BigML

Author: m | 2025-04-24

BigML Documentation. Welcome to BigML! Get familiarized with the BigML platform by reading our detailed documentation and learn how to use the BigML Dashboard, the BigML API, and BigML Documentation. Welcome to BigML! Get familiarized with the BigML platform by reading our detailed documentation and learn how to use the BigML Dashboard, the BigML API, and other developer tools we offer. Additionally, if

BigML Python Bindings BigML 9.8.1 documentation

The conventional.Real-world ML solutionsFor all the reasons described above, many customers trust BigML and choose our platform to develop their real-world ML solutions and continue improving. We are honored to say that from small startups to global enterprises, BigML’s platform has been deployed across industries to tackle diverse challenges:Retail: BigML helps retailers optimize inventory management and forecast demand.Finance: Banks use BigML for fraud detection and credit scoring.Manufacturing: Companies rely on BigML for predictive maintenance and supply chain optimization.Healthcare: BigML supports predictive diagnostics and patient segmentation. On this topic, stay tuned for future announcements on BigML for Healthcare to learn about several ML solutions we have developed to enhance people’s quality of life. It’s coming soon!The Future of Machine Learning with BigMLWith an intuitive and robust platform, powerful tools, and a track record of innovation, BigML stands out in the crowded landscape of ML tools and we plan to continue this journey to help organizations around the world become “AI-first” enterprises. By staying committed to accessibility, transparency, interpretability, traceability, and scalability, we aim to empower even more businesses to harness the power of machine learning knowing full well it’s a journey of evolution. As always, feel free to reach out to us at [email protected] to start the conversation.From all of us at BigML, we wish you success and growth in 2025 and beyond!

Datasets with the BigML Dashboard

Dec2021 BigML introduces Convolutional Neural Network (CNN), the most popular machine learning technique for image classification. When a dataset contains images and it is used to train a Deepnet, BigML’s resource for deep neural networks, the Deepnet will be a CNN. The BigML platform abstracts away hardware complexity so users do not need to worry about infrastructure setup such as GPU installation. Moreover, just like any other model, users can employ 1-click CNN and automatic parameter optimization to accommodate their use cases.Please visit the Image Processing page to learn more. Dec2021 The inclusion of configurable image features makes machine learning with images beautifully simple for everyone. Users can use image features to train all sorts of models, both supervised and unsupervised, greatly expanding their business scopes and enhancing their Machine Learning workflows.Image features are sets of numeric fields extracted from images. They can capture parts or patterns of images, such as edges, colors, and textures. The image features extracted by pre-trained CNNs capture more complex patterns. Now, BigML allows users to configure different image features at the source level.Please visit the Image Processing release page to learn more. Dec2021 BigML introduces composite sources, a new type of source that augments its Machine Learning capabilities. Image composite sources are collections of images, and play a central role in BigML Image Processing. As such, users can preview, add or remove images in composite sources, as well as extract image features and add labels.A composite source is a collection of other sources, called components. The power and flexibility of a composite source lie in its ability to allow many types of component sources, including other composite sources. Furthermore, users can manipulate composite sources by performing operations on their components, such as addition and exclusion.You can find more details about composite sources here. Apr2020 BigML now supports creating sources directly from your databases. You can also create sources from your Elasticsearch engine. Both the BigML Dashboard and the API allow you to establish a connector to your databases or Elasticsearch by providing connection parameters and authentication information. BigML can then connect to your database or engine and create sources in BigML's server. You have the options to import data from individual tables or to do it selectively by specifying the data with your own queries.Please visit the release page to learn more. Apr2020 Anything you create on BigML Dashboard, you can do the same with BigML API. Now BigML adds the feature of previewing an API request alongside the configuration of a resource creation on the Dashboard.This essentially shows the users how to create the resource programmatically. It includes the endpoint of the REST API call and the JSON file that specifies the arguments that had been configured.Find more information in the BigML Dashboard documentation. Apr2020 When you use WhizzML scripts, some inputs may be mandatory, some optional. You may also provide default values to inputs. You can specify them in the corresponding JSON metadata files. Now you can do this on the BigML

