Community Chair

Top AutoML Technologies

AutoML is a platform that simplifies each step in the machine learning process, from working with the raw dataset to deploying machine learning models. In traditional practices, the entire process of developing and deploying machine learning models is very manual and each step in the process must be handled separately.

As credit unions are starting to advance their usage of methods to get to insights and take action on them, they are facing several challenges when it comes to the successful implementation and adoption of ML solutions.

Here are some of the capabilities of AutoML solutions to explore when you are looking to use them.

  • Functionality – What steps of the machine learning model development can be partially or fully automated? How much control do you have over automated steps? Does the solution offer APIs to programmatically interact with the solution?
  • ML Methods – What ML methods such as classification, regression, etc. are included in the solution?
  • Explainability and Transparency – What capabilities are available for users to better understand and trust the results of the models?

Here are some of the top solutions to consider.

1. Neuton

Neuton.AI is a disruptive provider of a no-code AutoML that helps to explore data insights and make predictions. This tool is an optimal solution for Credit Unions to solve their common business challenges, such as risk estimation, service personalization, lifetime value predictions, and new member attraction, among others.
Since the platform doesn’t require any programming skills or data science knowledge, users of any technical background can easily build models and interpret the results in several clicks. Neuton’s intuitive interface, fully automated pipeline, and high accuracy enable Credit Unions to speed up decision-making and reduce the time to implementation to several days rather than months.

You can check out use cases by Neuton AI here - https://neuton.ai/case_studies

9757383875?profile=RESIZE_710x

Highlights

  • Ease of use: Neuton’s simple sign-up and clear interface enable business users to create models without engaging a data scientist.
  • Fully automated processes: All steps from infrastructure provisioning and data preprocessing to feature engineering and model creation are automated.
  • Proven business cases for Credit Unions: Neuton’s website contains detailed case studies focusing on Credit Unions. Plus, they state that their professional services team helps with the implementation of the first projects.
  • Comprehensive explainability options: Users can evaluate model quality at every stage, identify the logic behind the model analysis, and understand why certain predictions have been made.
  • No need for coding or programming skills: Any business users without special knowledge in Artificial Intelligence or Machine Learning, including managers and marketers, can build models with Neuton.
  • Free version: Users are able to train an unlimited number of models for free.
    Speaking of paid subscriptions, with fewer cloud infrastructure requirements, the cost of training and prediction is lower compared to others.

Know More

2. Amazon Sagemaker

For creating automatic machine learning models that too with full visibility, one can go with Amazon Sagemaker Autopilot. It can be used when some data is missing. It automatically infers the type of predictions that best suits the data. It was launched in 2019 to implement different steps of ML workflow.

9757384063?profile=RESIZE_710x

Highlights:

  • With automatic data pre-processing and automatic feature engineering features, Sagemaker offers complete visibility and control for non ML and ML experts. 
  • Versatility: It is best for handling classification and linear regression type of ML problems. For tackling forecasting problems, one can use Sagemaker’s hyper-parameter tuning service.
  • Flexibility: It provides statistical insights and flexibility to explain custom alerting schemes for model monitoring. 
  • It is cloud based solution and fully scalable. 
  • Transparency: One can check the original code, used to develop each candidate mode to provide full transparency.

Know More

3. Data Robot

Launched in 2015, Data Robot’s AutoML allows automation of the entire data science lifecycle from raw data to value. It also empowers in moving beyond automated machine learning and building innovative models from the diverse type of data. Furthermore, speedy deployment and ease of use are always at the core of Data Robot’s AutoML. Those looking for a fully explainable AI through human-friendly visual insights can go with Data Robot.

9757384300?profile=RESIZE_710x

Highlights:

 

  • Flexibility: One can reap the benefits with end-to-end acceleration. Besides this, one can easily control the various parameters of modeling processes. 
  • Contains a library of open-source and proprietary models to simplify the use. 
  • Ease of Use: With human-friendly explanations, it offers GUI for users to interact with solutions. Python client API is also provided with Data Robot, and one can install it on-premises, as well as most major cloud platforms.
  • It is versatile in terms of tackling classification, regression, and forecasting types of ML problems.
  • Transparency: Several types of explanations including feature impact, feature effect, feature fit, and variable effects are provided for the ease of users. 

Know More

4. Driverless AI

It is preferred for faster and efficient automation of projects. It came into being in 2017 as an enterprise solution by H2O. It deploys a library of algorithms and feature transformations to easily engineer new and high-value features for the dataset. It is capable to automate some of the difficult data science and machine learning workflows including Feature Engineering, Model Validation, Model Tuning, Model Selection, and Model Deployment among others.

