AWS Machine Learning Blog

AUGUST 21, 2023

Amazon SageMaker Data Wrangler reduces the time it takes to collect and prepare data for machine learning (ML) from weeks to minutes. SageMaker Data Wrangler supports fine-grained data access control with Lake Formation and Amazon Athena connections.

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Flipboard

DECEMBER 11, 2024

Many of these applications are complex to build because they require collaboration across teams and the integration of data, tools, and services. Data engineers use data warehouses, data lakes, and analytics tools to load, transform, clean, and aggregate data. Big Data Architect.

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IBM Data Science in Practice

JANUARY 9, 2024

Each of these accelerators leverages state-of-the-art algorithms and machine learning techniques to identify anomalies accurately and in real-time. Solution 2: Migrate 3rd party models to MAS (Custom Model) This data science solution predicts anomalies in air compressor assets using an isolation forest model.

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The MLOps Blog

JUNE 27, 2023

How to evaluate MLOps tools and platforms Like every software solution, evaluating MLOps (Machine Learning Operations) tools and platforms can be a complex task as it requires consideration of varying factors. Pay-as-you-go pricing makes it easy to scale when needed.

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AWS Machine Learning Blog

MARCH 1, 2023

Flywheel creates a data lake (in Amazon S3) in your account where all the training and test data for all versions of the model are managed and stored. Periodically, the new labeled data (to retrain the model) can be made available to flywheel by creating datasets. The data can be accessed from AWS Open Data Registry.

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AWS Machine Learning Blog

SEPTEMBER 1, 2023

ML operationalization summary As defined in the post MLOps foundation roadmap for enterprises with Amazon SageMaker , ML and operations (MLOps) is the combination of people, processes, and technology to productionize machine learning (ML) solutions efficiently. For them, the end-to-end MLOps lifecycle and infrastructure is necessary.

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AWS Machine Learning Blog

NOVEMBER 22, 2023

Now all you need is some guidance on generative AI and machine learning (ML) sessions to attend at this twelfth edition of re:Invent. In this chalk talk, learn how to select and use your preferred environment to perform end-to-end ML development steps, from preparing data to building, training, and deploying your ML models.

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Flipboard

NOVEMBER 24, 2023

In an increasingly digital and rapidly changing world, BMW Group’s business and product development strategies rely heavily on data-driven decision-making. With that, the need for data scientists and machine learning (ML) engineers has grown significantly.

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IBM Journey to AI blog

MAY 9, 2023

After some impressive advances over the past decade, largely thanks to the techniques of Machine Learning (ML) and Deep Learning , the technology seems to have taken a sudden leap forward. It helps facilitate the entire data and AI lifecycle, from data preparation to model development, deployment and monitoring.

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AWS Machine Learning Blog

JUNE 18, 2024

More than 170 tech teams used the latest cloud, machine learning and artificial intelligence technologies to build 33 solutions. The output data is transformed to a standardized format and stored in a single location in Amazon S3 in Parquet format, a columnar and efficient storage format.

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AWS Machine Learning Blog

MAY 31, 2024

Data preparation Before creating a knowledge base using Knowledge Bases for Amazon Bedrock, it’s essential to prepare the data to augment the FM in a RAG implementation. He is passionate about cloud and machine learning.

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Pickl AI

FEBRUARY 21, 2023

It involves using statistical and computational techniques to identify patterns and trends in the data that are not readily apparent. Data mining is often used in conjunction with other data analytics techniques, such as machine learning and predictive analytics, to build models that can be used to make predictions and inform decision-making.

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IBM Journey to AI blog

DECEMBER 7, 2023

Increased operational efficiency benefits Reduced data preparation time : OLAP data preparation capabilities streamline data analysis processes, saving time and resources.

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AWS Machine Learning Blog

JUNE 22, 2023

Utilizing data streamed through LnW Connect, L&W aims to create better gaming experience for their end-users as well as bring more value to their casino customers. With predictive maintenance, L&W can get advanced warning of machine breakdowns and proactively dispatch a service team to inspect the issue.

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Flipboard

AUGUST 17, 2023

Amazon Redshift is the most popular cloud data warehouse that is used by tens of thousands of customers to analyze exabytes of data every day. AWS Glue is a serverless data integration service that makes it easy to discover, prepare, and combine data for analytics, ML, and application development.

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phData

SEPTEMBER 28, 2023

Dataflows represent a cloud-based technology designed for data preparation and transformation purposes. Dataflows have different connectors to retrieve data, including databases, Excel files, APIs, and other similar sources, along with data manipulations that are performed using Online Power Query Editor.

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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. Automated development: Automates data preparation, model development, feature engineering and hyperparameter optimization using AutoAI.

