The following is a list of the most commonly used tools and workbenches for machine learning. These are specific to machine learning only. This list does not include any library or frameworks. These are tools and workbenches only. Most offering machine learning tools will include the following features:
- Easy drag and drop capabilities
- Data collection
- Data preparation and cleaning
- Model building
- Data Visualization
- Model Deployment
- Integration with other tools and languages
As more and more organizations implement machine learning, there are two core aims they want to achieve.
- Employee Productivity: Who wants to spend days or weeks writing mundane code to load data, clean data, etc etc etc. No one wants to do this and especially employers don’t want their staff wasting time on this. Instead they are happy to invest in tools and workbenches where a lot or most or all of these mundane tasks are automated for you. You can not concentrate on the important tasks of adding value to your organisation. This saves money, improves employee productivity and employee value.
- Integration with Technical Architecture: Many of these tools and workbenches allow for easy integration with the technical architecture and thereby allowing easy and quick integration of machine learning withe the day to day activities of the organization. This saves money, improves employee productivity and employee value.
SAS software has been around for every and is the great grand-daddy of analytics and machine learning. They have built a large number of machine learning tools and solutions built upon these for various industries. Their core machine learning tools include SAS Enterprise Miner and SAS Visual Data Mining and Machine Learning.
SAP Leonardo is a cloud based platform for machine learning and supports tight integration with other SAP software.
Oracle have a number of machine learning tools and supports for the main machine learning languages. They have built a large number of applications (both cloud and on-premises) with in-built machine learning. Their main tools for machine learning include Oracle Data Miner, Oracle Machine Learning and Oracle Analytics (OAC or DVD versions)
If you work with hadoop and big data then you are probably using Cloudera in some way. Cloudera have hired Hilary Mason as their GM of ML. By taking an “AI factory” approach to turning data into decisions, you can make the process of building, scaling, and deploying enterprise ML and AI solutions automated, repeatable, and predictable—boring even. Cloudera Data Science Workbench is their solution.
IBM have a number of machine learning tools, one of them being a long standing member of the machine learning community, SPSS Modeler. Other machine learning tools include Watson Studio, IBM Machine Learning for z/OS, and IBM Watson Explorer.
Google have a large number of machine learning solutions including everything from traditional machine learning, into NLP, in Image processing, Video processing, etc. It’s a long list. Many of these come with various APIs to access these features. Most of these revolve around their Google AI Cloud offering. But sticking with the tools and workbenches we have AI Platform Notebooks, Kubeflow, and BigQuery ML.
TensorBoard is a suite of tools for graphical representation of different aspects and stages of machine learning in TensorFlow.
A bit like Goolge, Amazon has a large number of solutions for machine learning and AI, and most of these are available via an API or some cloud service. Amazon SageMaker is their main service.
Looker connects directly with Google BQML reduces additional complexity for data scientists by eliminating the need to move outputs of predictive models back into the database for use, while also increases the time-to-value for business users, allowing them to operationalize the outputs of predictive metrics to make better decisions every day.
Weka has been around for a long time and still popular in some research groups. Weka is a collection of machine learning algorithms for data mining tasks. It contains tools for data preparation, classification, regression, clustering, association rules mining, and visualization.
RapidMiner Studio has been around for a long time and is one of the few more visual workflow tools (that everyone else should be doing).
From the people who created Spark, we have another notebook solution for your machine learning projects called Databricks Workbench.
KNIME Analytics Platform is the open source software for creating data science applications and services.
Dataiki Data Science (DSS) is a collaborative data science software workflow platform enabling data exploration, prototyping and delivery of analytical and machine learning solutions.
I’ve not included the tools like R Studio and Notebooks in this list as they don’t really address the aims listed above. But you will notice a lot of the above solutions are really Jupyter Notebooks. Most of these vendors have a long way to go to make the tasks of machine learning boring.
This list does not cover all available tools and workbenches, but it does list the most common one you will come across.
