6 tools to make machine learning easier
The term ‘machine learning’ (ML) is full of a magical aura. Teaching machines to learn is not yet in the realm of ordinary people. Today, the term is the domain of very professional alchemists, such as data scientists, turning data into gold.
But now, machine learning tools are progressing to the point where anyone with a little courage and motivation can push a button and let machines learn what’s important. Gathering data and turning it into actionable insights, if not all at once, is becoming automated enough, and progress has been made to a challenge for smart, motivated people.
This slow renaissance comes as many people in the business world are already quite proficient with data. Spreadsheets full of numbers are the language of decision-makers in any business. The many new tools that let you tackle machine learning are essentially a combination of strategies and options that turn table data into useful answers.
The strength of this tool is its ability to handle the cumbersome tasks of collecting data, adding structure and consistency where possible, and starting calculations. It simplifies the data collection process and the monotonous task of keeping information in rows and columns.
The tool is not yet smart enough to do all this learning on its own. Ask the right questions and look to the right places. But because this tool can help you get answers faster, you can cover a larger area and investigate more places.
AutoML: Democratizing Machine Learning
Recently, in the field of machine learning algorithms, a new buzzword ‘AutoML’ has emerged, meaning that an additional automation meta-layer is involved. Traditional algorithms have many options and parameters. Data scientists often spend 80-99% of their time tweaking these things until they find the most predictable rule.
AutoML automates this step by trying and testing different options and then doing some extra work. Instead of running the machine learning algorithm once, run it N times, tune it, and run it again N times. As long as your budget, money, or patience allow it.
In the cloud, AutoML tools can create enough machines to run in parallel and then send them back to the Pool when done. Therefore, this tool has characteristics suitable for cloud computing. This is because you only pay for peak computation time.
In general, AutoML algorithms are a good option for those starting to study machine learning on their own. Automation simplifies tasks by handling some of the basic parameter setting and option selection tasks prior to testing results. As users progress further and begin to understand the consequences, they can handle one of these tasks and set the values themselves.
Modern systems make it easier to understand how machines can learn. Where traditional programming turns rules and data into answers, machine learning algorithms can do the reverse, turning answers and data into rules. These rules can also teach you what’s going on deep inside your business. The developers of these streamlined tools are also developing interfaces that describe how to reproduce the rules discovered by the algorithm and more importantly the results.
6 Tools To Make Machine Learning Easier
All these features allow people who work with numbers, spreadsheets and data to enter the world of machine learning without being fluent in programming and data science. The six options below are those that simplify the process of finding answers using machine learning algorithms in a sea of numbers.
The original version of Splunk began as a tool to examine (or ‘explore’) the large number of log files generated by modern web applications. Since then, it has progressed to the level of analyzing all forms of time series and other sequentially generated data. The tool presents results as dashboards through complex visualization routines.
The latest version includes an app that integrates data sources with machine learning tools like TensorFlow and the best Python open source tools. They provide a simple solution for detecting outliers, flagging anomalies, and predicting future values. It specializes in finding clues in very large datasets.
‘s stack contains the best open-source machine libraries written in R, Python, and many other platforms. You only need to deal with a web interface that displays a flowchart-style tool for setting up a pipeline.
DataRobot connects to all major data sources: local databases, cloud datastores, downloaded files or spreadsheets. The pipeline you build can create a model that erases data, fills in missing values, flags outliers, and predicts future values.
Data robots can also attempt to provide ‘human-friendly explanations’ about the rationale behind certain predictions, a useful feature for understanding how AI works.
A combination of cloud and on-premises solutions can be deployed. Running in the cloud can provide maximized parallelism and throughput through shared resources, while local installations provide greater privacy and control.
H2O often uses the term ‘driverless AI’ to describe an automated stack that explores multiple machine learning solutions. It links multiple data sources (databases, Hadoop, Spark, etc.) and feeds them to different algorithms through a wide range of parameters. The tool allows the user to manage the amount of time, calculate the resources allocated to the problem, and test various combinations of parameters within the budget. The derived results can be explored and audited through the dashboard or Jupyter notes.
H2O’s core machine learning algorithms and integrations with tools like Spark are open source, but the so-called “driverless” option is one of the proprietary features sold with support to enterprise customers.
RapidMiner The heart of the
RapidMiner ecosystem is a studio that creates data analytics from visual icons. Dragging and dropping creates a pipeline that runs a series of statistical algorithms after cleaning the data. If you want to use machine learning instead of some more traditional data science, Otto Models chooses from several classification algorithms and examines various parameters to find the best fit. The purpose of this tool is to identify the best after generating hundreds of models.
After the model is created, the tool can deploy it, test its success rate, and explain how the model makes decisions. You can test the sensitivity to various data fields and adjust them with the visual workflow editor.
Recent improvements include better text analytics, more charts for building visual dashboards, and more complex algorithms for analyzing time series data.
BigMLThe dashboard provides all the basic tools for data science to identify correlations that can form the basis for more complex tasks with machine learning. For example, Deepnets provides a complex mechanism for testing and optimizing more sophisticated neural networks. The quality of a model can be compared to other algorithms through a standardized comparison framework that helps to choose between traditional data science and more complex machine learning.
BigML’s dashboard runs in the browser, and analytics run on the BigML cloud or on facilities in the server room. The cloud version is priced low to encourage early experimentation, and there is also a free tier.
The cost is mostly determined by the limitations on the size of the data set and the amount of computational resources that can be applied. The free tier uses no more than two processes working in parallel to analyze up to 16MB of data. A cheap paid account has a reasonable monthly fee of $30, but the cost rises as the resources required increase.
R is not an easy-to-use language for non-programmers, but it is very popular with serious data scientists, so it is one of the essential tools for precise statistical analysis. R Studio provides a set of menus and mouse-click options, making it easier for users to interact with R working deep inside.