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24 June 2020
4 min read
Software engineers and data scientists are two distinct, yet equally important roles in computer science. Although they both require knowledge of programming, there are several differentiating factors between software engineers and data scientists. Software engineers specialize in the creation and maintenance...
22 June 2020
4 min read
Today, mass amounts of data come from a myriad of applications and microservices. DevOps engineers are often tasked with ensuring that data is collected, retained, and secured in a way that follows strict regulations. Focusing on data security, many companies rely on VMware for various internal cloud-computing...
18 June 2020
6 min read
On 9 June 2020, Algorithmia CTO, Kenny Daniel, co-hosted a webinar with Sam Charrington of TWIML on building internally versus buying an existing machine learning operations platform. The webinar recording can be accessed here. The discussion generated a lot of questions, and Kenny, Sam, and other Algorithmia...
15 June 2020
5 min read
A data pipeline is a software that allows data to flow efficiently from one location to another through a data analysis process. The steps in a data pipeline usually include extraction, transformation, combination, validation, visualization, and other such data analysis processes. Without a data pipeline,...
11 June 2020
5 min read
Fundamentally, machine learning models are divided into two camps: supervised and unsupervised. The supervised model is probably the type you’re most familiar with, and it represents a paradigm of learning that’s prevalent in the real world.  What is supervised learning?  In supervised learning,...
9 June 2020
7 min read
Deep learning is a subset of machine learning that deals with algorithms that mimic the function of the brain, called artificial neural networks, which learn from large sets of data. It is called “deep” learning since it uses multiple layers in a network, making it deeper than other more simple...
4 June 2020
3 min read
Big data is a field that was developed for organizations to process, analyze, and extract information from datasets that are too large for traditional data collection methods. Today, we will be talking about the big data industry, including what kinds of industries use this data, real world examples...
2 June 2020
3 min read
As your company begins to proof out machine learning use cases and develop models, your ML teams need to be thinking long-term. How will you deploy, operate, and manage your models once you have them? Making AI–minded decisions like this starts with this question: should I build or buy a machine learning...
28 May 2020
3 min read
At Algorithmia, we have learned how enterprise machine learning is developing across industries, and we are most interested in the challenges that hinder organizations from extracting ML value. In late 2019, we published data on the main challenges, which we consolidated in a report called the “2020...
26 May 2020
8 min read
The machine learning lifecycle begins with data warehousing, ETL pipelining, and model training. At Algorithmia, we focus on the next stages in the lifecycle: deployment, management, and operations. Machine learning deployment plays a critical part in ensuring a model performs well, both now and in the...