[HN Gopher] Launch HN: Datrics (YC W21) - No-Code Analytics and ...
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Launch HN: Datrics (YC W21) - No-Code Analytics and ML for FinTech
Hey everyone, we're Anton (avais), Kirill (Datkiri), and Volodymyr
(vsofi), the founders of Datrics (https://datrics.ai). We help
FinTech companies build and deploy machine learning models without
writing code. We provide a visual tool to work with structured
data by constructing a diagram of data manipulations from lego-like
bricks, and then execute it all on a backend. This lets our users
accomplish tasks that usually need a team of software engineers,
data scientists, and DevOps. For instance, one of our customers is
a consumer lending company that developed a new risk model using
just our drag-and-drop interface. I used to lead a large data
science consultancy team, being responsible for Financial Services
(and Risks specifically). Our teams' projects included end-to-end
risk modeling, demand forecasting, and inventory management
optimization, mostly requiring combined efforts from different
technical teams and business units to be implemented. It usually
took months of work to turn an idea into a complete solution, going
through data snapshot gathering to cleansing to experimenting to
working with engineering and DevOps teams to turn experiments in
Jupyter notebooks into a complete application that worked in
production. Moreover, even if the application and logic behind the
scenes were really simple (could be just dozens or hundreds of
lines of code for a core part), the process to bring this to end-
users could take ages. We started thinking about possible
solutions when a request from one of the Tier 1 banks appeared,
which confirmed that we're not alone in this vision: their problem
was giving their "citizen data scientists" and "citizen developers"
power to do data-driven work. In other words, work with the data
and generate insights useful for business. That was the first time
I'd heard the term "citizen data scientist". Our users are now
these citizen data scientists and developers, whom we're giving the
possibility to manipulate data, build apps, pipelines, and ML
models with just nominal IT support. Datrics is designed not only
to do ML without coding, but to give analysts and domain experts a
drag and drop interface to perform queries, generate reports, and
do forecasting in a visual way with nominal IT support. One of our
core use cases is doing better credit risk modeling - create
application scorecards based on ML or apply rule-based
transactional fraud detection. For this use-case, we've developed
intelligent bricks that allow you to do variables binning and
scorecards in a visual way. Other use cases include reports and
pivot tables on aggregating sales data from different countries in
different formats or doing inventory optimization by forecasting
the demand without knowing any programming language. We're
providing 50+ bricks to construct ETL pipelines and build models.
There are some limitations - a finite number of pre-built building
blocks that can be included in your app, but if there is no block
that you need, you can easily build your own
(https://youtu.be/BQNFcZWwUC8). Datrics is initially cloud-native,
but also can be installed on-prem for those customers who have
corresponding security policy or setups. The underlying technology,
the pipeline execution engine is rather complex and currently built
on top of Dask, which gives Python scalability for big datasets. In
the next release, we are going to support Pandas as well as to
switch intelligently between small datasets for rapid prototyping
and big datasets for pipeline deployments. We're charging only for
private deployments, so our web version is free:
https://platform.app.datrics.ai/signup. Try it to create your
analytical applications with a machine learning component! We've
put together a wiki (https://wiki.datrics.ai) to cover the major
functionality, We are super-excited to hear your thoughts and
feedback! We're big believers in the power of Machine Learning and
self-service analytics and are happy to discuss what you think of
no-code approaches for doing ML and analytics generally as well as
the availability of them for non-data scientists. Or anything you
want to share in this space!
Author : avais
Score : 37 points
Date : 2021-02-18 08:12 UTC (14 hours ago)
| mritchie712 wrote:
| What makes this specific to fintech vs. general ML tools? I led
| risk at a fintech and it's unclear how this is better than
| generalized solutions.
| avais wrote:
| We're building templates (pre-built pipelines) for particular
| use cases, so that it's easy to start. We have one for credit
| risk specifically! In addition, some functionality tailored for
| Risks: binning of the variables based on information value,
| good/bad with WoE stats, building a scorecard based on odds of
| min-max scaler directly from LogReg Model. Does it make sense?
| mritchie712 wrote:
| Do you have any integrations with platforms that a fintech
| might be using (e.g. TSYS)?
| avais wrote:
| Not yet. I think integrations is our weak part at the
| moment (we support just csvs, json, SQL DBs - MySQL, MSSQL,
| PostgreSQL) and building custom integrations based on
| customers needs. We're going to extend the list of standard
| connectors in the next few months, under the discussion
| with our clients and prospects on priorities.
| pplonski86 wrote:
| Congratulations on launch! ML tools/services/platforms are really
| hard to build. You need to juggle not only frontend/backend
| frameworks but also make them work with ML frameworks. There are
| so many corner-cases that can make the whole app crash.
|
| How does it differ from open-source AutoML frameworks like
| https://github.com/mljar/mljar-supervised or drag-and-drop tools
| like Azure ML Studio?
|
| Is the no-writing the code a killer feature here?
|
| Do you have financial data enhancement feature? Do you plan such
| feature?
| avais wrote:
| yeaaah - we've experienced corner cases problem a lot. We're
| covering the functionality with unit tests and based on mock
| data, but still validating many things manually and sometimes
| need to do hot-fixes :) Though, the good side of this is that
| the users don't need to this testing on their end once building
| on top of datrics, so we (hopefully) save some time here.
| avais wrote:
| Re killer feature: I believe it's being end-to-end, provide
| experience for "citizen x", domain experts, rather than only ML
| engineers: so it's no-code data cleansing, transformation + ML
| layer on top of this + productionalization, and being cloud-
| agnostic. We found that the problem that we're trying to solve
| is bigger than just giving the possibility to use ML
| instruments for non-developers. There are a lot of citizen data
| scientists who want to automate their spreadsheet work, make
| inference easy once the model is created without too much
| efforts to change processing pipeline, etc. That's why we don't
| compete with Azure ML Studio as they more developers-focused
| and with AutoML tools as ML is only a feature of platform.
|
| In addition, we've designed and implemented own pipeline
| scheduling algorithm and APIs out-of-the-box, so ready
| pipelines or models could be easily embedded into business
| process or used outside of the platform.
| avais wrote:
| Right now there is a possibility to integrate third-party data
| with custom code + API calls, but I believe we need to work on
| this extensively to provide easier way for this. So happy to
| learn if you have some specific sources in mind?
| claytonjy wrote:
| Because financial data generally means time-series,
| autocorrelations abound, and it becomes very easy to develop a
| model that underperforms naive baselines (e.g. LOCF, arima) or
| peeks into the future through improper cross validation or
| feature engineering.
|
| If the customer gives you a column that peeks into the future
| (e.g. "quarterly sales" when each row is a sale in that quarter),
| you'll build a model that looks great on metrics and to the
| customer, and might take months for the customer to realize was
| practically useless. Are you able to reliably prevent these kinds
| of issues at a technical level, or do you lean towards customer
| education ("don't give us quarterly sales") instead?
| avais wrote:
| Great question, and we've seen this happening actually, when
| some payments-based features used in scoring, data leakage. We
| are not doing "hard" constraints on this, but there are 2 ways
| for the user to identify this: first one is feature importance
| built automatically, so the user will see these features
| immediately second one is the process of building inference.
| The same data prep/transformation pipeline assumed to be used
| as API or for batch processing before model application, and at
| this point they will definitely realise that they just don't
| have these features yet for new customers!
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