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.