117 docs tagged with "ML model"

Description

Input: Generated CSV file with results of IE process.

Annotators help to extract structured information from unstructured

A Worker is a single instance of a Worker application process started by BEP to process tasks.

Dashboard is out-of-the-box tool in WorkFusion to provide all kinds of analytics including AutoML statistics. It is a convenient and user-friendly way to present model statistics to the customer as well.

Introduction

AutoML plugins allow interacting with AutoML services from Bot Tasks.

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The recommended best practice is to develop a Python model package as a pure Python project, with tests and so on, and to use it as a dependency in an Archetype-generated project intended for integration with the WorkFusion ecosystem.

Task: Review model mistakes. Compare Tagged and Extracted values.

Task: Review model mistakes (compare tagged and extracted values)

Task: Calculate tags distribution in gold data

AutoML uses two services:

Note: Checker model should be used by an ML Engineer to improve

WorkFusion AutoML models provide an ability to automatically classify texts by analyzing its parts (tokens) and their combinations (features).

Search Engine is a framework for optimizing AutoML model parameters. WorkFusion offers three versions of the framework that you can choose from based on your purposes:

Task: Compare results of two models.

The automatic algorithm selection is a mechanism that finds the best classifier for a dataset to apply during model training.

Install Java

To make the most of AutoML SDK, use its components together in one configuration. The main hyper model class located at [ModelPackage]/model/[ModelName]Model.java contains a @HypermodelConfiguration annotation with a reference to the AutoML SDK configuration class.

The general flow to configure the sensitivity analysis is as follows:

Templates provide a way to reuse objects. Almost every object within IA Cloud Enterprise, including Manual Tasks and Business Processes, is based on templates.

WorkFusion AutoML empowers intelligent automation by enabling Machine Learning models to learn from user answers in Manual Tasks (MT).

AutoML SDK configuration is a Java Spring-like set of definitions

After reading this guide, you'll know how to prepare and run a Business Process with a trained model and a Manual Task.

AutoML SDK offers the following types of Maven archetypes related to custom Python pipelines:

The customization affects the pipeline.py file located on the following path: src/main/python/package/automl/ml/lib//pipeline.py. The file is part of a project template and serves as an entry point for integrating Python models.

To understand a Data Analyst's expected contribution to a successful project delivery, let's first outline the context of a cognitive automation implementation.

From the article, you can learn the following information about the Data Analyst (DA) role:

A Data Analyst (DA) acts based on several factors originating from the customer's data analysis. The article shares a typical workflow for new DAs.

Checklists are helpful as quality control techniques for documenting requirements. They can include a standard set of quality elements to capture the requirements. The purpose of a checklist is to ensure that items defined by the organization or the project team are essential and included in the final requirements or that issues determined by the project team are addressed.

Task: Split data set into test and training according to the selected proportion starting from the least popular field

Task: split data set into test and training for Classification.

Task: Identify and delete duplicate documents in a dataset.

After creating an AutoML project from an Archetype, you can deploy it to a Control Tower instance, even without training.

To set up a custom Python environment in a cluster and enable running a model in it, follow the instructions below.

Goal of the step: design a Manual Task with the business logic of

The main idea is to enable ML engineers to implement specific parts of training and execution pipelines, namely the composer workflow, on the Python side or using other supported platforms and languages. To this end, two extension points were implemented:

Goal: Get tagged data set of high quality.

The topic describes how to enable Tableau-based analytics to collect and visualize model data using an out-of-the-box (OOTB) AutoML Business Process (BP).

This article provides recommendations on how to adjust AutoML Worker parameters, such as CPU and memory consumption, for better cluster utilization and throughput.

Data Analyst manual is an advanced guide developed to teach the Data Analyst role for projects which use WorkFusion Machine Learning capabilities. Its goal is to give an end-to-end understanding of the qualitative ML Training Set collection. This guide teaches best practices and WorkFusion Center of Excellence (CoE)-approved delivery methods.