When working with analytics, in whatever flavor, one of the key things you need is some data. But data comes in many different shapes and sizes, but where can you get some useful data, be it transactional, time-series, meta-data, analytical, master, categorical, numeric, regression, clustering, etc.
Many of the popular analytics languages have some data sets built into them. For example the R language comes pre-loaded with data sets and these can be accessed using
but many of the R packages also come with data sets.
Similarly if you are using Python, it comes with some pre-loaded data sets and similarly many of the Python libraries have data sets build into them. For example scikit learn.
from sklearn import datasets
But where else can you get data sets. There are lots and lots of website available with data sets and the list could be very long. The following is a list of, what I consider, the websites with the best data sets.
Time-series analysis comprises methods for analyzing time series data in order to extract meaningful statistics and other characteristics of the data. In this blog post I’ll introduce what time-series analysis is, the different types of time-series analysis and introduce how you can do this using SQL and PL/SQL in Oracle Database. I’ll have additional blog posts giving more detailed examples of Oracle functions and how they can be used for different time-series data problems.
Time-series forecasting is the use of a model to predict future values based on previously observed/historical values. It is a form of regression analysis with additions to facilitate trends, seasonal effects and various other combinations.
Time-series forecasting is not an exact science but instead consists of a set of statistical tools and techniques that support human judgment and intuition, and only forms part of a solution. It can be used to automate the monitoring and control of data flows and can then indicate certain trends, alerts, rescheduling, etc., as in most business scenarios it is used for predict some future customer demand and/or products or services needs.
Typical application areas of Time-series forecasting include:
- Operations management: forecast of product sales; demand for services
- Marketing: forecast of sales response to advertisement procedures, new promotions etc.
- Finance & Risk management: forecast returns from investments
- Economics: forecast of major economic variables, e.g. GDP, population growth, unemployment rates, inflation; useful for monetary & fiscal policy; budgeting plans & decisions
- Industrial Process Control: forecasts of the quality characteristics of a production process
- Demography: forecast of population; of demographic events (deaths, births, migration); useful for policy planning
When working with time-series data we are looking for a pattern or trend in the data. What we want to achieve is the find a way to model this pattern/trend and to then project this onto our data and into the future. The graphs in the following image illustrate examples of the different kinds of scenarios we want to model.
Most time-series data sets will have one or more of the following components:
- Seasonal: Regularly occurring, systematic variation in a time series according to the time of year.
- Trend: The tendency of a variable to grow over time, either positively or negatively.
- Cycle: Cyclical patterns in a time series which are generally irregular in depth and duration. Such cycles often correspond to periods of economic expansion or contraction. Also know as the business cycle.
- Irregular: The Unexplained variation in a time series.
When approaching time-series problems you will use a combination of visualizations and time-series forecasting methods to examine the data and to build a suitable model. This is where the skills and experience of the data scientist becomes very important.
Oracle provided a algorithm to support time-series analysis in Oracle 18c. This function is called Exponential Smoothing. This algorithm allows for a number of different types of time-series data and patterns, and provides a wide range of statistical measures to support the analysis and predictions, in a similar way to Holt-Winters.
The first parameter for the Exponential Smoothing function is the name of the model to use. Oracle provides a comprehensive list of models and these are listed in the following table.
Check out my other blog posts on performing time-series analysis using the Exponential Smoothing function in Oracle Database. These will give more detailed examples of how the Oracle time-series functions, using the Exponential Smoothing algorithm, can be used for different time-series data problems. I’ll also look at example of the different configurations.
Normalization is the process of scaling continuous values down to a specific range, often between zero and one. Normalization transforms each numerical value by subtracting a number, called the shift, and dividing the result by another number called the scale. The normalization techniques include:
- Min-Max Normalization : There is where the normalization is based on the using the minimum value for the shift and the (maximum-minimum) for the scale.
- Scale Normalization : This is where the normalization is based on zero being used for the shift and the value calculated using max[abs(max), abs(min)] being used for the scale
- Z-Score Normalization : This is where the normalization is based on using the mean value for the shift and the standard deviation for the scale.
