Turboboost your Data-driven ML
Turboboost your Data-driven ML
Machine Learning Platform Reinvented for Real-time
Machine Learning Platform Reinvented for Real-time
Machine Learning Platform Reinvented for Real-time
Manage the complete production ML lifecycle and leverage real-time data.
Iterate faster by experimenting directly on live data.
Manage the complete production ML lifecycle and leverage real-time data.
Iterate faster by experimenting directly on live data.
TRY NOW
TRY NOW
Contact Us
Contact Us
Our Cloud Partners
Our Cloud Partners
Revolutionize Your
Revolutionize Your
Revolutionize Your
ML Workflow
ML Workflow
ML Workflow
Code 1
See what ETFs and trending at what index funds you should consider selling.
Code 2
See charts, graphs and trends to help you determine your next moves.
Code 3
Dive into the news of the ETFs world. Expert brokers and funds are given the option of writing their own articles.
TRY NOW
1
import turboml as th
import pandas as pd
transactions_df = pd.read_csv("data/transactions.csv")
labels_df = pd.read_csv("data/labels.csv")
transactions_df = transactions_df.reset_index()
labels_df = labels_df.reset_index()
transactions = tb. PandasDataset(topic_name = "transactions", dataframe = transactions_df, upload = True
labels = tb. PandasDataset(topic_name = "labels", dataframe = labels_df, upload = True)
2
import turboml as th
import pandas as pd
transactions_df = pd.read_csv("data/transactions.csv")
labels_df = pd.read_csv("data/labels.csv")
transactions_df = transactions_df.reset_index()
labels_df = labels_df.reset_index()
transactions = tb. PandasDataset(topic_name = "transactions", dataframe = transactions_df, upload = True
labels = tb. PandasDataset(topic_name = "labels", dataframe = labels_df, upload = True)
3
import turboml as th
import pandas as pd
transactions_df = pd.read_csv("data/transactions.csv")
labels_df = pd.read_csv("data/labels.csv")
transactions_df = transactions_df.reset_index()
labels_df = labels_df.reset_index()
transactions = tb. PandasDataset(topic_name = "transactions", dataframe = transactions_df, upload = True
labels = tb. PandasDataset(topic_name = "labels", dataframe = labels_df, upload = True)
Code 1
See what ETFs and trending at what index funds you should consider selling.
Code 2
See charts, graphs and trends to help you determine your next moves.
Code 3
Dive into the news of the ETFs world. Expert brokers and funds are given the option of writing their own articles.
TRY NOW
1
import turboml as th
import pandas as pd
transactions_df = pd.read_csv("data/transactions.csv")
labels_df = pd.read_csv("data/labels.csv")
transactions_df = transactions_df.reset_index()
labels_df = labels_df.reset_index()
transactions = tb. PandasDataset(topic_name = "transactions", dataframe = transactions_df, upload = True
labels = tb. PandasDataset(topic_name = "labels", dataframe = labels_df, upload = True)
2
import turboml as th
import pandas as pd
transactions_df = pd.read_csv("data/transactions.csv")
labels_df = pd.read_csv("data/labels.csv")
transactions_df = transactions_df.reset_index()
labels_df = labels_df.reset_index()
transactions = tb. PandasDataset(topic_name = "transactions", dataframe = transactions_df, upload = True
labels = tb. PandasDataset(topic_name = "labels", dataframe = labels_df, upload = True)
3
import turboml as th
import pandas as pd
transactions_df = pd.read_csv("data/transactions.csv")
labels_df = pd.read_csv("data/labels.csv")
transactions_df = transactions_df.reset_index()
labels_df = labels_df.reset_index()
transactions = tb. PandasDataset(topic_name = "transactions", dataframe = transactions_df, upload = True
labels = tb. PandasDataset(topic_name = "labels", dataframe = labels_df, upload = True)
Code 1
See what ETFs and trending at what index funds you should consider selling.
Code 2
See charts, graphs and trends to help you determine your next moves.
Code 3
Dive into the news of the ETFs world. Expert brokers and funds are given the option of writing their own articles.