You can extend AutoML SDK with a set of Python classifiers based on Scikit-learn, MXNet, and so on. The extension allows using ML algorithms implemented in Python with AutoML pipelines.

Features) are individual measurable properties or characteristics of data being observed. Selecting relevant features and deciding how to encode them for a training can have a great impact on model results. The process of feature development is known as feature engineering or feature generation. Simply put, it is a process of manually constructing new attributes from raw data. It involves intelligent combining or splitting the existing raw attributes into new ones which have a higher predictive power.

A Feature Extractor is an AutoML SDK component that has in-built logic to analyze each Token in a Document and produce a set of independent and

Formats

To simplify Python development, an integration was enabled with pure Python projects. You can use the projects to develop custom Python models instead of the mixed Java-Python approach described in the preceding articles of the section.

To get a model explanation, act as follows:

AutoML SDK is a WorkFusion-built Java framework for creating and customizing Machine Learning models. It is a powerful tool in addressing classification and information extraction use cases.

The chapter describes the end-to-end delivery flow of a Machine Learning (ML) project.

Grouping is a mechanism, enabling developers to combine existing or new models into one artifact. The main goal is to reduce the number of Workers and minimize the start time on the cluster.

Hybrid models are an ensemble model type containing the routing logic. They serve the following two purposes:

Task: Identify or count keywords in documents for rule-based classification or features

The tutorial walks you through the implementation of the following typical custom models: Information Extraction (IE) and Classification with or without Python support.

The section describes how to manually create an Asset Bundle to deploy an old AutoML project. In a new AutoML project, an Asset Bundle is already created as a part of the project immediately after its generation from the Archetype. For more details, refer to Start from Archetype.

Information extraction is a process of extracting structured

Typically, a Data Analyst deals with an unspecified variety of documents during the start of Use Case analysis and implementation. To perform a proper analysis of the documents, a Data Analyst has to deeply understand and drive work using following characteristics:

Goal: To understand the business logic of the Use Case and documents.

What is sensitivity analysis?

AutoML is a complex technology requiring a deep understanding of the core concepts behind Machine Learning and automation. The article provides visualizations of the supported automation flow and details typical use cases covered with the AutoML technology.

Goal: Get tagged data set of high quality

In order to ensure a data set is of high quality, result of labeling (tagging) must be:

This guide describes how to run a model with pre-configured sensitivity analysis and get model explanations via the Control Tower (CT) user interface.

The main goal of AutoML and BEP integration is to use the BEP platform for:

Optical Character Recognition

WorkFusion offers models for but not limited to the following use cases:

Machine learning (ML) is a branch of computer science that applies various techniques to enable a computer to learn from data without substantial programming.

Control Tower is the heart of Intelligent Automation Cloud used for automation. Use Control Tower to perform the following actions:

Control Tower allows users to manage hyper models, associated trained models, and model trainings. To access the capability, log in to Control Tower and, on the main menu, click AutoML.

To access a list of available trained models, select AutoML > Trained models. Control Tower updates the list every time you complete a model training, or a new trained model is deployed to your environment.

To access a list of available hyper models (artifacts), select AutoML > Hyper models. Control Tower updates the list every time a new hyper model is deployed to your environment.

You can use a merge and split utility for Information Extraction models trained on different machines or with different fieldsets. The utility requires AutoML SDK version 10.2.4.6 or later.

The IE case mathematical description contains the following steps.

Once the first results are received, there is low probability that they will meet all the success criteria without any changes and tuning. More often, some enhancements should be done.

Model training

Task: Clean up or format data values in gold or extracted values.

The goal of this stage is to achieve acceptable OCR configuration and get understanding of final OCR quality.

The majority of all use case implementations apply a number of common Feature Extractors most of which are reusable. AutoML SDK contains most used Feature Extractors optimized and ready out-of-the-box with AutoML SDK API. These Feature Extractors are intended to cover general use cases without any need to write your own from scratch.

