Data Science Blog

FEBRUARY 4, 2023

If the data sources are additionally expanded to include the machines of production and logistics, much more in-depth analyses for error detection and prevention as well as for optimizing the factory in its dynamic environment become possible.

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AWS Machine Learning Blog

APRIL 19, 2023

The DJL is a deep learning framework built from the ground up to support users of Java and JVM languages like Scala, Kotlin, and Clojure. With the DJL, integrating this deep learning is simple. The architecture of DJL is engine agnostic. Business requirements We are the US squad of the Sportradar AI department.

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Data Science Dojo

JULY 3, 2024

Machine Learning : Supervised and unsupervised learning algorithms, including regression, classification, clustering, and deep learning. Tools and frameworks like Scikit-Learn, TensorFlow, and Keras are often covered. Ensure that the bootcamp of your choice covers these specific topics.

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ODSC - Open Data Science

FEBRUARY 25, 2025

AI Engineering TrackBuild Scalable AISystems Learn how to bridge the gap between AI development and software engineering. This track will focus on AI workflow orchestration, efficient data pipelines, and deploying robust AI solutions. Join Us at ODSC 2025Secure Your Spot in the AI Revolution.

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Flipboard

NOVEMBER 24, 2023

Data scientists and ML engineers require capable tooling and sufficient compute for their work. Therefore, BMW established a centralized ML/deep learning infrastructure on premises several years ago and continuously upgraded it. He has a record of working with distributed teams across the globe within large enterprises.

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ODSC - Open Data Science

MAY 10, 2023

Many mistakenly equate tabular data with business intelligence rather than AI, leading to a dismissive attitude toward its sophistication. Standard data science practices could also be contributing to this issue. Feature engineering activities frequently focus on single-table data transformations, leading to the infamous “yawn factor.”

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ODSC - Open Data Science

APRIL 4, 2024

Find out how to weave data reliability and quality checks into the execution of your data pipelines and more. New Tool Thunder Hopes to Accelerate AI Development Thunder is a new compiler designed to turbocharge the training process for deep learning models within the PyTorch ecosystem. Learn more about them here!

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ODSC - Open Data Science

JANUARY 7, 2025

Machine Learning As machine learning is one of the most notable disciplines under data science, most employers are looking to build a team to work on ML fundamentals like algorithms, automation, and so on. Deep Learning Deep learning is a cornerstone of modern AI, and its applications are expanding rapidly.

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DagsHub

OCTOBER 23, 2024

With proper unstructured data management, you can write validation checks to detect multiple entries of the same data. Continuous learning: In a properly managed unstructured data pipeline, you can use new entries to train a production ML model, keeping the model up-to-date.

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IBM Journey to AI blog

AUGUST 11, 2023

Because ML is becoming more integrated into daily business operations, data science teams are looking for faster, more efficient ways to manage ML initiatives, increase model accuracy and gain deeper insights. MLOps is the next evolution of data analysis and deep learning. How MLOps will be used within the organization.

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Mlearning.ai

JULY 11, 2023

Before diving into the world of data science, it is essential to familiarize yourself with certain key aspects. The process or lifecycle of machine learning and deep learning tends to follow a similar pattern in most companies. In the data science industry, effective communication and collaboration play a crucial role.

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AWS Machine Learning Blog

MARCH 15, 2024

He has worked with multiple federal agencies to advance their data and AI goals. Nitin’s other focus areas include natural language processing (NLP), data pipelines, and generative AI. He applies his knowledge of cutting-edge research methods to the federal sector to deliver innovative technical papers, POCs, and MVPs.

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Pickl AI

SEPTEMBER 5, 2024

Data Preparation: Cleaning, transforming, and preparing data for analysis and modelling. Collaborating with Teams: Working with data engineers, analysts, and stakeholders to ensure data solutions meet business needs. Azure’s GPU and TPU instances further accelerate the training of deep learning models.

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DagsHub

AUGUST 23, 2024

Datafold is a tool focused on data observability and quality. It is particularly popular among data engineers as it integrates well with modern data pipelines (e.g., Source: [link] Monte Carlo is a code-free data observability platform that focuses on data reliability across data pipelines.

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phData

JUNE 7, 2023

The most critical and impactful step you can take towards enterprise AI today is ensuring you have a solid data foundation built on the modern data stack with mature operational pipelines, including all your most critical operational data. AI models can range from simple linear regressions to complex deep neural networks.

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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 models are large deep learning models that are trained on vast datasets, are adaptable to various tasks and specialize in NLP tasks. LLMOps is MLOps for LLMs.

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DagsHub

MAY 7, 2024

Machine learning, particularly its subsets, deep learning, and generative ML, is currently in the spotlight. Seamless integration into the workflow: Kolena can be integrated into existing data pipelines and CI systems using the kolena-client Python client, ensuring that data and models remain under user control at all times.

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DagsHub

MAY 7, 2024

Machine learning, particularly its subsets, deep learning, and generative ML, is currently in the spotlight. Seamless integration into the workflow: Kolena can be integrated into existing data pipelines and CI systems using the kolena-client Python client, ensuring that data and models remain under user control at all times.

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The MLOps Blog

AUGUST 11, 2023

This section delves into the common stages in most ML pipelines, regardless of industry or business function. 1 Data Ingestion (e.g., Apache Kafka, Amazon Kinesis) 2 Data Preprocessing (e.g., pandas, NumPy) 3 Feature Engineering and Selection (e.g., Scikit-learn, Feature Tools) 4 Model Training (e.g.,

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AWS Machine Learning Blog

SEPTEMBER 18, 2024

Zeta’s AI innovations over the past few years span 30 pending and issued patents, primarily related to the application of deep learning and generative AI to marketing technology. It simplifies feature access for model training and inference, significantly reducing the time and complexity involved in managing data pipelines.

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Mlearning.ai

JUNE 16, 2023

It also can minimize the risks of miscommunication in the process since the analyst and customer can align on the prototype before proceeding to the build phase Design: DALL-E, another deep learning model developed by OpenAI to generate digital images from natural language descriptions, can contribute to the design of applications.

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The MLOps Blog

MARCH 21, 2023

Other users Some other users you may encounter include: Data engineers , if the data platform is not particularly separate from the ML platform. Analytics engineers and data analysts , if you need to integrate third-party business intelligence tools and the data platform, is not separate. Allegro.io

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AWS Machine Learning Blog

OCTOBER 24, 2024

Large language models (LLMs) are very large deep-learning models that are pre-trained on vast amounts of data. LLMs have the potential to revolutionize content creation and the way people use search engines and virtual assistants. Data must be preprocessed to enable semantic search during inference.

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ODSC - Open Data Science

MARCH 12, 2025

20212024: Interest declined as deep learning and pre-trained models took over, automating many tasks previously handled by classical ML techniques. While traditional machine learning remains fundamental, its dominance has waned in the face of deep learning and automated machine learning (AutoML).

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