YOU® MLOps Platform

Lets build an MLOPS Platform tailored to YOU!

  • Fully customized to your needs
  • Integrated with your infrastructure
  • ANY cloud / hybrid / on premis
  • No vendor lock
  • Fast time to market

So you can focus on business and gain a competitive advantage.

Our clients & their benefits

Truecaller
In Truecaller we elevated the existing Jupyter-based data exploration pipeline to Kubeflow-based platform, <b>allowing not only quick experiments with the datain isolated environments but also full-scale ML operations with all the benefits of ML pipelines and Model Registry. It helped to starndardize the way how Data Scientists work with ML</b>
READ TRUECALLER CASE STUDY
Willa
Easier and more stable machine models development and deployment allowed Willapay to <b>improve many aspects of the product from decreasing the risk of unpaid invoices to faster partner reviewal.</b>
LISTEN PODCAST WITH WILLA CASE
PKO BP
We had different technology stacks between business and IT. All models needed to be rewritten and deployed in different technology. The solution was OUⓇ MLOps Platform that we built with GetInData. That was a big breakthrough. Now we have a common language within the company, and we hid the complexity of machine learning into a framework.
SEE PKO BP CASE
Adtech
In this case, the customer needed a classification solution for videos trending in a streaming app with a global reach. We designed a new solution according to the event-driven architecture paradigm. Its implementation <b>allowed the customer to radically reduce processing costs and roll out new models</b> to production without interfering with system that was continuously consuming from message queues (blue/green deployment).

Machine Learning Challenges

We’re stuck at a dead-end with MLOps, we have a team of analysts with low technical skills supplemented by engineers who don't get how ML works.

That is how one of our clients described why they need to build THEI® MLOps Platform with us.

Project fails

High rate of ML project that are not delivered

Time, cost and resource consuming

Even when companies take an ML project to production, it requires a lot of time and resources.

Wasted business potential

According to the Gartner report, by 2025 the 10% of enterprises that establish AI engineering best practices will generate at least three times more value from their AI efforts than the 90% of enterprises that do not.

Need help in dealing with these challenges?

See how our YOUⓇ MLOps Platform solves them!

schedule a free consultation

What is Machine Learning Operations (MLOps)?

The MLOps role is to diagnose the use of ML on production and optimize the process to maximum efficiency, by improving or creating a new personalized system that works well in real-life scenarios. In this way the company can reach its full potential in the market by being able to make accurate, data-driven decisions and define trends simultaneously.

MLOps is a set of best practices which are the solution to ML challenges. This practice aims to deploy and maintain Machine Learning Models in production reliably and efficiently.

Want to know more about MLOps?

What is the YOU® MLOps Platform?

The MLOPS Platform is a framework to implement the best practices of Machine Learning Operations (MLOps)  and make the process of Machine Learning experimentation, model training and model serving efficient, secure and reliable.

It's not a product available from tomorrow that you have to wrestle with and configure to suit you. It's a personalized solution tailored to your need and to your company's existing processes, solutions and technologies. The product is the final result of researching your needs, use cases, technical assessment and joint work of the combined team on the solution, until a fully customized and optimized platform is created that you can handle on your own.

We build a custom best-of-breed solution instead of a one suits all approach and support the top cloud providers (Amazon Web Services, Microsoft Azure and Google Cloud Platform).

“Under the hood, our MLOps platform combines all the disciplines required to take your Machine Learning out of the labs and continuously deploys to production on a large scale.”

Krzysztof Zarzycki
CTO at GetInData

How does the YOUⓇ MLOps Platform work?

A typical Machine Learning project has 3 phases:

Experimentation - where the data science teams do their research and look for the most suitable method of solving a problem

Training - where they structure the model training process into a pipeline

Serving - when the model is ready for production deployment

1
Experimentation

Data Scientists first need to get familiar with the available data, discover potential features and then build the prototype solution. This step is usually unstructured and requires a lot of experimentation. The key feature of the MLOps platform here is to provide the environment for prototyping and exploration, which allows faster development and enables a smooth transition to the production environment   (i.e. by using version control, containers and Kedro as an MLOps framework).

2
Training

In the Training phase, we build automation for the Continuous Integration/Continuous Deployment process, which outputs the containerized Machine Learning Pipeline. Next, we automate the Continuous Training process to execute the pipeline on the new data when it’s available. Continuous Training produces the Machine Learning model, which we register on the Model Registry, along with the model performance metrics and the other artifacts. This allows us to track model performance over time and decide which one we want to deploy for serving.

3
Serving

In this step, typically we deploy the model to the production environment in order to be able to use it in the application and serve predictions for the new data either online or offline.   Additionally, we set the model monitoring here to measure model performance   over time, and trigger model re-training when necessary.

Our MLOps Principles

What makes our approach different?

A software Engineering-like process but for ML models
Freedom of choice of tools
The pipeline is the result, not the model
Loosely coupled mix of cloud services and open-source solutions
No IT required for Data Science on production
Best-of-breed instead of one size fits all approach

The YOUⓇ MLOps Platform is based on tools you already know and use, so changes for your team and cost are minimized

As a result, a loosely coupled system of components that can be independently replaced is created.