Getting Started with BigML - Pluralsight

Dashboard: when inputs are resources, such as sources, datasets and models, BigML provides checkboxes for users to toggle between these inputs being mandatory or optional. Users also have the option to provide default values for those inputs or leave them empty.Please visit the release page to learn more about this feature. May2019 With a single click, produce a human readable report describing the exact steps followed to execute your workflow. You can share the output with colleagues to bring them up to speed or as a personal record of the process you followed.You can produce a Workflow Report easily for any resource. Just navigate to the Workflow Report option found under the scripts menu. BigML instantaneously runs a Scriptfy job and creates your report. Mar2019 The ultimate goal of creating a linear regression is to make predictions with it. Linear regression is a regression model that predicts numeric values.You can perform single predictions with linear regression, if you want to predict just one instance. This is easily achieved by using BigML prediction form —just input the values for the fields used by the linear regression. You can also ask for the prediction explanation, i.e., the per-field importance in the prediction.BigML batch predictions allow you to predict multiple instances with one-click. Just select the linear regression and the dataset containing the data you want to predict, and BigML will automatically generate an output CSV file with a prediction for each of your instances. A wide range of the output file settings can be configured. Learn more about linear regressions here. Mar2019 Evaluate the performance of your Linear Regressions to get an estimate of how good your model is at making predictions for new data. As with other supervised regression models, the resulting performance metrics of linear regression evaluations includes three measures: Mean Absolute Error, Mean Squared Error and R Squared. BigML also provides the measures of two other types of models to compare against your model performance. You can find explanations for these measures in the documentation. Mar2019 BigML adds Linear Regression to our collection of supervised learning methods. Linear Regression is a well known algorithm that discovers relationships between input fields and the objective field. BigML’s implementation can support any type of field, including numerical, categorical, text, items fields, and can even handle missing values. BigML Visualization for linear regression includes three views: a 1D chart, a Partial Dependence Plot (PDP) and a coefficient table. The 1D chart and PDP provide visual ways to analyze the impact of your input fields on predictions. The table shows all the coefficients learned for each of the variables of the linear function, which is useful for inspecting model results. Learn more about Linear Regression on the release page. Jan2019 When you create a topic model in BigML, your topics get default names. Until now the names were set as "Topic 00", "Topic 01", and so on. Now BigML takes the top term per topic as the default name, aiming to provide more descriptive. BigML Documentation. Welcome to BigML! Get familiarized with the BigML platform by reading our detailed documentation and learn how to use the BigML Dashboard, the BigML API, and BigML Documentation. Welcome to BigML! Get familiarized with the BigML platform by reading our detailed documentation and learn how to use the BigML Dashboard, the BigML API, and other developer tools we offer. Additionally, if

Are there any BigML tutorials? - Support

As 2024 draws to a close, it’s time to reflect on how the BigML team has been working to enhance our platform, solidifying its place as a leader in the machine learning space. In a world where artificial intelligence is reshaping industries, BigML remains a game-changer with its no nonsense approach to bringing more self-directed users on board the ML/AI boat.Since our inception in 2011, our mission has been clear: to democratize machine learning by making it easy, accessible, transparent, traceable, interpretable, scalable, and user-friendly for everyone, regardless of technical expertise. What truly sets BigML apart is its ability to empower professionals across all fields to create and use their own ML solutions by teaching them how to fish for themselves. As we celebrate our achievements and innovations, we would like to take this opportunity to review the main highlights and milestones that have shaped our journey since its inception.A Journey of Innovation: BigML’s Major MilestonesBigML’s evolution is marked by consistent innovation and strategic growth. Here are some of the defining milestones in its journey:2011: Laying the foundation. BigML was established in Corvallis, Oregon, USA, with a clear mission: to simplify machine learning and bring its transformative potential to non-technical users and businesses worldwide.2012: Public launch of Machine Learning as a Service (MLaaS). BigML pioneered MLaaS with the launch of our platform, allowing users to create predictive models through an intuitive dashboard, with no coding required!2013: Building a robust ML platform. BigML maked a large leap in functionality. In 2013, BigML added inline sources and interactive filters, the Sunburst Visualization, and brought 7 new features including Text Analysis, Microsoft Excel Export, Multi-label Classification, the BigML PredictServer, and more.2014: Introduction to Anomaly Detection. Expanding its portfolio of capabilities, BigML introduces a new tool to detect outliers, which has been one of the most used tools to solve real-world use cases.2015: BigML opened the European headquarters in Valencia, Spain, to bring talent to the company and set Valencia on the map to run several ML events that will attract innovation and prosperity, such as the first Machine Learning School, held in Valencia in September of 2015. This very same month BigML launched Association Discovery on the cloud, becoming the first machine learning service offering a tool on the cloud to pinpoint hidden relations between values of your variables in high-dimensional datasets with just one click.2016: Innovation and quality training. In January we

