Make a few of the images larger as requested during docs review (#3757)

Summary:
Make a few of the images larger as requested during docs review

# New image:
<img width="1480" alt="image" src="https://github.com/user-attachments/assets/20dd8201-0a79-4b4d-a826-318e24695979" />
<img width="1481" alt="image" src="https://github.com/user-attachments/assets/95f1791e-dcd1-46d2-99db-2d36024b51b4" />

# Old image:
<img width="1486" alt="image" src="https://github.com/user-attachments/assets/b22debb8-8990-4e5e-af0c-092d9e0d9aaa" />
<img width="1491" alt="image" src="https://github.com/user-attachments/assets/7c61001a-5a05-4471-9166-cb91285c384e" />

Pull Request resolved: #3757

Reviewed By: mpolson64

Differential Revision: D74422533

Pulled By: CristianLara

fbshipit-source-id: e29ab7bc5ca22bc5bdd46e5d433c37b3918e125e

Author: CristianLara

docs/assets/traditional_vs_adaptive.png

@@ -51,7 +51,7 @@ methods like `Client.get_next_trials` and `Client.complete_trial`), and
 optionally an `Orchestrator` with `Client.run_n_trials`. The iterative process
 of using the `Client` looks like this in more detail:
 
-<center><img src={ask_tell_flowchart} alt="Using Ax for 'ask-tell' optimization" width="60%" /></center>
+<center><img src={ask_tell_flowchart} alt="Using Ax for 'ask-tell' optimization" width="80%" /></center>
 
 We recommend avoiding interacting with the `Experiment` object directly unless
 developing Ax internals (opting to interact with it through `Client` instead),

docs/assets/traditional_vs_adaptive.svg

@@ -3,7 +3,7 @@ id: intro-to-ae
 title: Introduction to Adaptive Experimentation
 ---
 
-import Traditional_vs_Adaptive from './assets/traditional_vs_adaptive.svg';
+import traditional_vs_adaptive from './assets/traditional_vs_adaptive.png';
 
 # Introduction to Adaptive Experimentation
 
@@ -38,7 +38,7 @@ The basic adaptive experimentation flow works as follows:
 4. **Repeat** steps 2 and 3 until a stopping condition is met or the evaluation
    budget is exhausted
 
-<center><Traditional_vs_Adaptive alt="Traditional vs. Adaptive design" width="60%" height="auto"/></center>
+<center><img src={traditional_vs_adaptive} alt="Traditional vs. Adaptive design" width="50%" /></center>
 
 Bayesian optimization, one of the most effective forms of adaptive
 experimentation, intelligently balances tradeoffs between exploration (learning

docs/experiment.mdx

@@ -61,7 +61,7 @@ evaluation system:
 
 ## `Metric`s and `Runner`s
 
-<center><img src={metric_runner_flowchart} alt="Metrics and Runners allow Ax to interface with external deployment systems" width="60%" /></center>
+<center><img src={metric_runner_flowchart} alt="Metrics and Runners allow Ax to interface with external deployment systems" width="70%" /></center>
 
 `Runner`-s and `Metric`-s typically exchange information about trial deployment
 via "run metadata" dictionary (e.g. `{"job_id": 12345}`), captured on Ax
@@ -78,7 +78,7 @@ implementing the `IRunner` and `IMetric` interfaces, each requiring only one or
 two methods. Adding it to a previously "ask-tell" experiment with steps 1-4,
 adds steps 5-7 below:
 
-<center><img src={closed_loop_flowchart} alt="Using the Orchestrator from the Client" width="60%" /></center>
+<center><img src={closed_loop_flowchart} alt="Using the Orchestrator from the Client" width="80%" /></center>
 
 More trials can always be run an ask-tell fashion after the Ax-orchestrated ones
 run via `Client.run_trials`.