EMA TOP 3 - ENTERPRISE DECISION GUIDE
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EMA Top 3
Enterprise Decision Guide for MLOps in 2022

EMA Top 3 News - MLOps

Product Requirements

EMA Top 3 Award Winners in the MLOps category have demonstrated the ability to simplify the deployment, operation, and scaling of data science and machine learning environments. These products address the critical MLOps pain points such as cost, performance, a lack of staff skills, difficult experimentation for data scientists, and insufficient tooling. 

Critical MLOps Pain Points

Experimentation is the fastest growing MLOps related pain point, while performance still holds the overall lead as the most important pain point. Lack of skills, cost, and a lack of tooling are next in line. 
Data source: Stackoverflow.com

Developer Challenges

Machine Learning Challenges for Developers - Cluster Chart
Cluster chart of the critical machine learning-related topics developers discuss on Stackoverflow.com in 2022.

MLOps Momentum in 2022

  • Google Trends
  • Venture Capital
  • Reddit Posts
  • MLFlow
  • Stackoverflow
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Google Search Frequency 

Annual Venture Capital

Number of Monthly Reddit Posts

Number of GitHubs Stars

MLOps on Stackoverflow

Ranking of Machine Learning Topics

Training of learning models and feature-engineering gained the most importance of the top 10 machine learning topics between 2017 and 2021. Determining feature importance and oversampling entered the list of critical machine learning topics in 2021. 
Data Source: Stackexchange for Data Science

Machine Learning and Kubernetes

Due to its scalability, Kubernetes has become the platform of choice for machine learning. Performance, scalability, and cost are the three critical considerations for enterprises to address when developing and operating
Data source: Stackoverflow
Data source: Stackoverflow.com

Machine Learning Related Open Source Projects by GitHub Stars

Data-related GitHub repositories make up the largest part of the Linux Foundation's taxonomy of open source machine learning products and projects. Tensorflow and Pytorch are the largest deep learning frameworks, while Keras and Pandas are the most popular development libraries for building learning models. 
Picture
Data source: Linux Foundation

Importance of ML Technologies for Developers and Data Scientists

The Pandas machine learning library is the most important technology for software engineers, while Tensorflow and Keras are most critical for data scientists and data engineers. 
Data source: StackOverflow and StackExchange for Data Science

Most Popular Google Queries

Google searches focus most on MLOps tools, MLOps engineers, and MLOps on AWS and Azure. Searches for books, courses, and best practices are popular and indicate the desire of professionals to "jump aboard." DevOps is another essential MLOps-related Google search, as the synchronization between MLOps and DevOps is critical. 

MLOps in 25 Twitter Hashtags

This topic map is refreshed daily and contains the correlations that are closest to #MLOps on Twitter, using a standard topic clustering algorithm.
Data source: Twitter

Machine Learning Trends on Twitter

hashtags data by hashtagify.me
Data source: Google Trends

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  • Home
  • MLOps
    • EMA Top 3 Award Product Showcases >
      • Cisco Hyperflex
      • Cloudera Data Platform
      • Red Hat OpenShift
    • MLOps Topic Map
    • MLOps Research Facts
    • MLOps by Persona
    • Machine Learning Topic Map
    • Machine Learning Job Requirements
    • MLOps Quotes from the Trenches
  • Observability
  • Products to Watch
    • EMA Products to Watch - Infrastructure as Code - Pulumi
  • EMA Research Facts
    • Trends 2022
    • Observability
    • Machine Learning Research Facts
    • Multi-Cloud
    • Hybrid Cloud
    • Cost Challenges
    • Site Reliability Engineering
    • Kubernetes
    • Digital Transformation
  • Machine Learning for Kids
    • BERT for Kids
    • Evolution
    • NLP-Jurassic-1
  • FAQ