The first stage of document processing is a Parser: an AutoML SDK component which removes HTML tags from training set documents, and then

Automatic Quality Control (AutoQC) refers to the use of statistical methods in the monitoring and maintaining the quality of products and services. In the WorkFusion Automation workflow, the AutoQC sub-process chooses a cost-effective combination of automated machines and cloud workers to deliver at or above the acceptable quality level.

A common concern of an enterprise is whether it has enough allocated resources to handle an increase in users or the number of transactions.

Post-Processing is the final stage of the AutoML SDK pipeline performed after the model training. Post-Processing modifies the trained model output to fit the requirements of a customer by applying pre-defined rules. In rare cases, based on a separate ML model.

Normalizer Search Engine tests different combinations of normalizers for each field to select the optimal chain. The final choice is the most useful and the safest for particular case. This minimizes the practice of designing custom post-processors, so MLE doesn't have to write a post-processor in the majority of the cases.

This page provides a number of Post-Processor examples covering a variety of use cases.

Training set

Python 2 environment

Goal: Train SMEs to tag the documents properly to ensure high-quality data set and to prepare documents for tagging

Dataset requirements

To prepare a distribution bundle for setting up a custom environment in a cluster on an Agent, build Python and fetch dependencies as described below.

Goal: To prepare the SME team for labeling documents.

Requires ODF v10.1.0.9 or later.

Goal: Report ML quality to the customer and communicate poor results, if any, in an accurate yet positive way, and propose further solutions.

Goal: To analyze the results of the ML model and propose ways to improve them.

Below are triggers for AutoML to start training a machine learning (ML) model:

Model retraining is a mechanism that allows you to easily improve model quality. The basic operations include actual retraining of your model, comparison of statistics of two models, applying the retrained model to the use case, and downloading and uploading the use case training dataset or model.

Task: Study documents, sort out poor quality documents, identify templates, categories, and questionable documents.

After you activate a Cognitive Bot by configuring AutoML in a Manual Task and run the Manual Task, Control Tower automatically launches an instance of the out-of-the-box AutoML Business Process. This BP is auto-created, with the original Manual Task inside it used for those cases when manual processing is still required.

To access a list of ongoing or completed model trainings, select AutoML > Trainings. Control Tower updates the list every time you start a model training.

Reminder note on problem statement

The best practices described are validated on onsite SMEs training with the timeline 1 week, group of 20 Subject Matter Experts and 2 Data Analysts. The Use Case was IE from 10 different document types with complicated cross-document dependencies and integration with third-party systems.

Task: Split PDF files that contain multiple documents into separate files.

Goal: Provide appropriate documents for model training and estimation.

Goal: Split verified documents into two parts for model training and testing.

ML archetypes location

To better understand the model results, AutoML SDK provides comprehensive statistics for further analysis and model tuning.

To check the model quality, you can prepare a test set of documents and run execution locally. ModelEvaluationRunner.java contains logic similar to that of the training runner.

Goal: Analyze results of ML training and propose ways to improve them.

For details of AutoML SDK configuration, refer to AutoML SDK introduction and AutoML SDK workflow.

Train model using ML SDK

A model generated from an Archetype contains a local runner that allows training the configured model. The runner is located at ../run/ModelTrainingRunner.java.

Goal of the step: Train Subject Matter Experts (SMEs) to provide a high level of tagging accuracy for collecting a quality data set.

Report

Task: Upload data from a Data Store in Control Tower or a local CSV file.

Notebook name: DADB_pipeline

WorkFusion supports software versions as long as it is possible without hindering the development of new features and applying cutting-edge technologies. When upgrading to the latest version of IA Cloud, make sure it is compatible with your current AutoML Server and AutoML SDK version.

Creating NER annotators

The Machine Learning Engineer (ML Engineer) is one of the most technically skilled roles. The ML Engineer background includes a variety of completed end-to-end Java-based projects and a wide experience in creating business applications, rolling out to production, and dealing with environment challenges.