When using Automatic Data Processing the normalization functions are used. But sometimes you may want to process the data is a more explicit manner. To do so you can use the various normalization function. To use these there is a three stage process. The first stage involves the creation of a table that will contain the normalization transformation data. The second stage applies the normalization procedures to your data source, defines the normalization required and inserts the required transformation data into the table create during the first stage. The third stage involves the defining of a view that applies the normalization transformations to your data source and displays the output via a database view. The following example illustrates how you can normalize the AGE and YRS_RESIDENCE attributes. The input data source will be the view that was created as the output of the previous transformation (MINING_DATA_V_2). This is passed on the original MINING_DATA_BUILD_V data set. The final output from this transformation step and all the other data transformation steps is MINING_DATA_READY_V.
BEGIN -- Clean-up : Drop the previously created tables BEGIN execute immediate 'drop table TRANSFORM_NORMALIZE'; EXCEPTION WHEN others THEN null; END; -- Stage 1 : Create the table for the transformations -- Perform normalization for: AGE and YRS_RESIDENCE dbms_data_mining_transform.CREATE_NORM_LIN ( norm_table_name => 'MINING_DATA_NORMALIZE'); -- Step 2 : Insert the normalization data into the table dbms_data_mining_transform.INSERT_NORM_LIN_MINMAX ( norm_table_name => 'MINING_DATA_NORMALIZE', data_table_name => 'MINING_DATA_V_2', exclude_list => DBMS_DATA_MINING_TRANSFORM.COLUMN_LIST ( 'affinity_card', 'bookkeeping_application', 'bulk_pack_diskettes', 'cust_id', 'flat_panel_monitor', 'home_theater_package', 'os_doc_set_kanji', 'printer_supplies', 'y_box_games')); -- Stage 3 : Create the view with the transformed data DBMS_DATA_MINING_TRANSFORM.XFORM_NORM_LIN ( norm_table_name => 'MINING_DATA_NORMALIZE', data_table_name => 'MINING_DATA_V_2', xform_view_name => 'MINING_DATA_READY_V'); END; /
The above example performs normalization based on the Minimum-Maximum values of the variables/columns. The other normalization functions are:
|INSERT_NORM_LIN_SCALE||Inserts linear scale normalization definitions in a transformation definition table.|
|INSERT_NORM_LIN_ZSCORE||Inserts linear zscore normalization definitions in a transformation definition table.|
In a previous blog post I introduced HiveMall as a SQL based machine learning language available for Hadoop and integrated with Hive.
If you have your own Hadoop/Big Data environment, I provided the installation instructions for Hivemall, in that blog post
An alternative is to use Docker. There is a HiveMall Docker image available. A little warning before using this image. It isn’t updated with the latest release but seems to get updated twice a year. Although you may not be running the latest version of HiveMall, you will have a working environment that will have almost all the functionality, bar a few minor new features and bug fixes.
To get started, you need to make sure you have Docker running on your machine and you have logged into your account. The docker image is available from Docker Hub. Take note of the version number for the latest version of the docker image. In this example it is 20180924
Open a terminal window and run the following command. This will download and extract all the image files.
docker pull hivemall/latest:20180924
Until everything is completed.
This docker image has HDFS, Yarn and MapReduce installed and running. This will require the exposing of the ports for these services 8088, 50070 and 19888.
To start the HiveMall docker image run
docker run -p 8088:8088 -p 50070:50070 -p 19888:19888 -it hivemall/latest:20180924
Consider creating a shell script for this, to make it easier each time you want to run the image.
