datarobot what is aml hero

O que é Automated Machine Learning?

Automated machine learning, as pioneered by DataRobot, replaces much of the manual work required by a more traditional data science process. But to be considered a trusted, end-to-end enterprise AI solution, a platform must meet a broader set of key requirements. DataRobot is the first, and only, enterprise AI platform to address all 10 steps required to effectively automate the building and deployment of advanced AI applications.

Por que Automated Machine Learning é Importante para Você?

Automated machine learning aproveita os pontos fortes de humanos e computadores. Os seres humanos são excelentes em comunicação, engajamento, contexto e conhecimento geral, além de criatividade e empatia. Os computadores e sistemas de software são ideais para tarefas repetitivas, matemática, manipulação de dados, e processamento paralelo — oferecendo o poder e a velocidade para dominar soluções complexas.


Em resumo, automated machine learning é:

Um Sistema Especializado

Incorporando melhores práticas dos maiores cientistas de dados do mundo, o sistema seleciona automaticamente os algoritmos mais adequados de machine learning para testar com seus dados e o desafio específico.

Confiável

Oferece explicações descomplicadas, de fácil interpretação para os humanos sobre como um algoritmo de machine learning toma suas decisões e refaz modelos quando os dados sugerem que os padrões mudaram.

Automated machine learning cria uma nova classe de “cidadãos cientistas de dados”, com o poder de criar modelos avançados de machine learning, sem a necessidade de aprender a codificar ou compreender quando e como aplicar certos algoritmos. Os cientistas de dados são também mais produtivos, uma vez que as etapas repetitivas no processo de desenvolvimento do modelo são automatizadas, permitindo que usem sua expertise diferenciada para selecionar e refinar os modelos.

Os 10 Passos de Automated Machine Learning

Automated machine learning substitui a maior parte do trabalho manual necessário em um processo mais tradicional da ciência de dados Porém, para que seja considerada uma solução completa de machine learning, é preciso que a plataforma atenda a TODOS esses requisitos essenciais.A DataRobot é a primeira e única plataforma de machine learning a abordar todos os 10 passos necessários para realmente automatizar o desenvolvimento e a aplicação de modelos de machine learning.


1 2 3 4 5 6 7 8 9 10

Data Identification

Data Preparation

Engenharia de Recursos

Algorithm Diversity

Seleção de Algoritmos

Treinamento e Ajuste

Competições Diretas de Modelos

Insights human-friendly

Fácil Aplicação

Monitoramento e Gestão de Modelos

  • Passos 1:

    Data Identification

    Data is the fuel that drives high-scale innovation with AI. Ironically, many organizations struggle to use their data effectively because of the overwhelming number of data sources that are available, and no clear way to identify the most trusted sources of data. The AI Catalog inside DataRobot serves as a centralized source of truth for data engineers, data stewards, data scientists, and analysts to gain self-service access to AI assets they can trust.

    Introducing DataRobot AI Catalog

  • Passos 2:

    Data Preparation

    Todo algoritmo de machine learning trabalha de forma diferente e possui diferentes requisitos de dados. Por exemplo, alguns algoritmos precisam de informações numéricas para serem normalizados, outros não. O DataRobot transforma dados brutos em um formato específico que cada algorítimo precisa para atingir a performance ideal e segue as melhores práticas de particionamento de dados.Como Que Blueprints de Modelos Agregam Valor ao DataRobot?

  • Passos 3:

    Engenharia de Recursos

    Feature engineering is the process of modifying data to help machine learning algorithms work better and is often time-consuming and expensive. DataRobot engineers new features from existing numeric, categorical, and text features. It knows which algorithms benefit from extra feature engineering and which don’t and only generates features that make sense given the data characteristics.

    DataRobot Automated Feature Engineering

  • Passos 4:

    Algorithm Diversity

    Every dataset contains unique information that reflects the individual characteristics of a business. Due to the variety of situations and conditions, one algorithm cannot successfully solve every possible business problem or dataset. Some machine learning automation platforms only give users access to a few types of algorithms, but with DataRobot you get immediate access to hundreds of diverse algorithms, and the appropriate pre-processing, to test against your data in order to find the best one for your particular AI challenge.

    AIs are Individuals, Just Like People

  • Passos 5:

    Seleção de Algoritmos

    Having hundreds of algorithms at your fingertips is great, but in many cases users don’t have time to try each and every algorithm on their data. Some algorithms aren’t suited to the data, some are not suited to the data sizes, and some are extremely unlikely to work well on the data. DataRobot will only run the algorithms that make sense for your data.

    Can An AI Recommend the Best Algorithm for Me?

