Though, I have narrated only 20 best machine learning platform and tools through my article. So, let's get started. Google Cloud ML Engine. Google ml engine. Machine learning can be applied to different types of problems, but Machine Learning Platforms focus on helping businesses predict future outcomes such as a customer’s propensity to buy when presented with a given offer, or the likelihood that a given transaction will be rejected as fraudulent.
-->Machine Learning Services is a feature in SQL Server that gives the ability to run Python and R scripts with relational data. You can use open-source packages and frameworks, and the Microsoft Python and R packages for predictive analytics and machine learning. The scripts are executed in-database without moving data outside SQL Server or over the network. This article explains the basics of SQL Server Machine Learning Services.
Note
For executing Java in SQL Server, see the Language Extensions documentation.
What is Machine Learning Services?
SQL Server Machine Learning Services lets you execute Python and R scripts in-database. You can use it to prepare and clean data, do feature engineering, and train, evaluate, and deploy machine learning models within a database. The feature runs your scripts where the data resides and eliminates transfer of the data across the network to another server.
Base distributions of Python and R are included in Machine Learning Services. You can install and use open-source packages and frameworks, such as PyTorch, TensorFlow, and scikit-learn, in addition to the Microsoft packages revoscalepy and microsoftml for Python, and RevoScaleR, MicrosoftML, olapR, and sqlrutils for R.
Machine Learning Services uses an extensibility framework to run Python and R scripts in SQL Server. Learn more about how this works:
What can I do with Machine Learning Services?
You can use Machine Learning Services to build and train machine learning and deep learning models within SQL Server. You can also deploy existing models to Machine Learning Services and use relational data for predictions.
Examples of the type of predictions that you can use SQL Server Machine Learning Services for include:
| Classification/Categorization | Automatically divide customer feedback into positive and negative categories |
| Regression/Predict continuous values | Predict the price of houses based on size and location |
| Anomaly Detection | Detect fraudulent banking transactions |
| Recommendations | Suggest products that online shoppers may want to buy, based on their previous purchases |
How to execute Python and R scripts
There are two ways to execute Python and R scripts in Machine Learning Services:
The most common way is to use the T-SQL stored procedure sp_execute_external_script.
You can also use your preferred Python or R client and write scripts that push the execution (referred to as a remote compute context) to a remote SQL Server. See how to set up a data science client for Python development and R development for more information.
Python and R versions
The following lists the versions of Python and R included in Machine Learning Services with each version of SQL Server.
| SQL Server version | Python version | R version |
|---|---|---|
| SQL Server 2017 | 3.5.2 | 3.3.3 |
| SQL Server 2019 | 3.7.3 | 3.5.2 |
For the R version in SQL Server 2016, see the R version section in What is R Services?
Python and R packages
You can use open-source packages and frameworks, in addition to Microsoft's enterprise packages. Most common open-source Python and R packages are pre-installed in Machine Learning Services. The following Python and R packages from Microsoft are also included:
| Language | Package | Description |
|---|---|---|
| Python | revoscalepy | The primary package for scalable Python. Data transformations and manipulation, statistical summarization, visualization, and many forms of modeling. Additionally, functions in this package automatically distribute workloads across available cores for parallel processing. |
| Python | microsoftml | Adds machine learning algorithms to create custom models for text analysis, image analysis, and sentiment analysis. |
| R | RevoScaleR | The primary package for scalable R. Data transformations and manipulation, statistical summarization, visualization, and many forms of modeling. Additionally, functions in this package automatically distribute workloads across available cores for parallel processing. |
| R | MicrosoftML (R) | Adds machine learning algorithms to create custom models for text analysis, image analysis, and sentiment analysis. |
| R | olapR | R functions used for MDX queries against a SQL Server Analysis Services OLAP cube. |
| R | sqlrutils | A mechanism to use R scripts in a T-SQL stored procedure, register that stored procedure with a database, and run the stored procedure from an R development environment. |
| R | Microsoft R Open | Microsoft R Open (MRO) is the enhanced distribution of R from Microsoft. It is a complete open-source platform for statistical analysis and data science. It is based on and 100% compatible with R, and includes additional capabilities for improved performance and reproducibility. |
For more information on which packages are installed with Machine Learning Services and how to install other packages, see:
- Install new R packages with sqlmlutils.
How do I get started with Machine Learning Services?
Configure your development tools. You can use:
- Azure Data Studio or SQL Server Management Studio (SSMS) to use T-SQL and the stored procedure sp_execute_external_script to execute your Python or R script.
- Python or R on your own development laptop or workstation to execute scripts. You can either pull data down locally or push the execution remotely to SQL Server with revoscalepy and RevoScaleR. See how to set up a data science client for Python development and R development for more information.
Write your first Python or R script
- Quickstart: Run simple Python scripts
- Quickstart: Run simple R scripts
- Tutorial: Use Python in T-SQL: Explore data, perform feature engineering, train and deploy models, and make predictions (five-part series)
- Tutorial: Use R in T-SQL: Explore data, perform feature engineering, train and deploy models, and make predictions (five-part series)
Next steps
- Set up a data science client for Python development and R development
Machine-learning techniques are required to improve the accuracy of predictive models. Depending on the nature of the business problem being addressed, there are different approaches based on the type and volume of the data. In this section, we discuss the categories of machine learning.
Supervised learning
Supervised learning typically begins with an established set of data and a certain understanding of how that data is classified. Supervised learning is intended to find patterns in data that can be applied to an analytics process. This data has labeled features that define the meaning of data. For example, you can create a machine-learning application that distinguishes between millions of animals, based onimages and written descriptions.
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Unsupervised learning
Unsupervised learning is used when the problem requires a massive amount of unlabeled data. Knights and merchants mac torrent. For example, social media applications, such as Twitter, Instagram and Snapchat, all have large amounts of unlabeled data. Understanding the meaning behind this data requires algorithms that classify the data based on the patterns or clusters it finds. Unsupervised learning conducts an iterative process, analyzing data without human intervention. It is used with email spam-detecting technology. There are far too many variables in legitimate and spam emails for an analyst to tag unsolicited bulk email. Instead, machine-learning classifiers, based on clustering and association, are applied to identify unwanted email.
Reinforcement learning
Reinforcement learning is a behavioral learning model. The algorithm receives feedback from the data analysis, guiding the user to the best outcome. Reinforcement learning differs from other types of supervised learning, because the system isn’t trained with the sample data set. Rather, the system learns through trial and error. Therefore, a sequence of successful decisions will result in the process being reinforced, because it best solves the problem at hand.
Deep learning
Deep learning is a specific method of machine learning that incorporates neural networks in successive layers to learn from data in an iterative manner. Deep learning is especially useful when you’re trying to learn patterns from unstructured data. Deep learning complex neural networks are designed to emulate how the human brain works, so computers can be trained to deal with poorly defined abstractions and problems. The average five-year-old child can easily recognize the difference between his teacher’s face and the face of the crossing guard. In contrast, the computer must do a lot of work to figure out who is who. Neural networks and deep learning are often used in image recognition, speech, and computer vision applications.