9757384697?profile=RESIZE_710x

Highlights: 

  • Functionality: Built with state-of-the-art techniques, it is capable to fully or partially automate the ML workflow steps. 
  • Driverless AI: It has driverless AI that helps in detecting basic data types and data visualization.
  • It is Versatile enough to handle classification, regression, and forecasting types of problems involving structured and unstructured data.
  • It is Flexible, as it can be customized through expert settings as well as the "Bring Your Own Recipe" feature. This way, it empowers users to define custom data sources. 
  • Transparency: An auto-generated report is provided containing all the details and steps taken by driverless AI to develop the final scoring.

Know More

5. Google AutoML

It was introduced in 2018 to offer AutoML capabilities to non-ML experts. Google AutoML is one of the best creations of Google to design neural nets. One can opt for it to build a custom machine learning model in minutes. It is a cost-effective solution and gives reliable performance for all sorts of complex data processing.

Highlights

    1. It has an easy-to-use graphical interface, provides the functionality of a data labeling service to ease the data preparation steps of the ML workflow. 
    2. Its Versatilityfeature can be used for handling classification and regression problems involving unstructured text, image, and video data in addition to structured data. It is capable enough to manage models with confidence.
    3. It is Flexible to prepare and store datasets. Also, offers customization options for model development and optimization process.
    4. Transparency: One can look for two pre-prediction explanations that are feature attribution types for ensuring trust. 

Know More

6. H2O Auto ML

Introduced in 2017, H2O AutoML is a brilliant tool to automate the machine learning workflow. It offers several model explainability methods that apply to AutoML objects and individual models. It is good for advanced users as it reduces the number of code lines thus helping the user to focus on other aspects. 

9757385887?profile=RESIZE_584x

Highlights

  • Ease of Use: With a unified interface, it empowers in automating model selection, learning, and finalizing the steps of the ML workflow. It also performs model learning and hyper-parameter tuning of base models.
  • It generates models automatically and handles classification & regression types of ML problems. 
  • It is an open-source platform with the flexibility to control the AutoML optimization process through a set of parameters. 
  • Installation: It can be easily installed on all major cloud platforms.
  • Transparency: One can check for documentation, and references to developed base models to enhance their explainability.

Know More

7. Microsoft Azure AutoML

Introduced in 2018, it empowers the user to quickly customize the models and apply control settings to iterations, validations, and other experiment criteria. Microsoft launched it with a target to serve non ML experts for rapid ML model development and deployment. Also, preview and statistics features are there to help in validating data before developing models.

9757385500?profile=RESIZE_710x

Highlights:

  • It has in-built capabilities for common machine learning tasks like classification and regression.
  • Feature Engineering: For automatically handling unstructured text data, feature engineering is really helpful. Besides, it uses no-code UI and SDK for model building.
  • With easy data exploration and responsible ML solution, it offers customization facility through advanced settings, model validation approach, model trials, and more.
  • Transparency: Both local and global feature types of explanations are available for ensuring transparency. besides, documentation is provided including how-to guides and more.

Know More

So, these are some of the major AutoML technologies that are well-positioned to benefit users as these solutions can automate lower value tasks of data prep, feature engineering, model selection, learning, finalization, validation, packaging, deployment, and management. The demand for AutoML solutions has increased over the past few years. Businesses are aiming to reap the benefits from it. Successful implementation of machine learning requires addressing the three “ Ts” of ML; time, talent, and trust. With AutoML solutions, challenges with these three “Ts” can also be alleviated as it is expected to play an important role in the years to come.

 

E-mail me when people leave their comments –

You need to be a member of CULytics Community to add comments!

Join CULytics Community

 

advantedge
altair
ibi
arka
trellance
coopfs
dfa
wherescape
alkami
prismacampaigns
marquis
aiq
totex
cnet
datava
aun
cinch
know

Related Post

 