Cluster Analysis with the BigML Dashboard

Is very useful for time series, when you have a dataset containing a date field and you need to sort your instances chronologically. Oct2018 The BigML Flatline Editor has been upgraded to easily help you create new fields and validate existing Flatline expressions in your Dashboard. Flatline is BigML's domain-specific language for data generation and filtering, which helps you to perform an infinite number of calculations on top of your dataset fields. BigML included a table-like dataset preview where you can easily see a sample of your instances. When you write a formula and you want to view its result, the preview only shows the fields involved in the formula. That way you can quickly check if your formula is being calculated correctly. Moreover, BigML also included a formula autocompletion so it's convenient to see which operators and dataset fields you can use while writing in the editor.Find more information in the Datasets with the BigML Dashboard document. Oct2018 Creating new features using sliding windows is one of the most common feature engineering techniques in Machine Learning. It is usually applied to frame time series data using previous data points as new input fields to predict the next time data points. For example, imagine we have one year of sales data to predict sales, we can use our sales field to create an infinite number of fields containing past data: last day sales, the average of last week sales, the difference between last month and this month sales, etc. To set up an sliding window in BigML you just need to choose the operation you want to apply to the instances in the window and define a window start and end.Find more information in the Datasets with the BigML Dashboard document. Oct2018 The merging datasets option in BigML allows you to include the instances of several datasets in one dataset. This functionality can be very useful when you use multiple sources of data. For example, imagine that you collect data on an hourly basis and want to create a dataset aggregating data collected over the whole day. You only need to send the new data generated each hour to BigML, create a source and a dataset for each one, and then merge all the individual datasets into one at the end of the day.Find more information in the Datasets with the BigML Dashboard document. Oct2018 BigML allows you to join several datasets to combine their fields and instances based on one or more related fields between them. This is very useful when your data is scattered in two or more datasets. For example, imagine you have employee data in one dataset and department data in another dataset. You can add the department information per employee if you have a common field to join them such as department_id.Find more information in the Datasets with the BigML Dashboard document. Oct2018 Duplicated instances in a dataset can be problematic for training Machine Learning models. For example, if you make a random split of

Topic Modeling with the BigML Dashboard

Once the execution finishes, while you concentrate on other tasks. Feb2017 Furthering our obsession to speed up your Machine Learning processes, we have incorporated Scriptify into your 1-click menu options. Now, you can automatically regenerate any BigML resource (models, evaluations, predictions, etc) with a single click. Scriptify creates a script that contains all the workflow information end-to-end (from configuration parameters to resources created). You can precisely repeat the processing steps of any original Machine Learning resource to your heart's desire! Jan2017 These new Dashboard statistics allow you to introspect the predictive power of your model by revealing the significance of each coefficient estimate. BigML computes the likelihood ratio to test how well the model fits your data along with the p-value, confidence interval, standard, error and Z score for each coefficient.Learn more about the Logistic Regression statistics in the Dashboard documentation. Jan2017 Now, you can easily clone datasets, models and scripts, from other users into your BigML account. Provided that a user shares a resource using the sharing link and the cloning capability is enabled, any other user with access to the link will be able to include this resource in their BigML account.This new feature will allow you to fully use the shared resources. For example, when another user shares a dataset using the sharing link, it is in "view only" mode, so you can not perform any actions such as creating new models, exporting it, sampling it, etc. Now, by cloning it, you will be able to perform all BigML actions available for datasets. Jan2017 BigML is bringing predictions for Associations to the Dashboard. Association Sets allow you to pinpoint the items which are most strongly associated with your input data. For example, given a set of products purchased by a person, what other products are most likely to be bought?All the predicted items will be ranked according to a similarity score, and they will be displayed in a table view. You can also visualize each predicted rule in a Venn diagram to get a sense of the correlation strength between the input data and the predicted items. Read more about Association Sets in the 8th chapter of the Associations documentation. Jan2017 We are happy to announce BigML Certifications, for organizations and professionals that want to master BigML to successfully deliver real-life Machine Learning projects. These courses are ideal for software developers, system integrators, analysts, or scientists, to boost their skill set and deliver sophisticated data-driven solutions. We offer two separate courses, each of them consisting of 4 weekly online classes of 3 hours each:Certified Engineer: all you need to know about advanced modeling, advanced data transformations, and how to use the BigML API (and its wrappers) in combination with WhizzML to build and automate your Machine Learning workflows.Certified Architect: learn how to implement your Machine Learning solutions so they are scalable, impactful, capable of being integrated with third-party systems, and easy to maintain and retrain.If you successfully pass the certification exam, BigML will award you with a. BigML Documentation. Welcome to BigML! Get familiarized with the BigML platform by reading our detailed documentation and learn how to use the BigML Dashboard, the BigML API, and