Sign up for a Platform DEMO

MLOps Benefits

Democratization of Machine Learning within the company

One of our clients has opened the door to a 5 times bigger group of data analysts

Faster time to market Repeatable

A trustworthy process makes it easy for the data science team to experiment in real-time with their models, which leads to an increase in the scale of AI effort

Flexibility and scalability

You can focus on the value delivered by models rather than debugging code on different platforms

Built-in security

The quality gates allow you to keep control over what gets exposed to the end-users

Cost reduction & cost control



Automation



Benefits for Data Science Teams

  • Reliable experiment tracking
  • Easy path to the cloud
  • Faster time to production
  • Model management
schedule a free MLOPs consultation

*We work with clients all over the world in distributed teams, but if we worked together in one location we could look just like this according to AI (generated by AI based on our images).

How can you build MLOps solutions with us?

We have a unique way of working with clients that allows us to build deep trust-based partnerships. This is based on a few powerful and pragmatic principles, tested and refined over many years of our consulting and project delivery experience. In the MLOps area we work in a combined team (your experts and ours) on a custom best-of-breed solution to implement an MLOps platform based on tools which are already being used and familiar to your company. This is how we minimize input to maximize the output of the process.

As a result we leave you with

  • A deployed, optimized MLOps Platform that you are able to manage on your own

  • Full documentation of the solution

  • Onboarded and trained users (knowledge transfer)

  • Readiness to get the full potential out of Machine Learning to achieve business values in your market

  • YOU® MLOps Platform - deployed and functioning independently

Your use case
Technical assess&shy;ment
Solutions proposal
Shared Teams
Handover
Extensions
Production-&shy;grade solution
Discovery phase
YOU® MLOps Platform works on its own
Your business potential is unleashed

This is what implementation of our MLOps solution looks like on a timeline

Est. project duration: 6 MONTHS
PHASE0
Scope
Est. duration:1 MONTH

We scope the proof-of-concept project (POC) with an example use case, gather functional requirements and prepare the environment

  • Requirements gathering (workshops)
  • Current infrastructure / product / dependencies overview
  • Plan the process of building the MLOPS Platform
  • Access gathering / sandbox environment setup
  • Demo of the MLOPS Platform
  • Design the MLOPS Platform architecture for POC
Deliverables
  • Project requirements
  • Implementation plan
  • MLOps Platform demo
  • MLOps Platform architecture (POC)
PHASE1
Implementation
Est. duration:4 MONTHS

We implement the proof-of-concept (POC) project.

  • Start implementation of the POC
  • Propose and confirm the technologies to implement the MLOps Platform Components
  • Implement the POC project with the client’s checks
  • Demo of the POC project
Deliverables
  • MLOps Platform architecture (POC) with technologies
  • MLOps Platform POC implementation
  • Demo of POC + review
PHASE2
Onboarding
Est. duration:1 MONTH

We get the client ready to use and maintain THEI® MLOps Platform

  • Prepare the documentation along with the final architecture design
  • Train / onboard new MLOps Platform users
  • Hand-off process
Deliverables
  • Documentation
  • Onboarding workshops
 

Need help in dealing with these challenges?

See how our YOUⓇ MLOps Platform solves them!

schedule a free consultation

See our MLOps videos

More about MLOps

16 November 2021
Bartosz Chodnicki

Online ML Model serving using MLeap

Training ML models and using them in online prediction on production is not an easy task. Fortunately, there are more and more tools and libs that can help us to accomplish this challenge.  In online…
READ MORE
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A Review of the Presentations at the DataMass Gdańsk Summit 2023

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12 October 2023
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Kedro Dynamic Pipelines

“How can I generate Kedro pipelines dynamically?” - is one of the most commonly asked questions on Kedro Slack. I’m a member of Kedro’s Technical Steering Committee and I see this question popping up…
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8 September 2023
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Data Journey with Yetunde Dada & Ivan Danov (QuantumBlack) – Kedro (an open-source MLOps framework) – introduction, benefits, use-cases, data & insights used for its development

In this episode of the RadioData Podcast, Adam Kawa talks with Yetunde Dada & Ivan Danov  about QuantumBlack, Kedro, trends in the MLOps landscape e.g. so many MLOps tools and LLMOPs. We encourage you…
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22 June 2023
Michał Bryś, Marcin Zabłocki, Marek Wiewiórka

From 0 to MLOps with ❄️ Part 2: Architecting the cloud-agnostic MLOps Platform for Snowflake Data Cloud

From 0 to MLOps with Snowflake ❄️ In the first part of the blogpost, we presented our kedro-snowflake plugin that enables you to run your Kedro pipelines on the Snowflake Data Cloud in 3 simple steps…
READ MORE
17 May 2023
Marcin Zabłocki, Marek Wiewiórka, Michał Bryś

From 0 to MLOps with ❄️ Snowflake Data Cloud in 3 steps with the Kedro-Snowflake plugin

