In today’s data-driven business landscape, effective Master Data Management is key. The amount of data enterprises generate and collect is growing at unprecedented speed. Everyone agrees that data can deliver the edge a business needs to be successful. However, the requirement to be data-driven has pushed many businesses to frantically acquire as much data as …
Here you are, reading this article – most likely because some AI-powered algorithm made it available for you based on your search and other information it might have on you. I know this sounds a bit scary. And the truth is that, for most people, Artificial Intelligence (AI) and its closely connected sub-term machine learning …
Personalization is a critical aspect of any successful retail or e-commerce strategy. By tailoring your messages, offers, and product recommendations to each customer’s unique interests and preferences, you can create a more engaging and meaningful experience that drives loyalty and increases sales. However, personalization is only effective if you have the right data. In this …
Research by renowned analysts is also clear – as many as 43% of web shop visitors first go to “search”, and as many as 39% of all online customers admit that “search” significantly influenced their purchase. Even if your website is organized perfectly, customers will still get confused and some simply prefer search mechanics. Yet, …
When we started building our product, we focused on the Recommender engine and Segmentation. As a startup, we embraced agility and focus on the market. At the beginning of 2020, we decided that our product must become a reflection of our team. Then we said it must be a “no bullshit” product, agile or, in …
In this text we will try to briefly explain IM applications integration with Visual Search. You can read more about Visual Search implementation in our last blog post. Instant Messaging (IM) and Chatbots Among all other benefits, The Internet has transformed and simplified how people communicate with each other. In addition to email, IM has …
According to Gartner, only 15% to 20% of data science projects get completed. Of those projects that did complete, CEOs say that only about 8% of them generate value. Despite these facts, data science is still considered as an opportunity for business growth. These facts are something that always lingers in the back of everyone’s …
Intro and motivation In the beginning, it started as the three separate projects. The first one for managing machine learning models known as MoMa, the second one for forecasting using multiple models to give the best results (Fibi), and the third one was a business solution for segmentation and recommender system with personalized view and …
Time series is a term that you must or would have faced in your Data Science career. If you are completely new to this, don’t worry, it is really intuitive. What is actually a Time Series? It is a collection of data points collected at constant time intervals. So, we have a history data about …
In the field of software development, different teams have different approaches to how things are being done. When the team is small, introducing processes may be overwhelming, and the biggest challenge is to determine when is the right time to define one. From the early days at Things Solver, we didn’t quite think about defining …
After watching Udemy online course Building Recommender Systems with Machine Learning and AI, I came up with the idea to write a text that can help beginners to understand the basic ideas of the recommender systems. A recommender system, or a recommendation system is a subclass of information filtering system that seeks to predict the …
In the previous text, we talked about the basics of streaming, what it means in theory, what are the advantages, disadvantages and mentioned some streaming tools. This text is more technical, and we will talk about Flink in general as well as the basics of streaming in Flink, the whole process from start (read data) …
Needs in the IT sector are constantly changing. After Coronavirus hit us unexpectedly, this is true, more than ever. In order to keep distance from each other, we were forced to limit all our activities that can’t be done online. At first, this was shocking and we weren’t prepared for such change. But when we …
Data streaming has become very popular in the big data industry. It is used for processing large amounts of data from different sources which are continuously generated, in real-time. When we say “real-time” we need to understand that it can vary from a few milliseconds to a few minutes. Besides that streaming is enabling us …
After participating in a meetup at the end of March, subjected “Data Science – what is it?”, a lot of people contacted me to send them some introductory materials to help them get started with learning. It took me a long time to sit down and start compiling a list, because there are many sources, …
Let’s say you have been working on a project for clients segmentation. You have your client segments well separated and your final task is to present findings and results to the project stakeholders. Usual situation is that none of them have that level of technical expertise to understand your code so you need to visualize …
Retail industry. In the glory of Data Science, it’s all about the data and tailor-made targeting. If you want to brag about it, you would say – I’ve got the unique, omni-channel, 360-something, that can perfectly model customer’s behaviour and even go to Mars. What really happens is that you literally feel lost. There are …
Pedro Domingos is a professor of computer science and engineering at the University of Washington. He is a winner of the SIGKDD Innovation Award, which FYI is the highest honor in Data Science. They say that approximately seven years is the time period needed to become an expert in the field of Data Science. This …
Having spent couple of weeks on data preparation and developing that particular machine learning model, you are finally ready to show off with some really good results to your boss. You have your notebooks with lines of code doing magic, maybe some reports in Excel, amazing visualizations in Plotly etc. It’s 5 minutes till your presentation …
When dealing with anomalies in the data, there are lots of challenges that should be solved. We’ve already had a couple of articles regarding this topic, and if you haven’t already read it, I suggest you to take a look at this one, aaand this one, from my fellow colleague Miloš. In this post, I’m …
Why time series anomaly detection? Let’s say you are tracking a large number of business-related or technical KPIs (that may have seasonality and noise). It is in your interest to automatically isolate a time window for a single KPI whose behavior deviates from normal behavior (contextual anomaly – for the definition refer to this …
The problem of anomaly detection is a very challenging problem often faced in data analysis. Whether it is about clustering, classification or some other machine learning problem, it is of great importance to identify anomalies and handle them in some way, in order to achieve optimal model performances. Furthermore, anomalies could often influence the analysis …
Hi, everyone. Although I planned for my next post to be about anomaly detection and their treatment, I faced some other type of problem that quickly escalated into huge issue affecting the modelling and results accuracy, and couldn’t resist to share my experience as soon as possible. In this post, I will be talking about …
In my previous post, I tried to present the ARIMA model for forecasting. It was based on the use of autoregression and moving average concepts, combining the regression of variable based on its lagged values and calculation of error based on the linear combination of error terms occurred in the past, respectively. In this post, …
The Internet is becoming the largest global shop across markets, and anyone who is offering products and services of any kind prefers for web shops to become the primary outlets to supply customers. This leads to a reduction in the number of employees and traditional brick and mortar branches and reduction in costs, so it …
One of the most challenging machine learning problems is predicting some output based on the history of its previous values. The complexity of the problem multiplies as new features and constraints are added to analysis. Thus, in time series analysis it is not always enough to use previous values only, there often are many features …
