Believe it or not, it’s not just one specific problem that pops up time and time again. There are various points of AI project failure across both the business and technical teams.
According to global IDC data, what
percentage of AI projects failed in 2021?
Why Are Your AI
Projects Failing?
LEARN MORE ABOUT AI FAILURE
10%
28%
46%
87%
THE HARD TRUTH:
Click on the buttons below to explore each step of the AI project lifecycle.
Technical Reasons Behind AI Project Failure
Choose from five, easy-to-digest flipbooks to address the common organizational project failure points that resonate most with you. From choosing the right use cases and building trustworthy AI systems to finding ways to create more value from analytics and AI, get concrete solutions to implement today.
Organizational Reasons Behind AI Project Failure
Data
Discovery
PREPARE
Data
PREPARATION
PREPARE
Model Description
PREPARE
Model
Development
BUILD
Model Readiness
BUILD
Model
Sign Off
DEPLOY
Model Deployment
DEPLOY
Model Consumption
DEPLOY
Model
Monitoring
MONITOR
Continuous Improvement
MONITOR
Tackle the Right AI Projects for the Best ROI
Discover how to strategically choose the analytics and AI use cases that have both high business value and a high likelihood of success.
The Problems
People struggle to help the team rely on better data (data engineer)
Teams need to qualify content and expectations (business SME)
People struggle to help the team rely on better data (data engineer)
Teams need to qualify content and expectations (business SME)
The Problems
Models are not robust, secured, or approved (IT architect)
Models are not safe or compliant to policies (risk manager)
The Problems
Models are not robust, secured, or approved (IT architect)
Models are not safe or compliant to policies (risk manager)
The Problems
Implementation
The Problems
Models are not easy to deploy (IT Ops)
It takes too much time to put the model into users’ hands (business analyst)
Models are not easy to deploy (IT Ops)
It takes too much time to put the model into users’ hands (business analyst)
The Problems
Models are not easy to deploy (IT Ops)
It takes too much time to put the model into users’ hands (business analyst)
The Problems
Models no longer fit the company’s requirements (risk manager)
Teams can’t easily monitor models and check for improvements (IT Ops)
The Problems
Models no longer fit the company’s requirements (risk manager)
Teams can’t easily monitor models and check for improvements (IT Ops)
The Problems
SEE THE SOLUTIONS
People struggle to help the team rely on better data (data engineer)
Teams need to qualify content and expectations (business SME)
The Problems
10%
28%
46%
87%
SEE THE SOLUTIONS
SEE THE SOLUTIONS
SEE THE SOLUTIONS
SEE THE SOLUTIONS
SEE THE SOLUTIONS
SEE THE SOLUTIONS
SEE THE SOLUTIONS
SEE THE SOLUTIONS
SEE THE SOLUTIONS
PREPARE WITH DATAIKU
With data preparation in Dataiku, teams can connect, cleanse, and prepare data for analytics and machine learning projects at scale. To navigate the challenges outlined above, they can leverage:
Data preparation and enrichment via easy-to-use visual interfaces (i.e., join, group, aggregate, clean, transform
— all with a few clicks)
100 built-in data transformers for common data manipulations like binning, concatenation and strings manipulation, currency and date conversions, geo-enrichment, and reshaping
Specialized data preparation like geospatial data,
time series, images, and more
i
Structured sign-off and approvals ensure audit readiness on deployment decisions and deployments will be
blocked until proper approval is obtained
The model registry provides a centralized way to see all models in one place, versioned, and with performance metrics and project summaries for stakeholders (and
the bundle registry does the same for project bundles)
Stakeholders can leverage a project value and risk qualification framework to help compare initiatives, determine oversight requirements, and determine which projects should be prioritized for investment
BUILD WITH DATAIKU
Teams need to be able to safely scale with oversight and prioritize the data projects and models that deliver the most value. How can they avoid the problems outlined above by the IT architect and risk manager?
DEPLOY WITH DATAIKU
Also, data experts can create interactive projects dashboards to share with business users. They can include elements such as filterable charts and datasets, ML model insights, project health metrics, embedded web apps,
and much more.
MONITOR WITH DATAIKU
With model retraining in Dataiku, teams can either manually refactor a model or set up automated retraining based on a schedule or specific triggers, such as significant data or performance drift.
With comprehensive model comparisons, data scientists and ML operators can perform champion/challenger analysis on candidate models to inform their decisions about the best model to deploy in production.
Model
Development
BUILD
Model
Development
BUILD
Data
PREPARATION
PREPARE
Model
Readiness
BUILD
Data
Discovery
PREPARE
Model
Sign Off
deploy
Continuous Improvement
monitor
Model
Deployment
Deploy
Continuous Improvement
monitor
Avoid AI Project Failure
Believe it or not, it’s not just one specific problem that pops up time and time again.
Tackle the Best Projects for the Best ROI
Believe it or not, it’s not just one specific problem that pops up time and time again.
Tackle the Best Projects for the Best ROI
Believe it or not, it’s not just one specific problem that pops up time and time again.
Title of the ebook here somethimes the title is very long but that’s ok
Believe it or not, it’s not just one specific problem that pops up time and time again.
Model
Development
SEE THE SOLUTIONS
People struggle to help the team rely
on better data (data engineer)
Teams need to qualify content and expectations (business SME)
The Problems
A visual flow that provides an easy-to-digest representation of a project’s data pipeline and is the central space where data and domain experts view and analyze data, add recipes to join and transform datasets, and build predictive models
Pre-built connectors to dozens of leading data sources both on-premises and in the cloud
Data preparation and enrichment via easy-to-use visual interfaces (i.e., join, group, aggregate, clean, transform — all with a few clicks)
100 built-in data transformers for common data manipulations like binning, concatenation and strings manipulation, currency and date conversions, geo-enrichment, and reshaping
Specialized data preparation like geospatial data, time series, images, and more
PREPARE WITH DATAIKU
With data preparation in Dataiku, teams can connect, cleanse, and prepare data for analytics and machine learning projects at scale.