Ad Unit Settings






Ad Url Settings

 

api-lead-approach
the-amazon-lending-experience
executing-advanced-analytics-do-s-and-don-t
lending-transformation-old-vs-new
data-journey-building-strong-analytical-practices
4-step-iterative-process-building-a-relevant-analytics-practice
significant-measures-towards-new-normal
building-a-strong-analytics-practice-recipe-for-success
data-warehouse-evaluation-and-implementation
explainable-ai-trust-and-transparency
forecasting
top-50-members-using-transactional-website-jun-2020
top-50-cus-with-highest-and-lowest-efficiency-june-2020
importance-of-financial-risk-management
secret-sauce-for-long-term-sustainable-business-intelligence-succ
top-pfm-technologies
secret-sauce-for-long-term-sustainable-business-intelligence-succ
top-pfm-technologies
data-warehouse-and-bi-technologies-opportunities-challenges
top-chatbot-technologies
keys-to-building-an-effective-branch-or-atm-network
top-50-credit-unions-with-highest-and-lowest-accounts-per-member
lowest-and-highest-net-income-per-branch
marketing-holy-grail
top-50-most-and-least-delinquent-credit-unions
modern-marketing-technologies
incremental-low-cost-data-driven-wins
power-of-storytelling
the-cost-of-not-investing-in-data-governance
questions-you-should-ask-before-investing-in-data-warehouse
learnings-from-new-data-based-on-auto-loan-pricing
5-questions-you-need-to-ask-before-investing-in-data-governance
digital-marketing-maturity-models-for-credit-unions
marketing-expense-per-member
top-2-reasons-that-are-holding-credit-unions-back-when-they-are-i
using-data-analytics-to-manage-lending-complexity-while-driving-h
5-reasons-your-credit-union-should-invest-in-data-and-digital-now
top-50-most-and-least-efficient-credit-unions
retail-financial-services-outlook-during-covid-19
use-of-operational-analytics-to-mitigate-the-impact-of-covid-19
top-50-credit-unions-based-on-asset-size
cu-peer-comparison-dashboard
cu-peer-benchmark
all-about-machine-learning-engineering
top-web-design-trends
most-important-social-media-marketing-trends
state-of-digital-marketing-maturing-in-credit-unions
top-kpis-for-email-marketing
data-cloud-and-the-digital-transformation-imperative
digital-trinity-and-you
phases-of-financial-industry
analytics-roundtable-workshop
invitation-to-join-digital-transformation-hub
analytics-in-the-credit-union-business
value-of-member-centricity-and-analytics-in-the-growth-of-cus
all-about-membership-analytics
top-fraud-management-technologies
getting-started-with-your-data-analytics-journey
explore-vizualization-for-credit-unions
investment-in-website-personalization-technologies
data-analytics-supporting-cu-s-first-member-philosophy
loyalty-rewards-and-retention-technologies
member-experience-analytics
channel-analytics-and-its-importance
project-portfolio-management-technologies
investment-in-self-service-data-preparation-technologies
self-service-data-preparation-technologies
new-frontier-in-customer-experience-management
role-of-marketing-analytics-in-credit-unions
important-aspects-of-consumer-lending-analytics
kpis-on-website-analytics
journey-towards-bank-less-banking
investment-in-crm-technologies
top-omni-channel-vendors
conversational-banking-solutions
/top-kpis-for-chief-information-officer
mistakes-to-avoid-when-implementing-a-omnichannel-member
top-things-to-consider-when-building-dashboards
making-digital-marketing-more-agile-through-tag-managers
cecl-solution-providers
mistakes-to-avoid-while-implementing-marketing-automation
p2p-payment-integrated-solutions
kpis-for-social-media-tracking
kpis-for-human-resources-management
investment-in-fintechs-should-or-should-not
top-kpis-for-online-banking
investment-in-marketing-automation-technologies
investment-in-e-signature-technologies-should-or-should-not
tips-and-tricks-to-a-successful-bi-program
kpis-for-credit-card-business
kpis-for-digital-marketing
kpis-for-consumer-lending
hot-topics-for-credit-union-data-leaders
kpis-for-debt-collections
kpis-for-finance
website-personalization-tools
data-integration-technologies
robotic-process-automation-tools
why-data-analytics-initiatives-fail
electronic-signature-softwares
data-governance-tools-for-credit-unions
digital-and-mobile-banking-technologies
report-inconsistencies-are-frustrating
is-your-culture-ready-for-data-analytics
three-big-data-myths
turning-transaction-data-into-a-goldmine-a-becu-case-study
call-for-presentation-for-2019-credit-union-analytics-summit-is-n