MLOps on Snowflake Data Cloud MLOps is an ever-evolving field, and with the selection of managed and cloud-native machine learning services expanding by the day, it can be challenging to navigate the…
READ MORE
4 May 2023
Jakub Jurczak

The 7 Most Popular Feature Stores In 2023

Feature Stores are becoming increasingly popular tools in the machine learning environment, serving to manage and share the features needed to build machine learning models. By centralizing and…
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1 February 2023
Szymon Żaczek

Deploy your own Databricks Feature Store on Azure using Terraform

A tutorial on how to deploy one of the key pieces of the MLOps-enabling modern data platform: the Feature Store on Azure Databricks with Terraform as IaC.  Machine Learning Operations (MLOps) — that’s…
READ MORE
19 January 2023
Jakub Jurczak

Cloud data warehouses: Snowflake vs BigQuery. What are the differences between the pricing models?

Companies planning to process data in the cloud face the difficulty of choosing the right data warehouse. Choosing the right solution is one of the most important decisions at the early stage of a…
READ MORE
5 January 2023
Katarzyna Kusznierczuk

GetInData in 2022 - achievements and challenges in Big Data world

Time flies extremely fast and we are ready to summarize our achievements in 2022. Last year we continued our previous knowledge-sharing actions and launched new ones. Let’s not waste the time, dive…
READ MORE
23 November 2022
Marcin Zabłocki

Deep Learning with Azure: PyTorch distributed training done right in Kedro

At GetInData we use the Kedro framework as the core building block of our MLOps solutions as it structures ML projects well, providing great abstraction for nodes, pipelines, datasets and…
READ MORE
27 October 2022
Jakub Jurczak, Sylwia Kołpuć

eBook: Power Up Machine Learning Process. Build Feature Stores Faster - an Introduction to Vertex AI, Snowflake and dbt Cloud

Recently we published the first ebook in the area of MLOps: "Power Up Machine Learning Process. Build Feature Stores Faster - an Introduction to Vertex AI, Snowflake and dbt Cloud". In this short…
READ MORE
25 October 2022
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A Review of the Presentations at the DataMass Gdańsk Summit 2022

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19 September 2022
Michał Bryś

Automated Machine Learning (AutoML) with BigQuery ML. Start Machine Learning easily and validate if ML is worth investing in or not.

Machine learning is becoming increasingly popular in many industries, from finance to marketing to healthcare. But let's face it, that doesn't mean ML will necessarily be a viable solution for every…
READ MORE
23 August 2022
Klaudia Wachnio

How do we apply knowledge sharing in our teams? GetInData Guilds

Do you remember our blog post about our internal initiatives such as Lunch & Learn and internal training? If yes, that’s great! If you didn’t get the chance to read it before, please check: How do we…
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26 July 2022
Michał Talaśka

Power of Big Data: MLOps for business.

Welcome to the next instalment of the “Power of Big Data” series. The entire series aims to make readers aware of how much Big Data is needed and how popular it is becoming in the modern world. In an…
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6 June 2022
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Feature Store comparison: 4 Feature Stores - explained and compared

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READ MORE
17 May 2022
Jakub Jurczak

MLOps: 5 Machine Learning problems resulting in ineffective use of data

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30 March 2022
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One of the core features of an MLOps platform is the capability of tracking and recording experiments, which can then be shared and compared. It also involves storing and managing machine learning…
READ MORE
1 March 2022
Michał Talaśka

Power of Big Data: Science

Welcome to the next installment of the "Big Data for Business" series, in which we deal with the growing popularity of Big Data solutions in various branches of business.  This entire series aims to…
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1 February 2022
Marcin Zabłocki

Deploying serverless MLFlow on Google Cloud Platform using Cloud Run

At GetInData, we build elastic MLOps platforms to fit our customer’s needs. One of the key functionalities of the MLOps platform is the ability to track experiments and manage the trained models in…
READ MORE
18 January 2022
Klaudia Wachnio

GetInData in 2021 - let’s celebrate our achievements in the Big Data world!

The year 2021 passed in the blink of an eye and the time has come to summarize our goals at GetinData and define our challenges for the next year. Today, we would like to share our achievements with…
READ MORE
16 November 2021
Bartosz Chodnicki

Online ML Model serving using MLeap

Training ML models and using them in online prediction on production is not an easy task. Fortunately, there are more and more tools and libs that can help us to accomplish this challenge.  In online…
READ MORE
30 October 2023
Sylwia Kołpuć, Piotr Chaberski, Radosław Szmit, Mariusz Wojakowski

A Review of the Presentations at the DataMass Gdańsk Summit 2023

The Data Mass Gdańsk Summit is behind us. So, the time has come to review and summarize the 2023 edition. In this blog post, we will give you a review and key takeaways from selected topics presented…
READ MORE

Our opensource plugins

We are proud that our experts are the contributors to the most important technologies. We also develop our open source solutions like plugins for Kedro so you could run it... everywhere.

Kedro Kubeflow

Get plugin on GitHub

Kedro VertexAI

Get plugin on GitHub

Kedro Airflow k8s

Get plugin on GitHub

We are proud partners of:

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