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Alation

FEBRUARY 13, 2020

Figure 1 illustrates the typical metadata subjects contained in a data catalog. Figure 1 – Data Catalog Metadata Subjects. Datasets are the files and tables that data workers need to find and access. They may reside in a data lake, warehouse, master data repository, or any other shared data resource.

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DataRobot Blog

OCTOBER 3, 2017

This highlights the two companies’ shared vision on self-service data discovery with an emphasis on collaboration and data governance. 2) When data becomes information, many (incremental) use cases surface. Standard Chartered Bank (SCB), a customer of Paxata, spoke about data democratization at SCB. DataRobot Data Prep.

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Precisely

JULY 18, 2024

Ensures consistent, high-quality data is readily available to foster innovation and enable you to drive competitive advantage in your markets through advanced analytics and machine learning. You must be able to continuously catalog, profile, and identify the most frequently used data. Increase metadata maturity.

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Pickl AI

SEPTEMBER 5, 2024

As businesses increasingly turn to cloud solutions, Azure stands out as a leading platform for Data Science, offering powerful tools and services for advanced analytics and Machine Learning. This roadmap aims to guide aspiring Azure Data Scientists through the essential steps to build a successful career.

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AWS Machine Learning Blog

AUGUST 4, 2023

Train a recommendation model in SageMaker Studio using training data that was prepared using SageMaker Data Wrangler. The real-time inference call data is first passed to the SageMaker Data Wrangler container in the inference pipeline, where it is preprocessed and passed to the trained model for product recommendation.

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IBM Journey to AI blog

MARCH 14, 2024

Try Db2 Warehouse SaaS on AWS for free   Netezza SaaS on AWS IBM® Netezza® Performance Server is a cloud-native data warehouse designed to operationalize deep analytics, data mining and BI by unifying, accessing and scaling all types of data across the hybrid cloud. Netezza

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ODSC - Open Data Science

NOVEMBER 19, 2024

ODSC West 2024 showcased a wide range of talks and workshops from leading data science, AI, and machine learning experts. This blog highlights some of the most impactful AI slides from the world’s best data science instructors, focusing on cutting-edge advancements in AI, data modeling, and deployment strategies.

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Pickl AI

OCTOBER 10, 2024

Summary: Data transformation tools streamline data processing by automating the conversion of raw data into usable formats. These tools enhance efficiency, improve data quality, and support Advanced Analytics like Machine Learning. Aggregation : Combining multiple data points into a single summary (e.g.,

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Data Quality AWS Machine Learning Machine Learning 52

Pickl AI

NOVEMBER 4, 2024

Role of Data Engineers in the Data Ecosystem Data Engineers play a crucial role in the data ecosystem by bridging the gap between raw data and actionable insights. They are responsible for building and maintaining data architectures, which include databases, data warehouses, and data lakes.

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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.

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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.

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Pickl AI

AUGUST 1, 2023

Key Components of Data Engineering Data Ingestion : Gathering data from various sources, such as databases, APIs, files, and streaming platforms, and bringing it into the data infrastructure. Data Processing: Performing computations, aggregations, and other data operations to generate valuable insights from the data.

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Snorkel AI

MARCH 2, 2023

From a software engineering perspective, machine-learning models, if you look at it in terms of the number of parameters and in terms of size, started out from the transformer models. So the application started to go from the pure software-engineering/machine-learning domain to industry and the sciences, essentially.

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Snorkel AI

MARCH 2, 2023

From a software engineering perspective, machine-learning models, if you look at it in terms of the number of parameters and in terms of size, started out from the transformer models. So the application started to go from the pure software-engineering/machine-learning domain to industry and the sciences, essentially.

Machine Learning 52

Machine Learning Machine Learning Data Preparation AI 52

Alation

FEBRUARY 13, 2020

Data Literacy—Many line-of-business people have responsibilities that depend on data analysis but have not been trained to work with data. Their tendency is to do just enough data work to get by, and to do that work primarily in Excel spreadsheets. Who needs data literacy training? Who can provide the training?

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The MLOps Blog

OCTOBER 20, 2023

Placing functions for plotting, data loading, data preparation, and implementations of evaluation metrics in plain Python modules keeps a Jupyter notebook focused on the exploratory analysis | Source: Author Using SQL directly in Jupyter cells There are some cases in which data is not in memory (e.g.,

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phData

DECEMBER 4, 2023

If you answer “yes” to any of these questions, you will need cloud storage, such as Amazon AWS’s S3, Azure Data Lake Storage or GCP’s Google Storage. Knowing this, you want to have data prepared in a way to optimize your load. It might be tempting to have massive files and let the system sort it out.

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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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The MLOps Blog

MAY 9, 2023

They run scripts manually to preprocess their training data, rerun the deployment scripts, manually tune their models, and spend their working hours keeping previously developed models up to date. Building end-to-end machine learning pipelines lets ML engineers build once, rerun, and reuse many times.

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