ODSC - Open Data Science

FEBRUARY 24, 2023

Its goal is to help with a quick analysis of target characteristics, training vs testing data, and other such data characterization tasks. Apache Superset GitHub | Website Apache Superset is a must-try project for any ML engineer, data scientist, or data analyst. You can watch it on demand here.

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ODSC - Open Data Science

FEBRUARY 2, 2023

The role of a data scientist is in demand and 2023 will be no exception. To get a better grip on those changes we reviewed over 25,000 data scientist job descriptions from that past year to find out what employers are looking for in 2023. However, each year the skills and certainly the platforms change somewhat.

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AWS Machine Learning Blog

DECEMBER 4, 2024

Businesses are under pressure to show return on investment (ROI) from AI use cases, whether predictive machine learning (ML) or generative AI. Only 54% of ML prototypes make it to production, and only 5% of generative AI use cases make it to production. Using SageMaker, you can build, train and deploy ML models.

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Heartbeat

NOVEMBER 6, 2023

Image Source —  Pixel Production Inc In the previous article, you were introduced to the intricacies of data pipelines, including the two major types of existing data pipelines. You might be curious how a simple tool like Apache Airflow can be powerful for managing complex data pipelines.

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phData

MARCH 22, 2023

As companies continue to adopt machine learning (ML) in their workflows, the demand for scalable and efficient tools has increased. In this blog post, we will explore the performance benefits of Snowpark for ML workloads and how it can help businesses make better use of their data. Want to learn more? Can’t wait?

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Iguazio

DECEMBER 18, 2023

2023 was the year of generative AI, with applications like ChatGPT, Bard and others becoming so mainstream we almost forgot what it was like to live in a world without them. For data science practitioners, productization is key, just like any other AI or ML technology.

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AWS Machine Learning Blog

MARCH 15, 2024

Werner Vogels’s own words at AWS re:Invent 2023 , “every second that a person has a stroke counts.” Medical data restrictions You can use machine learning (ML) to assist doctors and researchers in diagnosis tasks, thereby speeding up the process. This isolated legacy data has the potential for massive impact if cumulated.

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AWS Machine Learning Blog

JUNE 18, 2024

On December 6 th -8 th 2023, the non-profit organization, Tech to the Rescue , in collaboration with AWS, organized the world’s largest Air Quality Hackathon – aimed at tackling one of the world’s most pressing health and environmental challenges, air pollution. She holds 30+ patents and has co-authored 100+ journal/conference papers.

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Alation

APRIL 14, 2023

And there’s a growing consensus that dumping all data into the cloud data warehouse is too expensive to be sustainable – though presenters expressed doubt that this may be the case for just small use cases. So, how can a data catalog support the critical project of building data pipelines?

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Iguazio

DECEMBER 18, 2023

2023 was the year of generative AI, with applications like ChatGPT, Bard and others becoming so mainstream we almost forgot what it was like to live in a world without them. For data science practitioners, productization is key, just like any other AI or ML technology.

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AWS Machine Learning Blog

DECEMBER 5, 2023

SGT release and deployment – The SGT that is output from the earlier optimization step is deployed as part of the data pipeline that feeds the trained LLM. Using Protopia AI Stained Glass, the unprotected sensitive data does not leave the enterprise’s site or trust zone.

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The MLOps Blog

JANUARY 26, 2024

Luckily, we have tried and trusted tools and architectural patterns that provide a blueprint for reliable ML systems. In this article, I’ll introduce you to a unified architecture for ML systems built around the idea of FTI pipelines and a feature store as the central component. But what is an ML pipeline?

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Snorkel AI

MAY 26, 2023

[link] Ahmad Khan, head of artificial intelligence and machine learning strategy at Snowflake gave a presentation entitled “Scalable SQL + Python ML Pipelines in the Cloud” about his company’s Snowpark service at Snorkel AI’s Future of Data-Centric AI virtual conference in August 2022. Welcome everybody.

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Snorkel AI

MAY 26, 2023

[link] Ahmad Khan, head of artificial intelligence and machine learning strategy at Snowflake gave a presentation entitled “Scalable SQL + Python ML Pipelines in the Cloud” about his company’s Snowpark service at Snorkel AI’s Future of Data-Centric AI virtual conference in August 2022. Welcome everybody.

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The MLOps Blog

JANUARY 23, 2023

Dolt LakeFS Delta Lake Pachyderm Git-like versioning Database tool Data lake Data pipelines Experiment tracking Integration with cloud platforms Integrations with ML tools Examples of data version control tools in ML DVC Data Version Control DVC is a version control system for data and machine learning teams.

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DagsHub

SEPTEMBER 10, 2023

Intermediate Data Pipeline : Build data pipelines using DVC for automation and versioning of Open Source Machine Learning projects. For that, DagsHub added Audio capabilities, enabling you to see its spectrogram, wave, and even listen to it! It is a fork of the brilliant buster project.

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Towards AI

OCTOBER 4, 2023

Last Updated on October 6, 2023 by Editorial Team Author(s): ????Mike Advanced-Data Engineering and ML Ops with Infrastructure as Code This member-only story is on us. This article is for data and ML Ops engineers who would want to deploy and update ML pipelines using CloudFormation templates.

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ODSC - Open Data Science

AUGUST 11, 2023

In this talk, you’ll learn how to transparently use AI in Incident Response and Threat Hunting with the help of the DS4N6 toolset (DAISY VM & CHRYSALIS) and learn about the most useful ML algorithms for this purpose.