TRY NOW
1
import turboml as th
import pandas as pd
transactions_df = pd.read_csv("data/transactions.csv")
labels_df = pd.read_csv("data/labels.csv")
transactions_df = transactions_df.reset_index()
labels_df = labels_df.reset_index()
transactions = tb. PandasDataset(topic_name = "transactions", dataframe = transactions_df, upload = True
labels = tb. PandasDataset(topic_name = "labels", dataframe = labels_df, upload = True)
2
import turboml as th
import pandas as pd
transactions_df = pd.read_csv("data/transactions.csv")
labels_df = pd.read_csv("data/labels.csv")
transactions_df = transactions_df.reset_index()
labels_df = labels_df.reset_index()
transactions = tb. PandasDataset(topic_name = "transactions", dataframe = transactions_df, upload = True
labels = tb. PandasDataset(topic_name = "labels", dataframe = labels_df, upload = True)
3
import turboml as th
import pandas as pd
transactions_df = pd.read_csv("data/transactions.csv")
labels_df = pd.read_csv("data/labels.csv")
transactions_df = transactions_df.reset_index()
labels_df = labels_df.reset_index()
transactions = tb. PandasDataset(topic_name = "transactions", dataframe = transactions_df, upload = True
labels = tb. PandasDataset(topic_name = "labels", dataframe = labels_df, upload = True)
TurboML at a
TurboML at a
TurboML at a
Glance
Glance
Glance
Complete ML platform
Complete ML platform
From data ingestion to post-deployment, manage all your ML needs in one place.
From data ingestion to post-deployment, manage all your ML needs in one place.
Complete ML platform
From data ingestion to post-deployment, manage all your ML needs in one place.
Built for real-time
Built for real-time
Accelerate experimentation with real-time data and continuous model updates.
Accelerate experimentation with real-time data and continuous model updates.
Built for real-time
Accelerate experimentation with real-time data and continuous model updates.
Meets you where you are
Meets you where you are
Works with your existing stack using open protocols and familiar tools.
Works with your existing stack using open protocols and familiar tools.
Meets you where you are
Works with your existing stack using open protocols and familiar tools.
Easy to use
Easy to use
Start immediately with no learning curve—familiar Python and Jupyter interface.
Start immediately with no learning curve—familiar Python and Jupyter interface.
Easy to use
Start immediately with no learning curve—familiar Python and Jupyter interface.
Deployment tailored for you
Deployment tailored for you
Deploy anywhere: fully private VPC, on-prem, or hybrid with flexible infrastructure.
Deploy anywhere: fully private VPC, on-prem, or hybrid with flexible infrastructure.
Deployment tailored for you
Deploy anywhere: fully private VPC, on-prem, or hybrid with flexible infrastructure.
Programmable all the way
Programmable all the way
Leverage custom Python scripting and streaming APIs for complete innovation.
Leverage custom Python scripting and streaming APIs for complete innovation.
Programmable all the way
Leverage custom Python scripting and streaming APIs for complete innovation.
Integrations
Integrations
Integrations
Explore our essential tools for seamless website planning and design to building.
Explore our essential tools for seamless website planning and design to building.
Why TurboML?
Why TurboML?
Why TurboML?
Explore our essential tools for seamless website planning and design. Manage and organize every subscription across teams and projects in your business.
Explore our essential tools for seamless website planning and design. Manage and organize every subscription across teams and projects in your business.
Adaptive
Make ML fast enough to increasingly shift towards on-demand compute,
Make ML fast enough to increasingly shift towards on-demand compute
Stateless
Make ML fast enough to increasingly shift towards on-demand compute,
Make ML fast enough to increasingly shift towards on-demand compute,
Build AI that is Continual
Build AI that is Continual
Build AI that is Continual
Explore our essential tools for seamless website planning and design. Manage and organize every subscription across teams and projects in your business.
Explore our essential tools for seamless website planning and design. Manage and organize every subscription across teams and projects in your business.
Cost
Revenue
ROI
Metrics
Productivity
UX
Cost
Revenue
ROI
Metrics
Productivity
UX
Cost
Revenue
ROI
Metrics
Productivity
UX
What people are saying?
What people are saying?
What people are saying?
Explore our essential tools for seamless website planning and design. Manage and organize every subscription across teams and projects in your business.
Explore our essential tools for seamless website planning and design. Manage and organize every subscription across teams and projects in your business.
Name of person
Designation
The team absolutely nailed
combining docs search and AI Q&A in a
delightful experience.
Name of person
Designation
Lorem Ipsum is simply dummy text of the printing and typesetting industry. Lorem Ipsum has been the industry's standard dummy text ever since
combining docs search and AI Q&A in a
delightful experience.
Name of person
Designation
It is absolutely amazing! Their
team has been such a pleasure to work
with, and just look at our docs - they
look gorgeous! I definitely think
everyone should try out!
Name of person
Designation
It is absolutely amazing! Their
team has been such a pleasure to work
with, and just look at our docs - they
look gorgeous! I definitely think
everyone should try out!
