AWS Machine Learning Blog

MAY 23, 2023

With organizations increasingly investing in machine learning (ML), ML adoption has become an integral part of business transformation strategies. However, implementing ML into production comes with various considerations, notably being able to navigate the world of AI safely, strategically, and responsibly.

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IBM Journey to AI blog

AUGUST 11, 2023

Both computer scientists and business leaders have taken note of the potential of the data. Machine learning (ML), a subset of artificial intelligence (AI), is an important piece of data-driven innovation. MLOps is the next evolution of data analysis and deep learning. What is MLOps?

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ODSC - Open Data Science

MARCH 13, 2023

First, there’s a need for preparing the data, aka data engineering basics. Machine learning practitioners are often working with data at the beginning and during the full stack of things, so they see a lot of workflow/pipeline development, data wrangling, and data preparation.

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AWS Machine Learning Blog

FEBRUARY 22, 2023

In this post, we discuss how Boomi used the bring-your-own-container (BYOC) approach to develop a new AI/ML enabled solution for their customers to tackle the “blank canvas” problem. Boomi’s ML and data engineering teams needed the solution to be deployed quickly, in a repeatable and consistent way, at scale.

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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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AWS Machine Learning Blog

OCTOBER 18, 2023

Purina used artificial intelligence (AI) and machine learning (ML) to automate animal breed detection at scale. The solution focuses on the fundamental principles of developing an AI/ML application workflow of data preparation, model training, model evaluation, and model monitoring.

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AWS Machine Learning Blog

APRIL 26, 2023

Do you need help to move your organization’s Machine Learning (ML) journey from pilot to production? Most executives think ML can apply to any business decision, but on average only half of the ML projects make it to production. Challenges Customers may face several challenges when implementing machine learning (ML) solutions.

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AWS Machine Learning Blog

MAY 3, 2023

For any machine learning (ML) problem, the data scientist begins by working with data. This includes gathering, exploring, and understanding the business and technical aspects of the data, along with evaluation of any manipulations that may be needed for the model building process.

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ODSC - Open Data Science

JANUARY 29, 2024

The role of prompt engineer has attracted massive interest ever since Business Insider released an article last spring titled “ AI ‘Prompt Engineer Jobs: $375k Salary, No Tech Backgrund Required.” While many of us dream of having a job in AI that doesn’t require knowing AI tools and skillsets, that’s not actually the case.

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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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Towards AI

MAY 8, 2024

Last Updated on May 9, 2024 by Editorial Team Author(s): Stephen Chege-Tierra Insights Originally published on Towards AI. How will this impact Google Earth Engine? Let us look at what is in this tool and how you can integrate Vertex with the Google Earth engine to analyse and deploy geospatial Earth data. What is Vertex?

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AWS Machine Learning Blog

JANUARY 6, 2023

The recently published IDC MarketScape: Asia/Pacific (Excluding Japan) AI Life-Cycle Software Tools and Platforms 2022 Vendor Assessment positions AWS in the Leaders category. This was the first and only APEJ-specific analyst evaluation focused on AI life-cycle software from IDC. Vendor Requirements for the IDC MarketScape.

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AWS Machine Learning Blog

DECEMBER 1, 2023

The ability to quickly build and deploy machine learning (ML) models is becoming increasingly important in today’s data-driven world. However, building ML models requires significant time, effort, and specialized expertise. This is where the AWS suite of low-code and no-code ML services becomes an essential tool.

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Tableau

FEBRUARY 24, 2021

However, as we dug into the existing studies on data scientists something that we did not expect to find, but that emerged as consistent and important, was how diverse ‘data scientists’ were and how their roles changed in relation to specific data science processes.

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Iguazio

FEBRUARY 8, 2024

Data engineers, data scientists and other data professional leaders have been racing to implement gen AI into their engineering efforts. LLM examples include GPT, BERT, and similar advanced AI systems. They are characterized by their enormous size, complexity, and the vast amount of data they process.

