Search Results for "spark"
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7 projects for "spark" with 2 filters applied:
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Custom VMs From 1 to 96 vCPUs With 99.95% UptimeLive migration and automatic failover keep workloads online through maintenance. One free e2-micro VM every month.
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$300 Free Credits to Build on Google CloudPut your $300 in credit toward real workloads, then keep building with free monthly usage for 20+ products. No commitment and no charge until you upgrade.
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mlforecast
Scalable machine learning for time series forecasting
...It supports multi-series forecasting, meaning you can train one model that forecasts many time series at once (common in retail, demand forecasting, etc.), rather than one model per series. The library is built to scale: behind the scenes, it can leverage distributed computing frameworks (Spark, Dask, Ray) when datasets or the number of series grow large. -
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MLflow
Open source platform for the machine learning lifecycle
MLflow is a platform to streamline machine learning development, including tracking experiments, packaging code into reproducible runs, and sharing and deploying models. MLflow offers a set of lightweight APIs that can be used with any existing machine learning application or library (TensorFlow, PyTorch, XGBoost, etc), wherever you currently run ML code (e.g. in notebooks, standalone applications or the cloud). -
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SparrowRecSys
A Deep Learning Recommender System
...SparrowRecSys supports a wide range of state-of-the-art recommendation algorithms, including models for click-through rate prediction and user behavior modeling that are widely used in advertising and content recommendation systems. The system is designed as a modular platform combining technologies such as Spark, TensorFlow, and web server components to represent the full lifecycle of recommendation pipelines. -
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Weld
High-performance runtime for data analytics applications
...This approach reduces data movement between libraries and enables the system to generate highly optimized machine code for parallel execution. Weld is particularly useful for workloads involving large-scale data processing in frameworks such as NumPy, Spark, and TensorFlow. The language includes built-in constructs for expressing data-parallel operations, enabling efficient execution on modern hardware architectures. By combining operations from multiple libraries into a single optimized execution plan, Weld can significantly improve performance in analytics and machine learning pipelines. -
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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spark-ml-source-analysis
Spark ml algorithm principle analysis and specific source code
spark-ml-source-analysis is a technical repository that analyzes the internal implementation of machine learning algorithms within Apache Spark’s MLlib library. The project aims to help developers and data scientists understand how distributed machine learning algorithms are implemented and optimized inside the Spark ecosystem. Instead of providing a runnable software system, the repository focuses on explaining algorithm principles and examining the underlying source code used in Spark’s machine learning package. ... -
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H2O-3
H2O is an Open Source, Distributed, Fast & Scalable Machine Learning
...The platform provides interfaces for multiple programming languages such as Python, R, Java, and Scala, making it accessible to a wide range of developers and data scientists. H2O-3 integrates with big data technologies such as Hadoop and Apache Spark, enabling organizations to run machine learning workflows on large-scale data infrastructure. The platform also includes a web-based interface called Flow that allows users to build models interactively through notebooks and visual tools. -
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Spark Python Notebooks
Apache Spark & Python (pySpark) tutorials for Big Data Analysis
Spark Python Notebooks is a curated collection of example Jupyter notebooks designed to help developers and data engineers learn Apache Spark using Python in an interactive environment. Rather than only providing static code files, this project uses notebooks to teach practical data processing workflows, exposing users to real Spark programming patterns like working with RDDs, DataFrames, and distributed computations.
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