- MLOps Best Practices - Jul 29, 2021.
Many technical challenges must be overcome to achieve successful delivery of machine learning solutions at scale. This article shares best practices we encountered while architecting and applying a model deployment platform within a large organization, including required functionality, the recommendation for a scalable deployment pattern, and techniques for testing and performance tuning models to maximize platform throughput.
Best Practices, MLOps
- Unleashing the Power of MLOps and DataOps in Data Science - Jun 29, 2021.
Organizations trying to move forward with analytics and data science initiatives -- while floating in an ocean of data -- must enhance their overall approach and culture to embrace a foundation on DataOps and MLOps. Leveraging these operational frameworks are necessary to enable the data to generate real business value.
Best Practices, Data Science, DataOps, MLOps
- 15 common mistakes data scientists make in Python (and how to fix them) - Mar 3, 2021.
Writing Python code that works for your data science project and performs the task you expect is one thing. Ensuring your code is readable by others (including your future self), reproducible, and efficient are entirely different challenges that can be addressed by minimizing common bad practices in your development.
Best Practices, Data Scientist, Jupyter, Mistakes, Programming, Python
- Can Data Science Be Agile? Implementing Best Agile Practices to Your Data Science Process - Jan 18, 2021.
Agile is not reserved for software developers only -- that's a myth. While these effective strategies are not commonly used by data scientists today and some aspects of data science make Agile a bit tricky, the methodology offers plenty of benefits to data science projects that can increase the effectiveness of your process and bring more success to your outcomes.
Agile, Best Practices, Data Science, Development
Software Engineering Tips and Best Practices for Data Science - Oct 13, 2020.
Bringing your work as a Data Scientist into the real-world means transforming your experiments, test, and detailed analysis into great code that can be deployed as efficient and effective software solutions. You must learn how to enable your machine learning algorithms to integrate with IT systems by taking them out of your notebooks and delivering them to the business by following software engineering standards.
Best Practices, Data Science, Software Engineering, Tips
- 5 Best Practices for Putting Machine Learning Models Into Production - Oct 12, 2020.
Our focus for this piece is to establish the best practices that make an ML project successful.
Best Practices, Machine Learning, Production
- LinkedIn’s Pro-ML Architecture Summarizes Best Practices for Building Machine Learning at Scale - Sep 23, 2020.
The reference architecture is powering mission critical machine learning workflows within LinkedIn.
Best Practices, LinkedIn, Machine Learning, Scalability
- Computer Vision Recipes: Best Practices and Examples - Sep 2, 2020.
This is an overview of a great computer vision resource from Microsoft, which demonstrates best practices and implementation guidelines for a variety of tasks and scenarios.
Best Practices, Computer Vision, Microsoft, Python
- 5 Apache Spark Best Practices For Data Science - Aug 4, 2020.
Check out these best practices for Spark that the author wishes they knew before starting their project.
Apache Spark, Best Practices, Data Science
- Software engineering fundamentals for Data Scientists - Jun 30, 2020.
As a data scientist writing code for your models, it's quite possible that your work will make its way into a production environment to be used by the masses. But, writing code that is deployed as software is much different than writing code for exploratory data analysis. Learn about the key approaches for making your code production-ready that will save you time and future headaches.
Advice, Best Practices, Data Science, Programming, Software Engineering
- Five Lines of Code - Jun 24, 2020.
If you want to learn simple and practical rules for coding and refactoring, "Five Lines of Code" from Manning is the guide for you, teaching you concrete principles for refactoring. Save 40% with code nlfive40 until July 24.
Best Practices, Book, Manning, Programming
- Taming Complexity in MLOps - May 28, 2020.
A greatly expanded v2.0 of the open-source Orbyter toolkit helps data science teams continue to streamline machine learning delivery pipelines, with an emphasis on seamless deployment to production.
Best Practices, Docker, MLOps, Python
- 10 Useful Machine Learning Practices For Python Developers - May 25, 2020.
While you may be a data scientist, you are still a developer at the core. This means your code should be skillful. Follow these 10 tips to make sure you quickly deliver bug-free machine learning solutions.
Best Practices, Machine Learning Engineer, Python
- Coding habits for data scientists - May 14, 2020.
While the core machine learning algorithms might only take up a few lines of code, it's the rest of your program that can get messy fast. Learn about some techniques for identifying bad coding habits in ML that add to complexity in code as well as start new habits that can help partition complexity.
Best Practices, Data Scientist, Development, Jupyter, Programming
Natural Language Processing Recipes: Best Practices and Examples - May 1, 2020.
Here is an overview of another great natural language processing resource, this time from Microsoft, which demonstrates best practices and implementation guidelines for a variety of tasks and scenarios.
Best Practices, Microsoft, NLP, Python
- Reproducibility, Replicability, and Data Science - Nov 19, 2019.
As cornerstones of scientific processes, reproducibility and replicability ensure results can be verified and trusted. These two concepts are also crucial in data science, and as a data scientist, you must follow the same rigor and standards in your projects.
Best Practices, Data Science, Overfitting, Reproducibility, Trust, Validation
6 Key Concepts in Andrew Ng’s “Machine Learning Yearning” - Aug 12, 2019.
If you are diving into AI and machine learning, Andrew Ng's book is a great place to start. Learn about six important concepts covered to better understand how to use these tools from one of the field's best practitioners and teachers.
AI, Andrew Ng, Best Practices, Deployment, Machine Learning, Metrics, Training Data
- 12 Things I Learned During My First Year as a Machine Learning Engineer - Jul 23, 2019.
Learn about the day-in-the-life of one machine learning engineer and the important lessons learned for being successful in that role.
Advice, Best Practices, Communication, Machine Learning Engineer, Skills
The Best and Worst Data Visualizations of 2018 - Feb 8, 2019.
We reflect on some of the best examples of Data Visualization throughout 2018, before focussing on some of the not-so-good and how these can be improved.
Advice, Best Practices, Data Visualization, Failure, Sankey
- Best Practices for Using Notebooks for Data Science - Nov 8, 2018.
Are you interested in implementing notebooks for data science? Check out these 5 things to consider as you begin the process.
Best Practices, Data Science, Jupyter
Programming Best Practices For Data Science - Aug 7, 2018.
In this post, I'll go over the two mindsets most people switch between when doing programming work specifically for data science: the prototype mindset and the production mindset.
Best Practices, Data Science, Pandas, Programming, Python
- Best Practices in Data Visualization - May 2, 2018.
Do your data visualizations need a reboot? Though data visualizations may be designed to facilitate understanding, not all graphs are effective. In this webcast, viewers will learn how to use best practices to give a graph a makeover.
Best Practices, Data Visualization, JMP
- 5 Best Practices for Big Data Security - Jun 9, 2016.
Lack of data security can not only result in financial losses, but may also damage the reputation of organizations. Take a look at some of the most important data security best practices that can reduce the risks associated with analyzing a massive amount of data.
Best Practices, Big Data, Security
- 10 things statistics taught us about big data analysis - Feb 10, 2015.
There are 10 ideas in applied statistics are relevant for big data analysis, focusing on prediction accuracy, interactive analysis and more.
Best Practices, Big Data, Overfitting, Statistics