Name
Designation
This is a must-have if you're building
any kind of developer tools. We used
other options in the past, which feels
outdated in terms of look-n-feel when
compared to . The team helped
us set up two different websites and
even migrated all the content for us. I
can't recommend it enough.
Name
Designation
This is a must-have if you're building
any kind of developer tools. We used
other options in the past, which feels
outdated in terms of look-n-feel when
compared to . The team helped
us set up two different websites and
even migrated all the content for us. I
can't recommend it enough.
Name of person
Designation
My team loves this! Great product
and a super supportive team.
Name of person
Designation
My team loves this! Great product
and a super supportive team.
Name of person
Designation
Absolutely the best. It is freaking easy
to set up, and the team has been great
to work with!
Name of person
Designation
Absolutely the best. It is freaking easy
to set up, and the team has been great
to work with!
Name of person
Designation
Why spend a week coding your own
documentation (and optimizing it for
SEO, design, etc.) while this brings
you the best in class documentation in
a few minutes? Worked on
documentation for years, in unicorns
Name of person
Designation
Why spend a week coding your own
documentation (and optimizing it for
SEO, design, etc.) while this brings
you the best in class documentation in
a few minutes? Worked on
documentation for years, in unicorns
Frequently asked questions
Frequently asked questions
Frequently asked questions
Is TurboML meant to replace the data scientists in my team?
Absolutely not! On the contrary, our aim is to become the platform that your data scientists love, integrating it more and more into their daily workflows. In fact, all our abstractions are designed to simplify usage for data scientists: no DSLs, minimal learning curve, batch-like semantics, abstractions for all streaming operations, full interoperability with Python, and the ability to bring your own feature stores, models, algorithms, etc.
If I’ve already built an ML platform in-house, do you expect me to discard it entirely?
Absolutely not! Interoperability has been our priority from the very beginning, and is heavily incorporated in our design. All components of TurboML are loosely coupled and can be substituted by other services. For instance, if you've built an in-house solution for model predictions or feature engineering, TurboML can be deployed to continue using your implementations for those services while supplementing your solution with capabilities like real-time learning, integrated streaming MLOps, etc.
If I receive labels or feedback with delays, or not at all, is real-time learning not for me?
Absolutely not! As counter-intuitive as it might sound, instantaneous feedback is not a prerequisite for real-time learning. Consider scenarios like predicting return-to-origin (RTO) for e-commerce or chargebacks for credit cards. In both cases, obtaining labels for individual data points can take days to weeks. However, that doesn’t mean we must wait weeks before updating our models. Real-time learning is a continuous process. Even today, we can leverage new information about past predictions to figure out which models were more accurate under what circumstances and further enhance performance.
Is TurboML meant to replace the data scientists in my team?
If I’ve already built an ML platform in-house, do you expect me to discard it entirely?
If I receive labels or feedback with delays, or not at all, is real-time learning not for me?
Is TurboML meant to replace the data scientists in my team?
Absolutely not! On the contrary, our aim is to become the platform that your data scientists love, integrating it more and more into their daily workflows. In fact, all our abstractions are designed to simplify usage for data scientists: no DSLs, minimal learning curve, batch-like semantics, abstractions for all streaming operations, full interoperability with Python, and the ability to bring your own feature stores, models, algorithms, etc.
If I’ve already built an ML platform in-house, do you expect me to discard it entirely?
Absolutely not! Interoperability has been our priority from the very beginning, and is heavily incorporated in our design. All components of TurboML are loosely coupled and can be substituted by other services. For instance, if you've built an in-house solution for model predictions or feature engineering, TurboML can be deployed to continue using your implementations for those services while supplementing your solution with capabilities like real-time learning, integrated streaming MLOps, etc.
If I receive labels or feedback with delays, or not at all, is real-time learning not for me?
Absolutely not! As counter-intuitive as it might sound, instantaneous feedback is not a prerequisite for real-time learning. Consider scenarios like predicting return-to-origin (RTO) for e-commerce or chargebacks for credit cards. In both cases, obtaining labels for individual data points can take days to weeks. However, that doesn’t mean we must wait weeks before updating our models. Real-time learning is a continuous process. Even today, we can leverage new information about past predictions to figure out which models were more accurate under what circumstances and further enhance performance.
Sign up for the early access
Sign up for the early access
Sign up for the early access
Sign in to get started with a Playground instance
Sign in to get started with a Playground instance
Sign in to get started with a Playground instance
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Real-time Machine Learning Platform
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