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Security Automation

DevOps Experience

The ML startup faced high costs during its growth for a data-driven platform infrastructure that processes around 30 TB per month and stores raw data for 12 months on AWS. We reduced the monthly cost from $75,000 to $22,000 and achieved 30% performance over SLA.

2k+

QPS performance

70%

cost reduction

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About the client

Deduce is an IT Services and IT Consulting company that protects businesses and their customers from unauthorized account access, and identity fraud.

Tech stack

AWS EC2

AWS RDS

PostqreSQL

Kafka

Redis

Challenges & solutions

Challenge

Create AWS infrastructure that performs 2k queries per second and has 99.9 % service availability.

Solution

Removed managed services and set up a self-hosted DB over EC2.

Challenge

Optimize redundancy and 2 times decrease in cost of infrastructure.

Solution

Used a cluster of servers for DB sharding, adding Elasticsearch, Kafka and Redis for different streams of data-based on industry standards.

Challenge

Create failover strategy and possibilities for larger scale.

Solution

Create master/master-slave mirroring in different regions to have a failover strategy.

Challenge

ML startup has encountered infrastructure cost issues during the extensive growth.

Solution

DB architecture was tuned to execute most-often queries faster.

Challenge

The main goal was to decrease the monthly operational cost ($75 K) for a large data-driven platform that handles ~ 240 bln entries monthly ( ~ 30TB), storing raw data for 12 months on AWS.

Solution

Updated ETL pipelines to reduce load on DB. Cost reduction from $75k to $22k per month with performance 30% over SLA.

They have very intelligent people on their team — people that I would gladly hire and pay for myself.

Robert P.

CTO
Cybersecurity

The way we deal with your task and help achieve results

Step 1 of 5
Free consultation

It’s a good time to get info about each other, share values and discuss your project in detail. We will advise you on a solution and try to help to understand if we are a perfect match for you.

Step 2 of 5
Discovering and

One of our core values is flexibility, hence we work with either one page high level requirements or with a full pack of tech docs.

In Data Science, there are numerous models and approaches, so at this stage we perform a set of interviews in order to define project objectives. We elaborate and discuss a set of hypotheses and assumptions. We create solution architecture, a project plan, and a list of insights or features that we have to achieve.

Step 3 of 5
Solution development

The work starts with data gathering, data cleaning and analysis. Feature engineering helps to determine your target variable and build several models for the initial review. Further modeling requires validating results and selecting models for the further development. Ultimately, we interpret the results. Nevertheless, data modeling is about a process that requires lots of back and forth iterations. We are result focused, as it’s one of our core values as well.

Step 4 of 5
Solution delivery

Data Science solutions can be a list of insights or a variety of different models that consume data and return results. Though we have over 15 years of expertise in data engineering, we expect client’s participation in the project. While modeling, we provide midterm results so you can always see where we are and provide us with feedback. By the way, a high-level of communication is also our core value.

Step 5 of 5
Support and continuous improvement

We understand how crucial the solutions that we code for our clients are! Our goal is to build long-term relations, so we provide guarantees and support agreements. What is more, we are always happy to assist with further developments and statistics show that for us, 97% of our clients return to us with new projects.

Success stories

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