To navigate the challenges outlined above, they can leverage:
SEE THE SOLUTIONS
The Problems
People struggle to help the team rely
on better data (data engineer)
Teams need to qualify content and expectations (business SME)
SEE THE SOLUTIONS
The Problems
People struggle to help the team rely
on better data (data engineer)
Teams need to qualify content and expectations (business SME)
SEE THE SOLUTIONS
Models are not robust, secured, or approved (IT architect)
Models are not safe or compliant to policies (risk manager)
The Problems
SEE THE SOLUTIONS
Models are not easy to deploy (IT Ops)
It takes too much time to put the model into users’ hands (business analyst)
The Problems
SEE THE SOLUTIONS
Models are not easy to deploy (IT Ops)
It takes too much time to put the model into users’ hands (business analyst)
The Problems
SEE THE SOLUTIONS
Models are not easy to deploy (IT Ops)
It takes too much time to put the model into users’ hands (business analyst)
The Problems
SEE THE SOLUTIONS
Models no longer fit the company’s requirements (risk manager)
Teams can’t easily monitor models
and check for improvements (IT Ops)
The Problems
SEE THE SOLUTIONS
Models no longer fit the company’s requirements (risk manager)
Teams can’t easily monitor models
and check for improvements (IT Ops)
The Problems
DEPLOY WITH DATAIKU
When it comes to deploying projects to production, operators have a central
place to manage versions of Dataiku projects and API deployments across their individual life cycles.
Teams can manage code environment and infrastructure dependencies for both batch and real time scoring, and deploy bundles and API services across dev, test, and prod environments for a robust approach to updates.
Also, data experts can create interactive projects dashboards to share with business users. They can include elements such as filterable charts and datasets, ML model insights, project health metrics, embedded web apps, and much more.
MONITOR WITH DATAIKU
Once a project is up and running in production, Dataiku monitors the pipeline to ensure all processes execute as planned and alerts operators if there are issues.
Model evaluation stores capture and visualize performance metrics to ensure that live models continue to deliver high-quality results over time. If it does degrade, built-in drift analysis helps operators detect and investigate potential data, performance, or prediction drift to
inform next steps.
With model retraining in Dataiku, teams can either manually refactor a model or set up automated retraining based on
a schedule or specific triggers, such as significant data or performance drift.
With comprehensive model comparisons, data scientists and ML operators can perform champion/challenger analysis on candidate models to inform their decisions about the best model
to deploy in production.
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In order to achieve Everyday AI and avoid overall AI project failure, data
and analytics executives along with industry CEOs need to strike the balance between quick, high-impact AI wins and long-term, cultural AI transformation.
Balance AI Quick Wins and Long-Term AI Transformation
Ensure frontline user adoption of data, models, and apps with trustworthy AI best practices, shared via real-world examples.
How to Build Trustworthy AI Systems
Uncover seven practical ways that data and analytics executives and team managers can ensure scalable and repeatable business value from its analytics and AI projects.
7 Concrete Ways to Create More Value From Analytics & AI
Many organizations don't have a process in place to measure their AI maturity. Discover concrete ways to do so, along with tips to reduce costs associated with analytics and AI projects.
5 Proven Tips to Improve Your AI Maturity
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•
•
A visual flow that provides an easy-to-digest representation of a project’s data pipeline and is the central space where data and domain experts view and analyze data, add recipes to join and transform datasets, and build predictive models
Pre-built connectors to dozens of leading data sources both on-premises and in the cloud
•
•
•
•
•
With Dataiku Govern, teams have a central control tower where stakeholders can track the progress of multiple
data initiatives and ensure the right workflows and processes are in place to ensure Responsible AI
Standardized governance plans and workflows enable clear steps and gates to explore, build, test, deploy, and maintain AI projects and, in turn, ensure the end-to-end process is documented and tracked
•
•
•
Once a project is up and running in production, Dataiku monitors the pipeline to ensure all processes execute as planned and alerts operators if there are issues.
Model evaluation stores capture and visualize performance metrics to ensure that live models continue to deliver high-quality results over time. If it does degrade, built-in drift analysis helps operators detect and investigate potential data, performance, or prediction drift to inform next steps.
In this section, we’ll shed light on the most common failure points we hear from data, analytics, and IT leaders at each stage of the AI project lifecycle — prepare, build, deploy, and monitor. Then, we’ll show you how you can quickly address those issues with Dataiku in order to make the entire process unified, operationalized, and repeatable.
In this section, we’ll shed light on the most common failure points we hear from data, analytics, and IT leaders at each stage of the AI project lifecycle — prepare, build, deploy, and monitor. Then, we’ll show you how you can quickly address those issues with Dataiku in order to make the entire process unified, operationalized, and repeatable.
Click on the buttons below to explore each step of the AI project lifecycle.
When it comes to deploying projects to production, operators have a central place to manage versions of Dataiku projects and API deployments across their individual life cycles.
Teams can manage code environment and infrastructure dependencies for both batch and real time scoring,
and deploy bundles and API services across dev, test,
and prod environments for a robust approach to updates.
DOWNLOAD THE EBOOK
DOWNLOAD THE EBOOK
DOWNLOAD THE EBOOK
DOWNLOAD THE EBOOK
DOWNLOAD THE EBOOK
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