The framework offers a number of different libraries. These are available in source code, as executable installers, as well as NuGet packages. They can be downloaded directly from the NuGet source and used on the corresponding device. Accord.NET Framework supports numeric and linear algebra, numerical optimization, statistics and machine learning. Neural networks can also be implemented with the Accord.NET Framework.
Apache Mahout – Big Data Meets Machine Learning
Apache Mahout is a library of scalable machine learning algorithms based on Apache Hadoop and MapReduce. The advantage of the solution is that it works in big data environments. Apache Mahout allows you to work with Apache Hadoop. Statistical calculations can be performed.
Mahout is therefore an important open-source software when it comes to developing software in the field of artificial intelligence. The solution works with other big data products, such as Apache Spark. The interactive shell allows a direct connection to different apps. This is how to use a domain-specific language (DSL), which can be compared to R. If you know R, you can handle mahout quickly. Spark and speed can be used in parallel. Code that was written with the DSL for Sparking mostly with speed.
Mahout focuses on linear algebra. The distributed row matrix (distributed row matrix) can be used as a data type in mahout. Mahout is integrated into Apache Zeppelin . It is a solution that simplifies the collection and analysis of data from big data systems. Visualizations from ggplot, a plot system, and matplotlib, a program library for Python, can also be used in Mahout. The calculations can also be accelerated by graphics CPUs in the computer.
Spark MLlib is a machine learning library that lets Apache Spark make the most of machine learning with its other features. Spark MLlib can be used with Java, Scala, Python and R. MLlib uses the APIs of Spark and interacts with NumPy in Python. NumPy is a Python library that handles vectors, matrices, and multidimensional arrays. .
R libraries can also be used with Apache Spark and MLlib (from Spark 1.5). You can use any Hadoop data source to facilitate integration with Hadoop workflows. File systems such as HDFS, HBase and local files can be used in parallel. This allows data to be processed and shared for machine learning and data from big data environments.
The developers assume that the algorithms are sometimes a hundred times faster than MapReduce. Both systems are structured and unstructured at high speed. This is especially important for environments in the big data area. MLlib contains algorithms that better work with Apache Spark and thus provide better results than the One Pass approximations used in MapReduce.
The advantage of MLlib is that the library runs everywhere, including Spark, Hadoop, Apache Mesos and Kubernetes. Clusters can be used locally, but data in the cloud can also be used. MLlib so runs on clusters in the cloud, which in turn can use different data sources.
Spark can be operated with MLlib in its standalone cluster mode, on Amazon AWS (EC2), on Hadoop YARN, Mesos or with containers and Kubernetes. Data can be read from HDFS, Apache Cassandra, Apache HBase, Apache Hive and many other sources.
The H2O open source software combines machine learning capabilities with scalable in-memory big data processing . Machine learning can be used in combination with big data analytics. In H2O, the responsiveness of in-memory processing is combined with the ability to quickly serialize between nodes and clusters. H2O scales quickly and easily. The administration is currently carried out with a web-based flow GUI. POJOs can be deployed to obtain data for accurate predictions in any environment. A POJO is a Java object that has no limitations.
H2O can access HDFS directly, as well as data from Yarn, a big data analysis system, and MapReduce. H2O can also be started directly in Amazon AWS- EC2 instances. It can communicate with Java via Hadoop, but also Python, R and Scala can be used, including all supported packages.
Since H2O builds directly on HDFS, the solution achieves high performance when HDFS is used as a storage system. The KI framework H2O provides a set of algorithms to develop and manage AI, along with big data and machine learning. Examples are Deep Learning, Gradient Boosting and Generalized Linear Models. These are machine learning technologies with which, for example, regression analyzes can be carried out. Together with Apache Spark it can also be used for calculations and applications in the cloud . Insurance companies, for example, use H2O because complex calculations can be made here.
Oryx 2 – real-time machine learning
Oryx is software that uses data from Kafka and Spark. This allows data from big data analytics to be used for machine learning as well. Kafka and Spark are both big data systems. It is possible to perform machine learning in real time. Data can be processed in real time, from different sources.
Oryx 2 is based on the lambda architecture . This new type of data management is primarily used in big data environments. The architecture is therefore used with Apache Spark and Apache Kafka. The solution specializes in machine learning. The framework can be used to build applications, but also offers end-to-end applications for collaborative filtering, classifying, regression, and clustering.
Oryx 2 consists of three levels: The batch layer calculates historical data . This operation can take several hours and start several times a day. The velocity level creates and publishes the incremental model updates from a stream of new data . These updates can be on the order of seconds. The third level, the serving layer, receives models and updates. The data transport layer shifts data between layers and receives input from external sources.
Google DeepVariant is an AI gene sequencing software. The software can be operated in Google Cloud . The open source software is based on TensorFlow. It can use data from gene sequencing to calculate a genome. If the software is used in Google Cloud , you can book two models. Both use 1025 processor cores.
The faster model also uses graphic adapters from NVidia for the calculation. These can count on different speeds. Google DeepVariant makes very few mistakes when it comes to analyzing genes and can carry out genetic analyzes very quickly. Therefore, the software has also won prizes from the Food and Drug Administration (FDA). The AI-based software also works with neural networks.