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cloudml vs mapsf

A side-by-side editorial comparison of cloudml and mapsf — release velocity, themes, recent moves, and the top alternatives to consider.

cloudml vs mapsf: at a glance

Featurecloudmlmapsf
SectorAnalyticsAnalytics
Velocity score0.00.0
Sparks · 30d00
Top themesmachine-learning, google-cloud, tensorflow, model-trainingcartography, thematic-maps, spatial, base-graphics
Last editorial update40m ago2h ago
WebsiteVisit →Visit →

What is cloudml?

Six years since the last functional change, and Google renamed the service it wraps in the release before that

cloudml lets R users train keras, tfestimators and tensorflow models on Google's managed machine learning service, tune hyperparameters there, and deploy the results. Its last release with functional content was 0.6.1 in September 2019, which adapted to Google renaming the service from ml-engine to ai-platform. The only entry since is a 2025 documentation update made to satisfy CRAN.

Read the full cloudml trajectory →

What is mapsf?

Thematic mapping in base R that finally got a theming system, then spent two years polishing legends.

mapsf produces thematic maps on R's base graphics device — choropleths, proportional symbols, typology maps, rasters, and their combinations, with legends, scale bars, north arrows, and insets as composable elements. Version 1.0.0 was the structural release, introducing a theming system that deprecated eight scattered styling arguments and adding mf_png() and mf_svg() export helpers plus alpha transparency across map types. The 1.1.x and 1.2.x line since then has been steady refinement: background and extent control on the drawing functions, decimal and thousands-separator control in legends, and label placement arguments.

Read the full mapsf trajectory →

cloudml vs mapsf: editorial side-by-side

C
cloudml
ANALYTICS
0.0

Six years since the last functional change, and Google renamed the service it wraps in the release before that

◆ Current state

cloudml lets R users train keras, tfestimators and tensorflow models on Google's managed machine learning service, tune hyperparameters there, and deploy the results. Its last release with functional content was 0.6.1 in September 2019, which adapted to Google renaming the service from ml-engine to ai-platform. The only entry since is a 2025 documentation update made to satisfy CRAN.

◆ Where it's heading

The visible arc is short and stops abruptly. Releases through 2018 tracked the TensorFlow runtime version and patched packaging problems; 0.6.1 added a customCommands hook so users could run OS-level setup before package installation, and adjusted to the service's new name. Then nothing for six years. A 2025 release containing only documentation changes is the standard signal of a package being kept on CRAN rather than being developed.

◆ Prediction

There is nothing in this feed to support a prediction of functional work. The most likely next event is another CRAN-driven documentation patch, or archival.

M
mapsf
ANALYTICS
0.0

Thematic mapping in base R that finally got a theming system, then spent two years polishing legends.

◆ Current state

mapsf produces thematic maps on R's base graphics device — choropleths, proportional symbols, typology maps, rasters, and their combinations, with legends, scale bars, north arrows, and insets as composable elements. Version 1.0.0 was the structural release, introducing a theming system that deprecated eight scattered styling arguments and adding mf_png() and mf_svg() export helpers plus alpha transparency across map types. The 1.1.x and 1.2.x line since then has been steady refinement: background and extent control on the drawing functions, decimal and thousands-separator control in legends, and label placement arguments.

◆ Where it's heading

The package has been consolidating control into fewer, more consistent places. Legend handling moved out to the maplegend package in 0.8.0 and the per-element mf_legend_* functions were deprecated in favor of arguments on the map calls themselves; theming replaced ad-hoc style arguments in 1.0.0; and recent releases keep propagating the same argument vocabulary — bg, extent, leg_val_rnd, leg_val_dec, leg_val_big — across every function that should accept it. Determinism is a visible concern too, with 1.2.1 fixing a seed so mf_distr() point positions stop moving between runs.

◆ Prediction

The recent releases are almost entirely argument-parity work across existing functions, so expect that to continue until the vocabulary is uniform rather than any new map type appearing.

Alternatives to cloudml and mapsf

Other Analytics products tracked by Sparkpulse, ranked by recent ship velocity. Each card links to a full editorial trajectory and lets you pivot into a head-to-head comparison with either cloudml or mapsf.

See all cloudml alternatives → · See all mapsf alternatives →

Recent activity from cloudml and mapsf

Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.

  1. 1mo agomapsfLabel placement arguments and deterministic distribution plots
  2. 2mo agomapsfBackground and extent control across the drawing functions
  3. 7mo agomapsfPNG resolution control and legend number formatting
  4. 0y agocloudmlDocumentation updated for CRAN
  5. 1y agomapsf1.0.0 introduces theming and deprecates eight style arguments
  6. 1y agomapsfPencil-sketch layers, ckmeans breaks, and border extraction
  7. 2y agomapsfGraticule label display fix
  8. 6y agocloudmlai-platform command adopted; custom pre-install commands added
  9. 7y agocloudmlDefault runtime moves to TensorFlow 1.9
  10. 8y agocloudmlPatch for CRAN results and a packrat error
  11. 8y agocloudmlCloud training, GPU jobs, tuning and deployment from R

Frequently asked questions

What is the difference between cloudml and mapsf?

They serve adjacent needs but don't currently overlap on shipped themes. cloudml and mapsf are shipping at a similar cadence (velocity 0.0 vs 0.0, both within Sparkpulse's "active" band). See the at-a-glance table above for a side-by-side breakdown of velocity, recent sparks, and editorial themes.

Is cloudml better than mapsf?

Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. cloudml and mapsf are shipping at a similar cadence (velocity 0.0 vs 0.0, both within Sparkpulse's "active" band). For your specific use case, the alternatives sections above list other Analytics products to evaluate alongside.

What are the best alternatives to cloudml?

Top cloudml alternatives in Analytics are ranked by recent ship velocity. Browse the "cloudml alternatives" section above for the current picks, or visit /alternatives/cloudml for the full list with editorial commentary on each.

What are the best alternatives to mapsf?

Top mapsf alternatives in Analytics are ranked by recent ship velocity. Browse the "mapsf alternatives" section above for the current picks, or visit /alternatives/mapsf for the full list with editorial commentary on each.