Now seed Hive with some data. The typical example uses the IRIS data set. Run the following command to do this. This script downloads the IRIS data set, creates a number directories and then creates an external table, in Hive, to point to the IRIS data set.
cd $HOME && ./bin/prepare_iris.sh
Now open Hive and list the databases.
hive -S hive> show databases; OK default iris Time taken: 0.131 seconds, Fetched: 2 row(s)
Connect to the IRIS database and list the tables within it.
hive> use iris; hive> show tables; iris_raw
Now query the data (150 records)
hive> select * from iris_raw; 1 Iris-setosa [5.1,3.5,1.4,0.2] 2 Iris-setosa [4.9,3.0,1.4,0.2] 3 Iris-setosa [4.7,3.2,1.3,0.2] 4 Iris-setosa [4.6,3.1,1.5,0.2] 5 Iris-setosa [5.0,3.6,1.4,0.2] 6 Iris-setosa [5.4,3.9,1.7,0.4] 7 Iris-setosa [4.6,3.4,1.4,0.3] 8 Iris-setosa [5.0,3.4,1.5,0.2] 9 Iris-setosa [4.4,2.9,1.4,0.2] 10 Iris-setosa [4.9,3.1,1.5,0.1] 11 Iris-setosa [5.4,3.7,1.5,0.2] 12 Iris-setosa [4.8,3.4,1.6,0.2] 13 Iris-setosa [4.8,3.0,1.4,0.1 ...
Find the min and max values for each feature.
hive> select > min(features), max(features), > min(features), max(features), > min(features), max(features), > min(features), max(features) > from > iris_raw; 4.3 7.9 2.0 4.4 1.0 6.9 0.1 2.5
You are now up and running with HiveMall on Docker.
It is widely recognised that SQL is one of the core languages that every data scientist needs to know. Not just know but know really well. If you are going to be working with data (big or small) you are going to use SQL to access the data. You may use some other tools and languages as part of your data science role, but for processing data SQL is king.
During the era of big data and hadoop it was all about moving the code to where the data was located. Over time we have seem a number of different languages and approaches being put forward to allow us to process the data in these big environments. One of the most common one is Spark. As with all languages there can be a large learning curve, and as newer languages become popular, the need to change and learn new languages is becoming a lot more frequent.
We have seen many of the main stream database vendors including machine learning in their databases, thereby allowing users to use machine learning using SQL. In the big data world there has been many attempts to do this, to building some SQL interfaces for machine learning in a big data environment.
One such (newer) SQL machine learning engine is called HiveMall. This will allow anyone with a basic level knowledge of SQL to quickly learn machine learning. Apache Hivemall is built to be a scalable machine learning library that runs on Apache Hive, Apache Spark, and Apache Pig.
Hivemall is currently at incubator stage under Apache and version 0.6 was released in December 2018.
I’ve a number of big data/hadoop environments in my home lab and build on a couple of cloud vendors (Oracle and AWS). I’ve completed the installation of Hivemall easily on my Oracle BigDataLite VM and my own custom build Hadoop environment on Oracle cloud. A few simple commands you will have Hivemall up and running. Initially installed for just Hive and then updated to use Spark.
Hivemall expands the analytical functions available in Hive, as well as providing data preparation and the typical range of machine learning functions that are necessary for 97+% of all machine learning use cases.
Download the hivemall-core-xxx-with-dependencies.jar file
# Setup Your Environment $HOME/.hiverc add jar /home/myui/tmp/hivemall-core-xxx-with-dependencies.jar; source /home/myui/tmp/define-all.hive;
This automatically loads all Hivemall functions every time you start a Hive session
# Create a directory in HDFS for the JAR hadoop fs -mkdir -p /apps/hivemall hdfs dfs -chmod -R 777 /apps/hivemall cp hivemall-core-0.4.2-rc.2-with-dependencies.jar hivemall-with-dependencies.jar hdfs dfs -put hivemall-with-dependencies.jar /apps/hivemall/ hdfs dfs -put hivemall-with-dependencies.jar /apps/hive/warehouse
You might want to create a new DB in Hive for your Hivemall work.
CREATE DATABASE IF NOT EXISTS hivemall; USE hivemall;
Then list all the Hivemall functions
show functions "hivemall.*"; +-----------------------------------------+--+ | tab_name | +-----------------------------------------+--+ | hivemall.add_bias | | hivemall.add_feature_index | | hivemall.amplify | | hivemall.angular_distance | | hivemall.angular_similarity | ...
