Data is the lifeblood of machine learning (ML) projects. At the same time, the data preparation process is one of the main challenges that plague most projects. According to a recent study, data preparation tasks take more than 80% of the time spent on ML projects. Data scientists spend most of their time on data cleaning (25%), labeling (25%), augmentation (15%), aggregation … [Read more...] about Solving Data Challenges In Machine Learning With Automated Tools
Infrastructure
Overview of the Different Approaches to Putting Machine Learning Models in Production
There are different approaches to putting models into production with benefits that can vary dependent on the specific use case. Take, for example, the use case of churn prediction. It is beneficial to have a static value that can be easily looked up when someone calls customer service, but there is some extra value that could be gained if, for specific events, the model could … [Read more...] about Overview of the Different Approaches to Putting Machine Learning Models in Production
Everything a Data Scientist Should Know About Data Management*
(*But Was Afraid to Ask) To be a real “full-stack” data scientist, or what many bloggers and employers call a “unicorn,” you’ve to master every step of the data science process — all the way from storing your data, to putting your finished product (typically a predictive model) in production. But the bulk of data science training focuses on machine/deep learning techniques; … [Read more...] about Everything a Data Scientist Should Know About Data Management*
20 Criteria You Should Use To Choose A Data Catalog
The Roles of a Data Catalog The difficulties of data management have intensified at a steady pace over the past several years. The management complexities of big data, cloud hosting, self-service analytics, and tightening regulations can’t be ignored. Effective data management has become a top priority for most organizations, but getting there is challenging. Data catalogs … [Read more...] about 20 Criteria You Should Use To Choose A Data Catalog
How to Organize Data Labeling for Machine Learning: Approaches and Tools
If there was a data science hall of fame, it would have a section dedicated to labeling. The labelers’ monument could be Atlas holding that large rock symbolizing their arduous, detail-laden responsibilities. ImageNet — an image database — would deserve its own style. For nine years, its contributors manually annotated more than 14 million images. Just thinking about it makes … [Read more...] about How to Organize Data Labeling for Machine Learning: Approaches and Tools
Machine Learning Project Structure: Stages, Roles, and Tools
Various businesses use machine learning to manage and improve operations. While ML projects vary in scale and complexity requiring different data science teams, their general structure is the same. For example, a small data science team would have to collect, preprocess, and transform data, as well as train, validate, and (possibly) deploy a model to do a single … [Read more...] about Machine Learning Project Structure: Stages, Roles, and Tools
Major Challenges for Machine Learning Projects
Although scientists, engineers, and business mavens agree we might have finally entered the golden age of artificial intelligence when planning a machine learning project you have to be ready to face much more obstacles than you think. Deep learning algorithms like AlphaGo are breaking one frontier after another, proving that machines can already be able to play complex … [Read more...] about Major Challenges for Machine Learning Projects