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AWS Machine Learning Blog

SEPTEMBER 11, 2024

These activities cover disparate fields such as basic data processing, analytics, and machine learning (ML). ML is often associated with PBAs, so we start this post with an illustrative figure. The ML paradigm is learning followed by inference. The union of advances in hardware and ML has led us to the current day.

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Iguazio

JANUARY 22, 2024

You can watch the full talk this blog post is based on, which took place at ODSC West 2023, here. In addition, MLOps practices like building data, experting tracking, versioning, artifacts and others, also need to be part of the GenAI productization process. This helps cleanse the data.

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AWS Machine Learning Blog

AUGUST 21, 2024

By using the AWS SDK, you can programmatically access and work with the processed data, observability information, inference parameters, and the summary information from your batch inference jobs, enabling seamless integration with your existing workflows and data pipelines.

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Iguazio

FEBRUARY 8, 2024

They are characterized by their enormous size, complexity, and the vast amount of data they process. These elements need to be taken into consideration when managing, streamlining and deploying LLMs in ML pipelines, hence the specialized discipline of LLMOps. Continuous monitoring of resources, data, and metrics.

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Precisely

SEPTEMBER 18, 2023

Data observability is a key element of data operations (DataOps). It enables a big-picture understanding of the health of your organization’s data through continuous AI/ML-enabled monitoring – detecting anomalies throughout the data pipeline and preventing data downtime.

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DagsHub

DECEMBER 11, 2023

A complete overview revealing a diverse range of strengths and weaknesses for each data versioning tool. It does not support the ‘dvc repro’ command to reproduce its data pipeline. DVC Released in 2017, Data Version Control ( DVC for short) is an open-source tool created by iterative.

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Ocean Protocol

JANUARY 17, 2024

We launched Predictoor and its Data Farming incentives in September & November 2023, respectively. The repo provides starting-point predictoor bots, which gather historical CEX price data and build AI/ML models. on November 20, 2023; with subsequent v0.1.x About pdr-backend v0.1 x releases.

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AWS Machine Learning Blog

OCTOBER 7, 2024

You can use Amazon Kendra to quickly build high-accuracy generative AI applications on enterprise data and source the most relevant content and documents to maximize the quality of your Retrieval Augmented Generation (RAG) payload, yielding better large language model (LLM) responses than using conventional or keyword-based search solutions.

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Snorkel AI

JUNE 9, 2023

“You need to find a place to park your data. It needs to be optimized for the type of data and the format of the data you have,” he said. By optimizing every part of the data pipeline, he said, “You will, as a result, get your models to market faster.” Catch the sessions you missed!

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Snorkel AI

JUNE 9, 2023

“You need to find a place to park your data. It needs to be optimized for the type of data and the format of the data you have,” he said. By optimizing every part of the data pipeline, he said, “You will, as a result, get your models to market faster.” Catch the sessions you missed!

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Ocean Protocol

FEBRUARY 28, 2024

Introduction Ocean Protocol was founded to level the playing field for AI and data .In In 2023, we tested hypotheses towards growing traction. From that, the three brightest spots are Ocean Predictoor, Ocean Compute-to-Data (C2D), and Ocean Enterprise. Continually improve data pipeline and analytics dapp.

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Snorkel AI

JUNE 22, 2023

One should really think of us at the level of doing the technical implementation work around designing, developing and operationally deploying data products and services that use ML. I’ll give you a rough guide to what we’ll talk about—in the first place, a very macro and micro view of the importance of data.

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Snorkel AI

JUNE 22, 2023

One should really think of us at the level of doing the technical implementation work around designing, developing and operationally deploying data products and services that use ML. I’ll give you a rough guide to what we’ll talk about—in the first place, a very macro and micro view of the importance of data.

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IBM Journey to AI blog

JULY 17, 2023

is our enterprise-ready next-generation studio for AI builders, bringing together traditional machine learning (ML) and new generative AI capabilities powered by foundation models. 1 When comparing published 2023 list prices normalized for VPC hours of watsonx.data to several major cloud data warehouse vendors. IBM watsonx.ai

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Snorkel AI

MAY 12, 2023

Piyush Puri: Please join me in welcoming to the stage our next speakers who are here to talk about data-centric AI at Capital One, the amazing team who may or may not have coined the term, “what’s in your wallet.” What can get less attention is the foundational element of what makes AI and ML shine. That’s data.

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Snorkel AI

MAY 12, 2023

Piyush Puri: Please join me in welcoming to the stage our next speakers who are here to talk about data-centric AI at Capital One, the amazing team who may or may not have coined the term, “what’s in your wallet.” What can get less attention is the foundational element of what makes AI and ML shine. That’s data.

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phData

JULY 19, 2024

This article was co-written by Lawrence Liu & Safwan Islam While the title ‘ Machine Learning Engineer ’ may sound more prestigious than ‘Data Engineer’ to some, the reality is that these roles share a significant overlap. Generative AI has unlocked the value of unstructured text-based data.

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Mlearning.ai

FEBRUARY 16, 2023

Thus, the solution allows for scaling data workloads independently from one another and seamlessly handling data warehousing, data lakes , data sharing, and engineering. Machine Learning Integration Opportunities Organizations harness machine learning (ML) algorithms to make forecasts on the data.

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Snorkel AI

JULY 3, 2023

As the algorithms we use have gotten more robust and we have increased our compute power through new technologies, we haven’t made nearly as much progress on the data part of our jobs. Because of this, I’m always looking for ways to automate and improve our data pipelines. So why should we use data pipelines?

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