Power Up Machine Learning Process
Get Your free copy of the eBook:
Build Feature Stores Faster
an Introduction to Vertex AI, Snowflake and dbt Cloud
How to eliminate the risk of the ineffective use of data in Machine Learning
What’s inside:
How to reach the full potential of data-driven decision-making in real-time
A step-by-step guide to designing and building a Feature Store
What MLOps is and the MLOps Platform
+ BONUS: MLOps Video Workshop:
Building Machine Learning pipelines with Kedro and
Vertex AI on Google Cloud Platform
Building Machine Learning pipelines with Kedro and
Vertex AI on Google Cloud Platform
Discover the need for Machine Learning
Download the eBook
& Workshop access
& Workshop access
Power Up the ML Process and be ready for data-driven decision making in real-time
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Build a data effective Feature Store
You are already a step ahead in the Machine Learning race.
Now, you will get to stage 4 simply by applying these eBook guides.
Now, you will get to stage 4 simply by applying these eBook guides.
The administrator of your personal data is GetInData Poland Sp. z o.o. with its registered seat in Warsaw (02-508), 39/20 Pulawska St. Your data is processed for the purpose of provision of electronic services in accordance with the Terms & Conditions. For more information on personal data processing and your rights please see Privacy Policy.
By submitting this form, you agree to our Terms & Conditions and Privacy Policy
By submitting this form, you agree to our Terms & Conditions and Privacy Policy
BONUS: Video Workshop
Two practical exercises to help you build the ML pipeline yourself in an hour
Building Machine Learning pipelines with Kedro and Vertex AI on Google Cloud Platform
Why do we need a pipeline for Machine Learning models? Introduction to MLOps
Kedro, an open-source Python framework for creating reproducible, maintainable and modular data science code
With Michał Bryś - Machine Learning Engineer at GetInData
- MLOps for business introduction - what MLOps is, and what impact its implementation may have on business
1 BIZ - MLOps for Business
- 5 Machine Learning problems resulting in the ineffective use of data
- Why and how MLOps is essential to boost Machine Learning?
- What is the MLOps Platform and do you need it?
- How a well-functioning, optimized Feature Store can eliminate all these risks
- What is a Feature Store?
2 TECH - the technical perspective
- Comparison of the most popular Feature Stores - tips on how to choose the right Feature Store for your project
- How to build a Feature Store quickly and efficiently?
- A step by step guide on building a Feature Store
Data Engineer
Data Scientist
Principal Architect
Business owner
Business development specialist
Digital Transformation Leader
CEO / CTO / COO / CIO
Who will benefit from this e-book?
Thanks to easy navigation between these two areas and segmentation to different levels of technical initiation, this eBook will be useful if you are:
From short and specific explanations of technical issues necessary in a business context
to advanced technical content necessary to implement the eBook knowledge.
Download MLOps: Feature Store eBook now
Why do you need this eBook?
You will gain an insight into the advantages and consequences of introducing Machine Learning. More and more companies in almost every possible sector: healthcare, eCommerce, finance, advertising, telecommunication and more are implementing Machine Learning.
- Why are they doing it?
- Does it make sense in the face of so much competition?
- What is the condition for the effectiveness of ML entry?
You will learn about the areas that are at risk of inefficient use of collected data. We present 5 common Machine Learning problems that cause inefficiency. In this eBook, we address each of them with a solution.
You will gain insight into Machine Learning - MLOps correlations. You will learn what your own MLOps Platform will require.
You will learn all about the Feature Store, which is an important component of the MLOps Platform. In addition, we cover the issues of whether the MLOps Platform is needed, and to what extent. Will its introduction disrupt accepted processes? How to approach building the platform to make the most of the resources the company already has.
You will get a step-by-step guide as to how to do it and which Feature Store will be best for your project.
Do any of the above points describe your or your project's situation?
Grab eBook & Tutorial and get it DONE.
Grab eBook & Tutorial and get it DONE.
The administrator of your personal data is GetInData Poland Sp. z o.o. with its registered seat in Warsaw (02-508), 39/20 Pulawska St.
Your data is processed for the purpose of provision of electronic services in accordance with the Terms & Conditions.
For more information on personal data processing and your rights please see Privacy Policy.
By submitting this form, you agree to our Terms & Conditions and Privacy Policy.
Your data is processed for the purpose of provision of electronic services in accordance with the Terms & Conditions.
For more information on personal data processing and your rights please see Privacy Policy.
By submitting this form, you agree to our Terms & Conditions and Privacy Policy.
For 4 years GetInData has been building Machine Learning Platforms for dozens of clients such as PLAY - the biggest Polish telecom or ING Bank with over 14.1M primary customers from over 40 countries. However, we were building data lakes and other Big Data solutions long before that.
We have developed projects that have brought tangible benefits to our clients (from small startups to the biggest companies on the market) by accelerating the Machine Learning processes, e.g.:
We have developed projects that have brought tangible benefits to our clients (from small startups to the biggest companies on the market) by accelerating the Machine Learning processes, e.g.:
For ING Bank we reduced data discovery time by 30%, transferred servers’ layer to the platform as xrdp containers in 5 months, meeting the regulations of over 40 different countries' markets. Download the customer story to get more insight: ING Customer Story.
For PLAY we delivered architectural guidance and navigated the project from the PoC phase to successful full scale deployment in production. As a result, PLAY is currently using a scalable, secure, extensible Data Platform that can easily be queried for analytical, business and marketing purposes in real time, with a reduced operational cost. Download the customer story to get more insight: PLAY Customer Story.
This eBook is based on our expertise
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