Types of data science platforms - Workspace, MLOps or full stack?

Data Science platforms can be categorized to a few different buckets:

List of data science platforms

Comprehensive list of data science platforms. Sometimes known also as machine learning platforms, ai platforms or DSML platforms.

Neural networks for natural language processing

Natural Language Processing (NLP) refers to tools and methods to explore text data as well as identifiy patterns and making predictions.

Neural networks for image recognition

Some notes about image recognition while preparing for Google Cloud MLE certification.

Keras for basic neural networks

Keras is one of the high level APIs in Tensorflow deep learning stack.

Tensorflow for ML Engineers

I have more experience from Pandas and Scikit-Learn Python libraries compared to Tensorflow.

Recommendation systems in Google Cloud

Recommendation systems are useful to personalize experience and find relevant items among huge catalogs.

Machine learning fundamentals

Notes about fundamental ML concepts for Google Cloud ML Engineering certification.

Vertex AI for ML Engineers in Google Cloud

Some Google materials refer to it as Fully managed Tensorflow.

BigQuery for ML Engineers in Google Cloud

BigQuery is by far the most important storage and processing service in Google Cloud from ML perspective.

Free data science workspaces

Google Colab, Databricks Community Edition, Visual Studio Code and Dcoker are some options to create a free data science workspace.

Comparison of machine learning platforms in major clouds

Comparing the major machine learning platforms AWS SageMaker, Azure Machine Learning, Google Vertex AI and Databricks.

What is a machine learning platform?

What is a machine learning platform? Introducing different components such as workbench, MLOps tools and cloud computation.

Machine learning in predictive maintenance

Machine learning in predictive maintenance. The two-part blog series provides insights for cost savings and an example script in Python.

Difference between data scientist and data engineer roles

In my opinion the big difference is that a data scientist focuses more on business problems while data engineer solves technical problems.

DataCamp - Learn data science online

Experiences from DataCamp online training. Structured data science courses are easy to organize for yourself or a team.

Clustering data using SQL - An example with industrial IoT data

Clustering time series data with SQL - Nice 3D visualization using simple logic. Python notebook example in GitHub with industrial data.

Finnish stemming and lemmatization in python

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

Experiences from funding application classification by text analytics

Combining machine learning and business - Practical example

I give an example about machine learning use case in a format that should be understandable also for less technical people.

Sports betting tutorial - Can you make the living?

You can make the living by sports betting. The blog is not sponsored as I'm sharing my own experiences. Read the tutorial.