Machine learning

Feeling Overwhelmed by Data? We Can Help You Extract Meaning.

You have a wealth of data, but are unsure how to turn it into actionable insights. We understand the challenge. Our goal is to bridge the gap between your data and the answers you seek.

Beyond Technical Expertise: Focusing on Your Business Goals

We go beyond simply understanding machine learning methods. We take the time to understand your specific business challenges and goals. This allows us to tailor our approach to deliver solutions that directly impact your bottom line.

Don’t Get Lost in the Jargon: Transparency and Collaboration

Machine learning can feel like a black box. We believe in clear communication. We’ll explain the various methods (decision trees, support vector machines, etc.) in a way that is relevant to your situation. We’ll also collaborate with you throughout the process, ensuring you understand the path we’re taking.

Cutting-Edge Solutions for Complex Problems

The world of data is constantly evolving. We stay at the forefront of advancements, including deep learning and its potential to overcome previously difficult challenges.

A Customized Approach for Optimal Results

There’s no one-size-fits-all solution. We leverage our deep theoretical background to select and potentially even develop original methods that are best suited to address your unique needs.

The Right Process for Success

Similar to traditional statistical problem-solving, we follow a proven process to ensure efficient and effective solutions. We’ll work with you through each step, keeping your goals at the forefront.

We don’t just process data, we unlock its potential to help you achieve your business objectives.

The stages of the process:

1

step

Characterizing the algorithmic problem and selecting metrics for success

2

step

Find the effective way to collect and store the information.

3

step

Improve the information by clearing the information and keeping it convenient for processing

4

step

Calculate the characteristics of the information (feature engineering) that will help in performing the task such as classification.

5

step

Select the methods to be used to perform the tasks. Methods from the selection above and other advanced methods we specialize in such as Monte-Carlo methods for computing statistical metrics on complex distributions.

6

step

Train the model and evaluate the parameters. Depending on the amount of information collected and the computational load, a local computer or cloud services will be used.

7

step

Examine the model. Examination of the results is according to the defined metrics (methods such as cross-validation etc.)

8

step

Apply the model to new data that is received. Implementation of the model is necessary for purposes such as forecasting or decision making.
Depending on the problem, there may be changes in the process. We are committed to success and use advanced tools to achieve a good solution in a short time.

Make decisions based on your DATA!
It's easy and good results are immediate!