Special topics in machine learning
Algorithmia is machine learning, managed. Deploy, serve, and manage your ML portfolio.
22 April 2021
2 min read
Algorithmia has been recognized as a leader in GigaOm’s Radar for Evaluating Machine Learning Operations (MLOps). read more
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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...
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...
21 May 2020
6 min read
The difference between machine learning and statistics has been the subject of long-running debate. A debate so contentious at times, that it has even become the subject of memes.  Some say that machine learning is just glorified statistics, rebranded for the age of big data and faster computing. Others...
20 May 2020
3 min read
In the last 12 months, there have been myriad developments in machine learning (ML) tools and applications, yet many companies are still struggling to see tangible business results from AI models in production. Deploying AI/ML solutions isn’t easy, but Algorithmia and Appen make it simple to tackle...
18 May 2020
5 min read
Decision making in the business world today utilizes business intelligence and technologies like machine learning to provide the insights needed to make informed decisions. Businesses can easily churn out data analytics from a variety of big data sources. This combats the challenges of incomplete data,...