After Google I/O I’m inspired and excited to try new APIs and learn new stuff from Google. This time I decided to try Dialogflow and build a Flutter Chatbot app that will answer some frequently asked questions about Dialogflow. This time I want to be focused more on Dialogflow rather than Flutter. Firstly, go to Dialogflow ES console , create a new Agent, specify the agent’s name, choose English as a language and click “Create”. As you created a new agent go to setting and enable beta features and APIs and Save. Now let’s model our Dialogflow agent When you create a new Dialogflow agent, two default intents will be created automatically. The Default Welcome Intent is the first flow you get to when you start a conversation with the agent. The Default Fallback Intent is the flow you’ll get once the agent can’t understand you or can not match intent with what you just said. Click Intents > Default Welcome Intent Scroll down to Responses . Clear all Text Responses. In the defau
This year on Google I/O (Google’s Developer conference) Google presented a new platform that unites all ML tools. Vertex AI brings together the Google Cloud services for building ML under one, unified UI and API. There are many benefits to using Vertex AI. You can train models without code, with minimal expertise required, and take advantage of AutoML to build models in less time. Also, Vertex AI’s custom model tooling supports advanced ML coding, with nearly 80% fewer lines of code required to train a model with custom libraries than competitive platforms. Google Vertex AI logo You can use Vertex AI to manage the following stages in the ML workflow: Define and upload a dataset. Train an ML model on your data: Train model Evaluate model accuracy Tune hyperparameters (custom training only) Upload and store your model in Vertex AI. Deploy your trained model and get an endpoint for serving predictions. Send prediction requests to your endpoint. Specify a prediction traffic split in your