  • Passos 6:

    Treinamento e Ajuste

    It’s standard for automated machine learning software to train the model on your data. DataRobot takes this a step further by using smart hyperparameter tuning, not just brute force, to tune the most important hyperparameters for each algorithm. The platform can also create ensemble models (also known as “blenders”) that combine the strengths of several algorithms and balance out the weaknesses of others. Ensemble models typically outperform individual algorithms because of their diversity. DataRobot finds the optimal algorithms to blend together and tunes the weighting of the algorithms within each blender model.

    Data Science Fails: There’s No Such Thing As A Free Lunch

  • Passos 7:

    Competições Diretas de Modelos

    You won’t know in advance which algorithm will perform the best, so you need to compare the accuracy and speed of different algorithms on your data regardless of which programming language or machine learning library they came from. You can think of it as a competition amongst the models where the best model wins. DataRobot builds and trains dozens of models for AI machine learning automation, comparing the results, and ranking the models by accuracy, speed, and the most efficient combination of the two.

    Competition in AI Blog

  • Passos 8:

    Insights human-friendly

    Over the past few years, some automated machine learning tools and AI have made massive strides in predictive power, but at the price of complexity. It is not enough for a model to score well on accuracy and speed – you also have to trust the answers it is giving. And in regulated industries, you must justify the model to a regulator. DataRobot explains model decisions in a human-interpretable manner, showing which features have the greatest impact on the accuracy of each model and the patterns fitted for each feature. DataRobot can also provide prediction explanations to illustrate the key reasons why a specific prediction was made.

    Give me one good reason to trust artificial intelligence

  • Passos 9:

    Fácil Aplicação

    Harvard Business Review once described a team of analysts that built an impressive predictive model, but the business lacked the infrastructure needed to directly implement the trained model in a production setting, which was a waste of time and resources. All DataRobot models are production-ready, and can be deployed in several ways on standard system hardware.

    Machine Learning Model Deployment

  • Passos 10:

    Monitoramento e Gestão de Modelos

    In a constantly changing world, your AI applications will start to decay over time as the data that’s being used to make predictions is different than what the model was trained on. Unfortunately, figuring out when to replace an outdated model is difficult because traditional IT tools for managing software applications don’t effectively work for machine learning models. DataRobot MLOps provides a common framework for model deployment, monitoring, and governance no matter what data science language or software tool was used to create the model.

  • Step 1: Data Identification

    Data is the fuel that drives high-scale innovation with AI. Ironically, many organizations struggle to use their data effectively because of the overwhelming number of data sources that are available, and no clear way to identify the most trusted sources of data. The AI Catalog inside DataRobot serves as a centralized source of truth for data engineers, data stewards, data scientists, and analysts to gain self-service access to AI assets they can trust.

    Introducing DataRobot AI Catalog

  • Step 2: Data Preparation

    Todo algoritmo de machine learning trabalha de forma diferente e possui diferentes requisitos de dados. Por exemplo, alguns algoritmos precisam de informações numéricas para serem normalizados, outros não. O DataRobot transforma dados brutos em um formato específico que cada algorítimo precisa para atingir a performance ideal e segue as melhores práticas de particionamento de dados.Como Que Blueprints de Modelos Agregam Valor ao DataRobot?

  • Step 3: Engenharia de Recursos

    Feature engineering is the process of modifying data to help machine learning algorithms work better and is often time-consuming and expensive. DataRobot engineers new features from existing numeric, categorical, and text features. It knows which algorithms benefit from extra feature engineering and which don’t and only generates features that make sense given the data characteristics.

    DataRobot Automated Feature Engineering

  • Step 4: Algorithm Diversity

    Every dataset contains unique information that reflects the individual characteristics of a business. Due to the variety of situations and conditions, one algorithm cannot successfully solve every possible business problem or dataset. Some machine learning automation platforms only give users access to a few types of algorithms, but with DataRobot you get immediate access to hundreds of diverse algorithms, and the appropriate pre-processing, to test against your data in order to find the best one for your particular AI challenge.

    AIs are Individuals, Just Like People

  • Step 5: Seleção de Algoritmos

    Having hundreds of algorithms at your fingertips is great, but in many cases users don’t have time to try each and every algorithm on their data. Some algorithms aren’t suited to the data, some are not suited to the data sizes, and some are extremely unlikely to work well on the data. DataRobot will only run the algorithms that make sense for your data.

    Can An AI Recommend the Best Algorithm for Me?

  • Step 6: Treinamento e Ajuste

    It’s standard for automated machine learning software to train the model on your data. DataRobot takes this a step further by using smart hyperparameter tuning, not just brute force, to tune the most important hyperparameters for each algorithm. The platform can also create ensemble models (also known as “blenders”) that combine the strengths of several algorithms and balance out the weaknesses of others. Ensemble models typically outperform individual algorithms because of their diversity. DataRobot finds the optimal algorithms to blend together and tunes the weighting of the algorithms within each blender model.

