Eval Frameworks. 7 AI in the EnterpriseEvals defined Evaluation is the process of validating and testing the outputs that your models produce. Rigorous evals lead to more stable, reliable applications support. These results didn’t happen overnight. Klarna achieved this performance by continuously testing and refining the assistant. Just as importantly, 90% of Klarna’s employees now use AI in their automate workflows that previously required human intervention, such as: Automating software testing and QA using Operator to interact with web apps 
 like a real user, flagging any UI issues. Updating

packages import numpy as np from tvm import relay 2. Load a pretrained network mod, params = relay.testing.mobilenet.get_workload(batch_size=1) 3. Partition and build the network with an external codegen your_codegen_name>/graph_annotator.py ● Apply the annotator to a workload: mod, params = relay.testing.mobilenet.get_workload(batch_size=1) mod[‘main’] = MyAnnotator().visit(mod[‘main’]) mod = relay.build_extern(mod

Focus – Global CMOs = 75% Using / Testing AI Tools % of Survey Responses 0% 25% 50% 75% Plan on Start Testing Within 1-2 Years Fully Implemented Plan on Start Testing Within 12 Months Running Initial Ark Design AI AI Developer Use Cases – 2024, per IBM Code Generation Bug Detection & Fixing Testing Automation Project / Workflow Management Documentation Refactoring & Optimization Security

for compressive force (shell strength)following Burnett and Belk (2018). A universal material+testing machine(MTS System Corporation, Eden Prairie, MIN, USA, Model 661; Fig1,)was used to determine for compressive force (shell strength)following Burnett and Belk (2018). A universal material-testing machine (MTS System Corporation, Eden Prairie, MN, USA, Model 661; Fig. 1) was used to determine

flexibility with the runtime plugins? ○ Integrate TVM codegen into Arm NN? ● CI and benchmark testing for TVM on member hardware platforms ○ Shall we maintain a list of Arm platforms supported by TVM

Engineering February 2025 60 A good rule of thumb is to start with 6 few shot examples and start testing the accuracy from there. Adapt to model updates It’s important for you to stay on top of model architecture

Length (#Tokens) 0 9 18 27 36 45 55 64 73 82 91 100 Document Depth Percent (%) Pressure Testing DeepSeek-V2 Base 128K Context via "Needle In A HayStack" 1 2 3 4 5 6 7 8 9 10 Score Figure