Hivemall for ML using SQL is now up and running. Next step is to do try out the various analytical and ML functions.
Ethics is one of those topics that everyone has a slightly different definition or view of what it means. The Oxford English dictionary defines ethics as, ‘Moral principles that govern a person’s behaviour or the conducting of an activity‘.
As you can imagine this topic can be difficult to discuss and has many, many different aspects.
In the era of AI, Machine Learning, Data Science, etc the topic of Ethics is finally becoming an important topic. Again there are many perspective on this. I’m not going to get into these in this blog post, because if I did I could end up writing a PhD dissertation on it.
But if you do work in the area of AI, Machine Learning, Data Science, etc you do need to think about the ethical aspects of what you do. For most people, you will be working on topics where ethics doesn’t really apply. For example, examining log data, looking for trends, etc
But when you start working of projects examining individuals and their behaviours then you do need to examine the ethical aspects of such work. Everyday we experience adverts, web sites, marketing, etc that has used AI, Machine Learning and Data Science to delivery certain product offerings to us.
Just because we can do something, doesn’t mean we should do it.
One particular area that I will not work on is Location Based Advertising. Imagine walking down a typical high street with lots and lots of retail stores. Your phone vibrates and on the screen there is a message. The message is a special offer or promotion for one of the shops a short distance ahead of you. You are being analysed. Your previous buying patterns and behaviours are being analysed, Your location and direction of travel is being analysed. Some one, or many AI applications are watching you. This is not anything new and there are lots of examples of this from around the world.
But what if this kind of Location Based Advertising was taken to another level. What if the shops had cameras that monitored the people walking up and down the street. What if those cameras were analysing you, analysing what clothes you are wearing, analysing the brands you are wearing, analysing what accessories you have, analysing your body language, etc. They are trying to analyse if you are the kind of person they want to sell to. They then have staff who will come up to you, as you are walking down the street, and will have customised personalised special offers on products in their store, just for you.
See the segment between 2:00 and 4:00 in this video. This gives you an idea of what is possible.
Are you Ok with this?
As an AI, Machine Learning, Data Science professional, are you Ok with this?
The technology exists to make this kind of Location Based Marketing possible. This will be an increasing ethical consideration over the coming years for those who work in the area of AI, Machine Learning, Data Science, etc
Just because we can, doesn’t mean we should!
We keep hearing from people about all the computing resources needed for machine learning. Sometimes it can put people off from trying it as they will think I don’t have those kind of resources.
This is another blog post in my series on ‘How long does it take to create a machine learning model?‘
Check out my previous blog post that used data sets containing 72K, 210K, 660K, 2M and 10M records.
- Creating Machine Learning Models in Oracle Cloud Database service
- Creating Machine Learning Models using Oracle Autonomous Data Warehouse (ADW)
There was some surprising results in those these.
In this test, I’ll be using Python and SciKitLearn package to create models using the same algorithms. There are a few things to keep in mind. Firstly, although they maybe based on the same algorithms, the actual implementation of them will be different in each environment (SQL vs Python).
With using Python for machine learning, one of the challenges we have is getting access to the data. Assuming the data lives in a Database then time is needed to extract that data to the local Python environment. Secondly, when using Python you will be using a computer with significantly less computing resources than a Database server. In this test I used my laptop (MacBook Pro). Thirdly, when extracting the data from the database, what method should be used.
I’ve addressed these below and the Oracle Database I used was the DBaaS I used in my first experiment. This is a Database hosted on Oracle Cloud.
Extracting Data to CSV File
This kind of depends on how you do this. There are hundreds of possibilities available to you, but if you are working with an Oracle Database you will probably be using SQL Developer. I used the ‘export’ option to create a CSV file for each of the data sets. The following table shows how long it took for each data set.
As you can see this is an incredibly slow way of exporting this data. Like I said, there are quicker ways of doing this.