    Data Science Fails: There’s No Such Thing As A Free Lunch

  • Step 7: Competições Diretas de Modelos

    You won’t know in advance which algorithm will perform the best, so you need to compare the accuracy and speed of different algorithms on your data regardless of which programming language or machine learning library they came from. You can think of it as a competition amongst the models where the best model wins. DataRobot builds and trains dozens of models for AI machine learning automation, comparing the results, and ranking the models by accuracy, speed, and the most efficient combination of the two.

    Competition in AI Blog

  • Step 8: Insights human-friendly

    Over the past few years, some automated machine learning tools and AI have made massive strides in predictive power, but at the price of complexity. It is not enough for a model to score well on accuracy and speed – you also have to trust the answers it is giving. And in regulated industries, you must justify the model to a regulator. DataRobot explains model decisions in a human-interpretable manner, showing which features have the greatest impact on the accuracy of each model and the patterns fitted for each feature. DataRobot can also provide prediction explanations to illustrate the key reasons why a specific prediction was made.

    Give me one good reason to trust artificial intelligence

  • Step 9: Fácil Aplicação

    Harvard Business Review once described a team of analysts that built an impressive predictive model, but the business lacked the infrastructure needed to directly implement the trained model in a production setting, which was a waste of time and resources. All DataRobot models are production-ready, and can be deployed in several ways on standard system hardware.

    Machine Learning Model Deployment

  • Step 10: Monitoramento e Gestão de Modelos

    In a constantly changing world, your AI applications will start to decay over time as the data that’s being used to make predictions is different than what the model was trained on. Unfortunately, figuring out when to replace an outdated model is difficult because traditional IT tools for managing software applications don’t effectively work for machine learning models. DataRobot MLOps provides a common framework for model deployment, monitoring, and governance no matter what data science language or software tool was used to create the model.

Automated Machine Learning Capacita Toda Sua Organização

Encontrar e reter cientistas de dados costuma ser a parte mais difícil para se implementar IA e machine learning em uma empresa. Com automated machine learning, você qualifica os profissionais de analítica de dados e engenheiros de software a desenvolver modelos preditivos e embutir IA em aplicativos – ao mesmo tempo, tudo isso traz mais produtividade e motivação a seu pessoal de ciência de dados.
Profissionais de Analítica
Com treinamento prático, com a mão na massa e o apoio da equipe de primeira linha da DataRobot, os profissionais de analítica de dados rapidamente se transformam em analistas de IA, que descobrem e se concentram no que é mais importante para gerar valor real do negócio.
Engenheiros de Software
Os engenheiros de software são cruciais para gerar valor em modelos de machine learning, integrando-os aos sistemas de produção. A DataRobot oferece o treinamento, ferramentas e o suporte para possibilitar que engenheiros de software se transformem em engenheiros de IA.
Cientistas de Dados
Quando as tarefas mundanas de desenvolvimento de modelos são automatizadas - como segregação de dados, ajuste do modelo, seleção de funcionalidades, etc. - cientistas de dados habilidosos produzem radicalmente mais do que seriam capazes usando abordagens tradicionais de codificação manual. A DataRobot também proporciona aos experts a flexibilidade de customizar seus modelos quando necessário.
Executivos
Quando os líderes empresariais compreendem a importância da IA, e como estruturar um projeto de machine learning, e falar sobre ele com suas equipes, eles trazem todo seu conhecimento de domínio e a experiência que agregaram para ajudar a empresa a desenvolver aplicações de IA.
  • Profissionais de Analítica
    Com treinamento prático, com a mão na massa e o apoio da equipe de primeira linha da DataRobot, os profissionais de analítica de dados rapidamente se transformam em analistas de IA, que descobrem e se concentram no que é mais importante para gerar valor real do negócio.
  • Engenheiros de Software
    Os engenheiros de software são cruciais para gerar valor em modelos de machine learning, integrando-os aos sistemas de produção. A DataRobot oferece o treinamento, ferramentas e o suporte para possibilitar que engenheiros de software se transformem em engenheiros de IA.
  • Cientistas de Dados
    Quando as tarefas mundanas de desenvolvimento de modelos são automatizadas - como segregação de dados, ajuste do modelo, seleção de funcionalidades, etc. - cientistas de dados habilidosos produzem radicalmente mais do que seriam capazes usando abordagens tradicionais de codificação manual. A DataRobot também proporciona aos experts a flexibilidade de customizar seus modelos quando necessário.
  • Executivos
    Quando os líderes empresariais compreendem a importância da IA, e como estruturar um projeto de machine learning, e falar sobre ele com suas equipes, eles trazem todo seu conhecimento de domínio e a experiência que agregaram para ajudar a empresa a desenvolver aplicações de IA.

Como o DataRobot fornece IA corporativa

A plataforma de IA corporativa da DataRobot democratiza a ciência de dados e automatiza o processo de ponta a ponta para criar, implantar e manter a IA em escala.

with datarobot
old way
Quais projetos você vai usar o DataRobot?