top-10-keys-to-successful-data-analytics-practice
credit-union-chooses-accountscore-for-open-banking-transaction-da
how-much-do-you-spend-to-serve-a-customer
marketing-automation-technologies-for-credit-union
alexa-ask-first-abilene-fcu-for-my-balance
dataweb-content-management-technologies-for-credit-unions
efficiency-ratio
web-analytics-technologies
data-warehousing-software-for-banks
customer-experience-software
the-best-kept-secret-for-credit-union-data-analytics
mark-sievewright-on-technology-trends
naveen-jain-on-credit-union-analytics-summit-2018
why-analytics-doesn-t-make-a-difference-by-gary-angel
cuas2018-harnessing-the-right-data
build-a-financial-phone-assistant-for-your-credit-union-in-3-step
2018-culytics-analytics-challenge-winner
update-from-naveen
error-resolution
benefits-of-conversational-apps
who-are-your-most-valuable-members-part-1
how-alexa-can-help-your-credit-union
top-10-kpis-for-measuring-retail-channel-performance
how-much-is-too-much-personalization
top-10-kpis-for-measuring-contact-center-efficiency
pressure-on-margins-for-auto-loans-indirect-auto-loans-declining
best-business-intelligence-technologies-for-credit-unions
establishing-a-thriving-data-analytics-practice-is-a-journey
educational-presentations-from-the-2017-axfi-conference
modelling-alternatives-for-cecl-a-deep-future-analytics-study
data-analytics-use-cases-for-credit-unions-infographic
data-analytics-opportunities-in-credit-union-business
loan-application-analytics-with-cufx
machine-learning-delivers-great-consumer-experiences
deep-insights-of-credit-union-members-data-with-machine-learning
web-analytics-reporting-tips-for-credit-unions
big-data-strategy-roadmap-our-data-journey
webinar-framework-for-member-focused-decision-making
too-many-regulations-hurt-credit-union-members
digital-marketing-automation-solutions
online-banking-boom
transformation-transactions-to-relationships
top-dispute-management-technologies
2020-retail-trends
future-of-artificial-intelligence
2020-culytics-summit-attendee-dashboard
repositioning-the-role-of-marketing
marketing-automation-a-step-towards-marketing-transformation
strategic-agility
using-data-to-navigate-through-the-new-normal
digital-transformation-bcu
highest-and-lowest-new-loan-balances-per-branch-as-of-jun-2020
-new-members-ratio-as-of-june-2020
cus-with-highest-and-lowest-loan-grants-per-member-june-2020
self-service-data-preparation-technologies
highest-and-lowest-marketing-expense-per-member-june-2020
the-amazon-lending-experience
api-lead-approach
4-step-iterative-process-building-a-relevant-analytics-practice
data-journey-building-strong-analytical-practices
post-election-the-cu-outlook
most-and-least-delinquent-credit-unions-sept-2020
leveraging-ach-data-to-produce-real-outcomes
member-engagement-scores-benefits
member-engagement-key-to-serve-the-best
story-of-james-an-intelligence-transformation
executive-kpis-the-pulse-of-the-organization
untangling-member-journey
onboarding-strategy-to-deliver-success
the-importance-of-digital-technologies
top-interactive-financial-calculators
using-artificial-intelligence-to-improve-your-productivity
organizational-transformation-to-drive-growth
multi-year-journey-through-data-transformation
top-50-cus-with-the-highest-and-lowest-member-per-branch
digital-transformation-lessons-through-the-eyes-of-a-ceo
organizational-readiness-for-digital-transformation
ruthless-prioritization-to-do-more-to-learn-more-and-to-earn-more
performance-measures-for-digital-services
analytical-maturity-journey-towards-growth
less-is-more-the-necessity-of-focus-for-strategic-success
solving-the-crm-mrm-puzzle
insights-driven-messaging-member-and-product-onboarding
performance-measures-for-marketing
data-insights-that-drive-member-product-innovation
solving-the-crm-mrm-puzzle
the-agility-flywheel-a-strategy-that-never-goes-out-of-the-way
artificial-intelligence-as-a-playing-field-for-credit-unions
performance-measures-for-call-centers
top-automl-technologies
performance-measures-for-lending
building-business-case-for-data-analytics
driving-innovation-and-change
data-analyze-decide-and-create
digital-readiness-important-steps-to-achieve
digital-readiness-important-steps-to-achieve
enabling-credit-unions-with-ai
culytics-virtual-summit-2022-a-resounding-success
culytics-virtual-summit-2022-day-1
digital-banking-roundtable
digital-marketing-roundtable
transformative-lessons-from-a-chief-digital-officer
data-analytics-roundtable-mar-11
