Data for all postal codes in Finland free of charge enriched by area boundaries as standard coordinates.
Datalore is an online data science environment. Typical CTRL+C and CTRL+V commands do not work in the Datalore terminal, so here is the solution.
List of business intelligence, reporting and data pipeline tools. Reporting tools Reporting and business intelligence tools.
Vertex AI is a bare-bones analytics environment in Google Cloud.
Google Colab is a low barrier option to run Python scripts.
Datalore is a collaborative data science platform. The notebook experience has been taken to the next level.
The online Python worskpace Datalore has three main models for pricing and licensing.
Here is a presentation of the wealth management app I have developed.
Reading and visualizing Finland’s postal code data on a map in Python.
Visualize Finland’s postal code areas on a map in Google Looker Studio.
with these instructions, you can visualize Finland’s postal code areas on a map in Power BI Desktop.
Looker Studio is a free reporting and business intelligence tool in Google Cloud.
Data Science platforms can be categorized to a few different buckets:
Various kinds of teams from business innovation to academic research can benefit from data science platforms.
Let’s go through the most typical use cases and their benefits to start using a data science platform.
Comprehensive list of data science platforms. Sometimes known also as machine learning platforms, ai platforms or DSML platforms.
Robust MLOps platform for end-to-end solutions.
For technically advanced teams looking for flexible Kubernetes deployments.
Specific use cases with fully managed low code.
Log experiments and ML models versions from any environment.
Paperspace Gradient machine learning platform is best known from extensive GPU support.
Saturn Cloud is a greate choice for data science teams who want to maximize flexibility of their environment.
Datalore is a fairly recent online platform for advanced data analytics.
Bodo is a platform for data processing with Python and SQL.
User-Managed notebooks in Vertex AI are virtual workspaces for data exploration.
Around 30 questions I memorize from the Google Cloud Professional Machine Learning certification exam.
After 4 months of intense studying I passed the Google Cloud certification for Professional Machine Learning Engineer!
Google Cloud Platform has excellent toolset to operationalize and productionize machine learning models.
Natural Language Processing (NLP) refers to tools and methods to explore text data as well as identifiy patterns and making predictions.
Some notes about image recognition while preparing for Google Cloud MLE certification.
Keras is one of the high level APIs in Tensorflow deep learning stack.
Tensorflow Extended (known as TFX) is a framework to define ML pipelines.
I have more experience from Pandas and Scikit-Learn Python libraries compared to Tensorflow.
Recommendation systems are useful to personalize experience and find relevant items among huge catalogs.
I heard about artificial neural networks first time around 2017. Since then I have tried to understand their behavior and explain them in a simple way.
Dataflow product in Google Cloud is mandatory for advanced data processing pipelines for machine learning solutions.
Notes about fundamental ML concepts for Google Cloud ML Engineering certification.
Some Google materials refer to it as Fully managed Tensorflow.
BigQuery is by far the most important storage and processing service in Google Cloud from ML perspective.
This is a summary of Google Cloud Platform (GCP) products relevant for Machine Learning Engineer role.
Kubernetes have been everywhere lately. Especially in the context of MLOps. I gave it a try by creating web app with Python Flask.
Google Colab, Databricks Community Edition, Visual Studio Code and Dcoker are some options to create a free data science workspace.
Comparing the major machine learning platforms AWS SageMaker, Azure Machine Learning, Google Vertex AI and Databricks.
What is a machine learning platform? Introducing different components such as workbench, MLOps tools and cloud computation.
Machine learning in predictive maintenance. The two-part blog series provides insights for cost savings and an example script in Python.
How to fool a web service about your actual location? In an experiment I pretended being in Ireland while traveling in Sweden.
In my opinion the big difference is that a data scientist focuses more on business problems while data engineer solves technical problems.
Experiences from DataCamp online training. Structured data science courses are easy to organize for yourself or a team.
The article goes through the PySpark execution logic and provides guidelines to optimize the speed and performance.
Clustering time series data with SQL - Nice 3D visualization using simple logic. Python notebook example in GitHub with industrial data.
A tutorial for parallel computation with Spark and Python. The example has been ran on AWS cloud computing platform.
AWS Glue service works especially well for big data batch processing. Read the full post from data.solita.fi.
I wrote to Solita's blog about text analytics with the headline "Finnish stemming and lemmatization in python". The post has code examples.
Experiences from funding application classification by text analytics
I give an example about machine learning use case in a format that should be understandable also for less technical people.
Read about Solita team's solution in a hackathon organized by Hiab. The task was to take advantage of data to maximize machine uptime.
Python code to automatically list header fields of multiple CSV files. The original use case was related to data warehouse documentation.
The built-in dataset quakes in RStudio had 1000 records of earthquakes nearby Fiji.
The problem: A virus is spreading across the world - it kills without treatment. Your task is to solve a statistical puzzle.
It's easy to spot these hype terms like data science, big data in LinkedIn or exhibition posters. I summarized the definitions.
Python based Django web framework offers a great platform to create a data oriented web application for any size of needs.