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phData

FEBRUARY 7, 2024

On the client side, Snowpark consists of libraries, including the DataFrame API and native Snowpark machine learning (ML) APIs for model development (public preview) and deployment (private preview). Machine Learning Training machine learning (ML) models can sometimes be resource-intensive.

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IBM Journey to AI blog

MARCH 14, 2024

Businesses face significant hurdles when preparing data for artificial intelligence (AI) applications. The existence of data silos and duplication, alongside apprehensions regarding data quality, presents a multifaceted environment for organizations to manage.

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Towards AI

AUGUST 16, 2023

Last Updated on August 17, 2023 by Editorial Team Author(s): Jeff Holmes MS MSCS Originally published on Towards AI. Pietro Jeng on Unsplash MLOps is a set of methods and techniques to deploy and maintain machine learning (ML) models in production reliably and efficiently.

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DataRobot Blog

MARCH 16, 2023

Data scientists run experiments. They work with partners in IT to harden ML use cases into production systems. To work effectively, data scientists need agility in the form of access to enterprise data, streamlined tooling, and infrastructure that just works. Because the current ML lifecycle process is broken.

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ODSC - Open Data Science

SEPTEMBER 25, 2023

Be sure to check out his talk, “ Using Graphs for Large Feature Engineering Pipelines ,” there to learn more about GraphReduce and more! For readers who work in ML/AI, it’s well understood that machine learning models prefer feature vectors of numerical information.

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IBM Journey to AI blog

OCTOBER 20, 2023

Artificial intelligence platforms enable individuals to create, evaluate, implement and update machine learning (ML) and deep learning models in a more scalable way. AI platform tools enable knowledge workers to analyze data, formulate predictions and execute tasks with greater speed and precision than they can manually.

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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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Tableau

FEBRUARY 24, 2021

However, as we dug into the existing studies on data scientists something that we did not expect to find, but that emerged as consistent and important, was how diverse ‘data scientists’ were and how their roles changed in relation to specific data science processes.

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AWS Machine Learning Blog

NOVEMBER 29, 2023

Amazon SageMaker Studio provides a fully managed solution for data scientists to interactively build, train, and deploy machine learning (ML) models. Amazon SageMaker notebook jobs allow data scientists to run their notebooks on demand or on a schedule with a few clicks in SageMaker Studio.

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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. With watsonx.ai, businesses can effectively train, validate, tune and deploy AI models with confidence and at scale across their enterprise.

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Pickl AI

JULY 2, 2023

Amidst all the new developments, data bricks have emerged as a unified analytics platform. What is Databricks? It is a unified analytics platform that simplifies building big data and AI solutions. It brings together Data Engineering, Data Science, and Data Analytics.

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AWS Machine Learning Blog

MARCH 10, 2023

Amazon SageMaker Data Wrangler reduces the time it takes to aggregate and prepare data for machine learning (ML) from weeks to minutes in Amazon SageMaker Studio. Starting today, you can connect to Amazon EMR Hive as a big data query engine to bring in large datasets for ML.

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DataRobot Blog

APRIL 1, 2018

Today’s data management and analytics products have infused artificial intelligence (AI) and machine learning (ML) algorithms into their core capabilities. These modern tools will auto-profile the data, detect joins and overlaps, and offer recommendations. DataRobot Data Prep. Sallam | Shubhangi Vashisth. .

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ODSC - Open Data Science

AUGUST 9, 2023

This is a straightforward and mostly clear-cut question — most of us can likely classify a dish as a dessert or not simply by reading its name, which makes it an excellent candidate for a simple ML model. Step 3: Train, Test, and Evaluate Model Once the data is processed and transformed, we can split it into a training set and a testing set.

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phData

FEBRUARY 24, 2023

Alteryx provides organizations with an opportunity to automate access to data, analytics , data science, and process automation all in one, end-to-end platform. Its capabilities can be split into the following topics: automating inputs & outputs, data preparation, data enrichment, and data science.

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AWS Machine Learning Blog

APRIL 3, 2024

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. SageMaker 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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