After downloading the data sets, the next step is to see how load it takes to load these CSV files into a pandas data frame in Python. The following table show the timings in seconds.
Extracting Data using cx_Oracle Python package
As I’ll be using Python to create the models and the data exists in an Oracle Database (on Oracle Cloud), I can use the cx_Oracle package to download the data sets into my Python environment. After using the cx_Oracle package to download the data I then converted it into a pandas data frame.
I had the array fetch size set to 10,000. I also experimented with smaller and larger numbers for the array fetch size, but 10,000 seemed to give a quickest results.
How long to create Machine Learning Models in Python
Now we get onto checking out the timings of how long it takes to create a number of machine learning models using different algorithms and using the default settings. The algorithms include Naive Bayes, Decision Tree, GLM, SVM and Neural Networks.
I had to stop including SVM in the tests as it was taking way too long to run. For example I killed the SVM model build on the 210K data set after it was running for 5 hours.
The Neural Network models created had 3 hidden layers.
In addition to creating the models, there was some minor data preparation steps performed including factorizing, normalization and one-hot-coding. This data preparation would be comparable to the automatic data preparation steps performed by Oracle, although Oracle Automatic Data Preparation does a bit of extra work.
At the point I would encourage you to look back at my previous blog posts on timings using Oracle DBaaS and ADW. You will see that Python, in these test cases, was quicker at creating the machine learning models. But with Python the data needed to be extracted from the database and that can take time!
A separate consideration is being able to deploy the models. The time it takes to build models is perhaps not the main consideration. You need to consider ease of deployment and use of the models.
Everyday someone talks about the the processing power needed for Machine Learning, and the vast computing needed for these tasks. It has become evident that most of these people have never created a machine learning model. Never. But like to make up stuff and try to make themselves look like an expert, or as I and others like to call them a “fake expert”.
When you question these “fake experts” about this topic, they huff and puff about lots of things and never answer the question or try to claim it is so difficult, you simply don’t understand.
Having worked in the area of machine learning for a very very long time, I’ve never really had performance issues with creating models. Yes most of the time I’ve been able to use my laptop. Yes my laptop to build models large models. In a couple of these my laptop couldn’t cope and I moved onto a server.
But over the past few years we keep hearing about using cloud services for machine learning. If you are doing machine learning you need to computing capabilities that are available with cloud services.
So, the results below show the results of building machine learning models, using different algorithms, with different sizes of data sets.
For this test, I used a basic cloud service. Well maybe it isn’t basic, but for others they will consider it very basic with very little compute involved.
I used an Oracle Cloud DBaaS for this experiment. I selected an Oracle 18c Extreme edition cloud service. This comes with the in-database machine learning option. This comes with 1 OCPUs, 7.5G Memory and 170GB storage. This is the basic configuration.
Next I created data sets with different sizes. These were based on one particular data set, as this ensures that as the data set size increases, the same kind of data and processing required remained consistent, instead of using completely different data sets.
The data set consisted of the following number of records, 72K, 660K, 210K, 2M, 10M and 50M.
I then created machine learning models using Decisions Tree, Naive Bayes, Support Vector Machine, Generaliszd Linear Models (GLM) and Neural Networks. Yes it was a typical classification problem.
The following table below shows the length of time in seconds to build the models. All data preparations etc was done prior to this.
Note: It should be noted that Automatic Data Preparation was turned on for these algorithms. This performed additional algorithm specific data preparation for each model. That means the times given in the following tables is for some data preparation time and for building the models.
Converting the above table into minutes.
Machine learning is a very popular topic in recent times, and we keep hearing about languages such as R, Python and Spark. In addition to these we have commercially available machine learning languages and tools from SAS, IBM, Microsoft, Oracle, Google, Amazon, etc., etc. Everyone want a slice of the machine learning market!
The Apache Foundation supports the development of new open source projects in a number of areas. One such area is machine learning. If you have read anything about machine learning you will have come across Spark, and maybe you might believe that everyone is using it. Sadly this isn’t true for lots of reasons, but it is very popular. Spark is one of the project support by the Apache Foundation.
