh Ramchander is a Senior Data & ML Engineer in Professional Services at AWS.

IDP 70

IDP Explainability Big Data ML 70

Snorkel AI

JULY 5, 2023

Abhishek Ratna, in AI ML marketing, and TensorFlow developer engineer Robert Crowe, both from Google, spoke as part of a panel entitled “Practical Paths to Data-Centricity in Applied AI” at Snorkel AI’s Future of Data-Centric AI virtual conference in August 2022. Is more data always better? AR : Absolutely.

Large Language Models 52

Large Language Models Metadata AI AI 52

Snorkel AI

JULY 5, 2023

Abhishek Ratna, in AI ML marketing, and TensorFlow developer engineer Robert Crowe, both from Google, spoke as part of a panel entitled “Practical Paths to Data-Centricity in Applied AI” at Snorkel AI’s Future of Data-Centric AI virtual conference in August 2022. Is more data always better? AR : Absolutely.

Large Language Models 52

Large Language Models Metadata AI AI 52

AWS Machine Learning Blog

AUGUST 31, 2023

As Artificial Intelligence (AI) and Machine Learning (ML) technologies have become mainstream, many enterprises have been successful in building critical business applications powered by ML models at scale in production.

ML 52

ML Data Scientist Machine Learning Automation 52

Flipboard

MARCH 7, 2023

Amazon SageMaker Studio is a web-based, integrated development environment (IDE) for machine learning (ML) that lets you build, train, debug, deploy, and monitor your ML models. Studio provides all the tools you need to take your models from data preparation to experimentation to production while boosting your productivity.

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Python Automation Data Scientist ML 93

Snorkel AI

MAY 24, 2023

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. Gideon Mann is the head of the ML Product and Research team in the Office of the CTO at Bloomberg LP. Archana Sapkota is a machine learning manager at Wayfair.

Machine Learning 52

Machine Learning Data Scientist Computer Vision ML 52

Snorkel AI

MAY 24, 2023

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. Gideon Mann is the head of the ML Product and Research team in the Office of the CTO at Bloomberg LP. Archana Sapkota is a machine learning manager at Wayfair.

Machine Learning 52

Machine Learning Data Scientist Computer Vision ML 52