rewind-2022-culytics-day-key-highlights
data-analytics-team-roles
data-warehouse-development
data-analytics-team-size
is-your-data-analytics-program-not-delivering-results
active-deposit-management-for-profitable-growth
data-modeling
maximize-your-success-with-2023-CULytics-summit
biggest-opportunities-for-credit-unions
should-ceos-attend-the-culytics-summit
the-cost-of-a-wrong-decision
biggest-roadblocks-in-becoming-data-driven
a-journey-for-all-organizational-maturity-levels
maximize-your-data-analytics-checkup
navigating-the-data-analytics-landscape
improving-data-literacy
why-credit-union-leaders-should-invest-in-their-teams
why-credit-unions-should-not-invest-in-building-predictive-models
why-should-measure-the-success-of-data-analytics-program
cost-of-choosing-the-wrong-data-analytics-technology-stack
why-data-analytics-strategy-focus-on-supply-and-demand-side
kpis-to-measure-the-success-of-data-analytics-program
data-analytics-for-credit-union-branch-heads
data-organizing-principles
top-data-warehouse-storage-technologies
discover-the-hidden-truth-behind-watermelon-kpis
unveiling-the-hidden-dangers-of-cobra-effect-on-kpis
are-you-accurately-interpreting-your-kpi
unmasking-biases-a-guide-to-data-analysis-and-kpi-definition
uncover-the-power-of-proxy-kpis
unraveling-the-hidden-impact-of-sampling-bias-in-credit-unions
bi-department-structure
hidden-impact-of-confirmation-bias-in-credit-unions
getting-executive-attention-for-your-data-analytics-program
uncovering-biases-in-data-preprocessing
navigating-missing-data-in-credit-unions
navigating-sampling-bias-in-cu
unleash-the-power-of-real-time-data-use-cases
how-confirmation-bias-impacts-cus
breaking-down-selection-bias-in-credit-unions
unmasking-reporting-bias
elevate-your-cu-with-data-analytics-expertise
understanding-and-tackling-volunteer-bias-in-credit-unions
time-period-bias-in-credit-union
overcoming-biases-in-credit-unions
embracing-the-future-fast-future-fundamentals-program-equips-cred
unlock-growth-and-efficiency-credit-unions-guide-to-generative-ai
how-better-data-and-behavioral-biometrics-can-help-credit-unions-
harnessing-the-power-of-data-in-credit-unions
leveraging-third-party-data-a-strategic-guide-for-credit-unions
unlocking-member-insights-how-cus-can-leverage-third-party-data
enhancing-customer-experience-through-third-party-data
third-party-data-integration-techniques-and-technologies
the-future-of-lending-third-party-data-role-in-credit-decisioning
how-third-party-information-shapes-cu-strategies
using-data-to-improve-access-to-credit-for-low-income-members
designing-financial-products-for-low-income-members-using-data
measuring-and-enhancing-the-impact-of-support-programs
data-governance-why-selling-internally-is-important
selling-data-governance-in-your-credit-union
building-a-business-case-and-engaging-stakeholders
creating-a-data-governance-roadmap-and-executing-it
measuring-and-demonstrating-the-impact-of-data-governance
sustaining-momentum-keeping-data-governance-a-priority
overcoming-challenges-in-transaction-data-analysis-credit-unions
empowering-members-through-transaction-data
how-credit-unions-leverage-transaction-data-best-practices
unlocking-financial-independence-the-power-of-transaction-data
the-power-of-transaction-data-enrichment
avoid-financial-reputation-and-member-trust-issues
introduction-to-model-risk-management
week-1-mrm-a-practitioner-s-approach
week-2-guide-to-identifying-and-maintaining-models
survey-insights-navigating-mrm-in-credit-unions
week-3-application-of-mrm-insights-to-sound-model-development-eff
unlocking-the-secrets-to-attracting-gen-y-and-z
creating-a-seamless-member-experience-for-gen-y-and-gen-z
data-analytics-maturity-assessment-report
marketing-to-gen-y-and-z-strategies-that-work-for-credit-unions
the-imperative-of-engaging-millennials-and-gen-z
cu-build-lasting-relationships-with-gen-z-financial-literacy
how-social-responsibility-drives-gen-z-membership
loyalty-programs-that-work-keeping-gen-y-and-z-members-engaged
insights-on-engaging-millennials-and-gen-z-at-credit-union
ai-driven-member-experience
streamlining-operations-with-ai
innovation-and-member-inclusion-in-ai-credit-risk-models
ai-risk-management-enhancing-fraud-detection-and-cybersecurity
how-ai-is-transforming-data-analytics-for-credit-union
overcoming-ai-adoption-challenges-in-credit-unions
the-state-of-ai-in-credit-unions-survey-insights
creating-a-culture-of-innovation
building-the-foundation