But are there any other machine learning projects being supported under the Apache Foundation that are an alternative to Spark? The follow lists the alternatives and lessor know projects: (most of these are incubator/retired/graduated Apache projects)
|Flink||Flink is an open source system for expressive, declarative, fast, and efficient data analysis. Stratosphere combines the scalability and programming flexibility of distributed MapReduce-like platforms with the efficiency, out-of-core execution, and query optimization capabilities found in parallel databases. Flink was originally known as Stratosphere when it entered the Incubator.
|HORN||HORN is a neuron-centric programming APIs and execution framework for large-scale deep learning, built on top of Apache Hama.
|HiveMail||Hivemall is a library for machine learning implemented as Hive UDFs/UDAFs/UDTFs
Apache Hivemall offers a variety of functionalities: regression, classification, recommendation, anomaly detection, k-nearest neighbor, and feature engineering. It also supports state-of-the-art machine learning algorithms such as Soft Confidence Weighted, Adaptive Regularization of Weight Vectors, Factorization Machines, and AdaDelta. Apache Hivemall offers a variety of functionalities: regression, classification, recommendation, anomaly detection, k-nearest neighbor, and feature engineering. It also supports state-of-the-art machine learning algorithms such as Soft Confidence Weighted, Adaptive Regularization of Weight Vectors, Factorization Machines, and AdaDelta.
|MADlib||Apache MADlib is an open-source library for scalable in-database analytics. It provides data-parallel implementations of mathematical, statistical and machine learning methods for structured and unstructured data. Key features include: Operate on the data locally in-database. Do not move data between multiple runtime environments unnecessarily; Utilize best of breed database engines, but separate the machine learning logic from database specific implementation details; Leverage MPP shared nothing technology, such as the Greenplum Database and Apache HAWQ (incubating), to provide parallelism and scalability.
|MXNet||A Flexible and Efficient Library for Deep Learning . MXNet provides optimized numerical computation for GPUs and distributed ecosystems, from the comfort of high-level environments like Python and R MXNet automates common workflows, so standard neural networks can be expressed concisely in just a few lines of code.
|OpenNLP||OpenNLP is a machine learning based toolkit for the processing of natural language text. OpenNLP supports the most common NLP tasks, such as tokenization, sentence segmentation, part-of-speech tagging, named entity extraction, chunking, parsing, language detection and coreference resolution.
|PredictionIO||PredictionIO is an open source Machine Learning Server built on top of state-of-the-art open source stack, that enables developers to manage and deploy production-ready predictive services for various kinds of machine learning tasks.
|SAMOA||SAMOA provides a collection of distributed streaming algorithms for the most common data mining and machine learning tasks such as classification, clustering, and regression, as well as programming abstractions to develop new algorithms that run on top of distributed stream processing engines (DSPEs). It features a pluggable architecture that allows it to run on several DSPEs such as Apache Storm, Apache S4, and Apache Samza.
|SINGA||SINGA is a distributed deep learning platform. An intuitive programming model based on the layer abstraction is provided, which supports a variety of popular deep learning models. SINGA architecture supports both synchronous and asynchronous training frameworks. Hybrid training frameworks can also be customized to achieve good scalability. SINGA provides different neural net partitioning schemes for training large models.
|Storm||Storm is a distributed, fault-tolerant, and high-performance realtime computation system that provides strong guarantees on the processing of data. Storm makes it easy to reliably process unbounded streams of data, doing for realtime processing what Hadoop did for batch processing. Storm is simple, can be used with any programming language.
|SystemML||SystemML provides declarative large-scale machine learning (ML) that aims at flexible specification of ML algorithms and automatic generation of hybrid runtime plans ranging from single node, in-memory computations, to distributed computations such as Apache Hadoop MapReduce and Apache Spark.
I will have a closer look that the following SQL based machine learning languages in a lager blog post: