Posts 1-25 / 110
Paperspace Gradient - ML platform with their own data centers and IPU processors
Paperspace Gradient machine learning platform is best known from extensive GPU support. They have recently partnered with Graphcore to provide new generation Intelligence Processing Units .
Saturn Cloud - Data science workspace with Dask cluster
Saturn Cloud is a greate choice for data science teams who want to maximize flexibility of their environment. Integrated parallel processing with Dask differentiates it from the competitors.
Datalore - Collaborative data science platform with great notebook experience
Datalore is collaborative data science platform from Jetbrains. The notebook experience has been taken to the next level. The company is best known for its Python IDE PyCharm.
Bodo is a faster alternative for Spark to run massive ETL jobs in Python
Bodo is a platform for data processing with Python and SQL. It is especially suitable for large datasets thanks to its unique parallel processing technology.
Vertex AI User-Managed notebooks auto shutdown
User-Managed notebooks in Vertex AI are virtual workspaces for data exploration. But they lack automatic shutdown after being idle for specific amount of time.
30 questions for Google Cloud Professional Machine Learning Engineer exam
Around 30 questions I memorize from the Google Cloud Professional Machine Learning certification exam. You find all sources for exam training questions from my preparation tips.
I became a certified Google Cloud Professional Machine Learning Engineer!
After 4 months of intense studying I passed the Google Cloud certification for Professional Machine Learning Engineer! Google Cloud Platform certificates are considered to be challenging compared to other cloud platforms .
MLOps in Google Cloud
Google Cloud Platform has excellent toolset to operationalize and productionize machine learning models. Vertex AI is the key MLOps product while Google Kubernetes Engine is valid alternative for custom workflows.
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.
Big toe gets numb in skates - After years of trial and error I found the solution
Ice hockey has remained my hobby until these days after taking my first skates almost three decades ago. Despite my long experience, I suffered for years from an annoying problem with my skates.
Keras for basic neural networks
Keras is one of the high level APIs in Tensorflow deep learning stack. It is the recommended framework to get started with neural networks, if you do not have special requirements.
Tensorflow Extended (TFX) for MLOps
Tensorflow Extended (known as TFX) is a framework to define ML pipelines. The extensions are obviously compatible with the core Tensorflow.
Tensorflow for ML Engineers
I have more experience from Pandas and Scikit-Learn Python libraries compared to Tensorflow. I was surprised how large the Tensorflow ecosystem with its ML engineering extensions.
Are cloud certificates beneficial?
Cloud certificates are proof of developer’s competence. But are they beneficial in practice? I am talking about the certification diploma rather than the learning process.
Recommendation systems in Google Cloud
Recommendation systems are useful to personalize experience and find relevant items among huge catalogs. Recommendations have became signigficant sub topic of machine learning.
Introduction to neural networks for ML
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 for ML Engineers in Google Cloud
Dataflow product in Google Cloud is mandatory for advanced data processing pipelines for machine learning solutions. It performs typical data engineering tasks by allowing same code to execute both batch and streaming.
Machine learning fundamentals
Notes about fundamental ML concepts for Google Cloud ML Engineering certification. Data exploration Perform mainly univariate and bivariate analysis during initial exploration.
Vertex AI for ML Engineers in Google Cloud
Some Google materials refer to it as Fully managed Tensorflow. Vertex AI assumes that data is prepared elsewhere before training the model.
BigQuery for ML Engineers in Google Cloud
BigQuery is by far the most important storage and processing service in Google Cloud from ML perspective. It has many integrated functionalities for ML.
Machine learning products in Google Cloud
This is a summary of Google Cloud Platform (GCP) products relevant for Machine Learning Engineer role. Google philosphy seems to be that moving to their platform requires minimal changes to the existing solution.
Google Cloud ML Engineer certification - Training and preparing
I am preparing for Google Cloud Professional Machine Learning Engineer certification . The certification is mandatory in the project I am working on.
Pirsch - Google Analytics alternative for static website
Setting up anayltics tracking for a web page is simple. Just copy couple of lines of code from the select analytics service and you are good to go.
EmailLabs - EU based service for 24 000 free transactional emails per month
During my website migration it became evident that I would also need a new email service. Previously the email hosting was integrated in the CPanel of my web hotel.