Online self-learning courses offer autonomy, allowing individuals to learn at their own pace. They provide structured training materials with review exercises to enhance understanding. Utilizing multimedia resources like videos and presentations, learners actively engage with the content. while flexibility enables customization of study schedules. This fosters an environment conducive to effective learning and skill development, accommodating personal commitments.
The response from the AI model would be available within seconds. But can it scan through thousands of pages of documents at the firm and provide an appropriate response? This requires more than just a prompt-based course. You need knowledge of data engineering, language models, and intelligent retrieval.
Through this Generative AI Online Training by GoLogica Databricks, participants get insight on the technology required for building enterprise AI applications using Large Language Models (LLM), Retrieval-Augmented Generation (RAG), and Databricks. It provides participants with basic knowledge of the integration of AI models with real-life data and app development techniques.
Some of the subject matter covered includes Databricks Lakehouse concepts, Python Notebooks, Foundation models, and prompt engineering. The key concepts covered in this learning are text embeddings, document chunking, semantic search, and Mosaic AI Vector Search.
MLflow, model evaluation, Mosaic AI Model Serving, and AI agent workflows are also covered under the learning path. Participants study ways of monitoring model behavior, enhancing retrieval precision, minimizing unhelpful responses, and deployment requirements.
Examples of learning in Generative AI include the creation of a document question-answering system, enterprise knowledge assistants, and Generative AI Chatbots. The above-mentioned applications showcase Generative AI's capability in knowledge retrieval, document analysis, and business processes.
GoLogica provides learners with live training with instructors, hands-on learning, practical project-based learning, and guidance on Generative AI. This Databricks Generative AI Certification Course is beneficial for Python developers, Data Engineers, Data Scientists, Machine Learning Engineers, and Cloud Engineers looking to develop practical skills in AI.
Python programming, knowledge of SQL, and the basics of Machine Learning are necessary. Individuals having prior experience in Databricks can use this course to learn advanced AI Development techniques.
Since organizations are implementing AI-powered search, automated knowledge management, and intelligent assistants, professionals having knowledge of LLM integration, RAG architectures, and model evaluation can explore opportunities in AI Engineering.
Are you ready to transform theoretical AI knowledge into practical skills? Enroll in Databricks Generative AI Online Training at GoLogica and find out about available batches today!
Salary Trends:
According to Glassdoor and market salary reports, Databricks Generative AI professionals in India earn approximately 8 to 15 LPA, while US-based Generative AI Engineers earn around $118,000 to $185,000 annually. Actual salaries depend on experience, technical skills, expertise in LLMs, and enterprise project exposure.
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Databricks Generative AI Curriculum
Introduction to Generative AI and Agentic AI.
Understanding Large Language Models (LLMs).
Databricks Lakehouse and Mosaic AI overview.
AI agents, architecture, and enterprise use cases.
Databricks workspace and environment configuration.
Notebooks, clusters, and serverless compute.
Python, SQL, and PySpark fundamentals.
Delta Lake and Unity Catalog.
Transformer architecture, tokens, and embeddings.
Foundation Models and Model APIs.
Working with open-source and proprietary LLMs.
Model selection, inference, and optimization.
Prompt design and optimization techniques.
Zero-shot and few-shot prompting.
Structured outputs and function calling.
Working with Databricks AI Playground.
Introduction to Mosaic AI Gateway.
Model endpoint configuration and management.
Model routing, rate limits, and access controls.
LLM monitoring and usage tracking.
RAG architecture and implementation.
Document ingestion and preprocessing.
Chunking, embeddings, and semantic search.
Context retrieval and response generation.
Building enterprise knowledge assistants.
Vector Search architecture and configuration.
Creating and managing vector indexes.
Delta Sync and Direct Vector Access indexes.
Similarity search, metadata filtering, and hybrid search.
Integrating Vector Search with RAG.
Introduction to Databricks Agent Bricks.
Building Knowledge Assistants.
Connecting enterprise data sources.
Configuring agent instructions and tools.
Testing and optimizing AI agents.
Databricks Agent Framework.
Building AI agents using Python.
LangChain, LangGraph, and OpenAI Agents SDK.
Custom tools, function calling, and REST API integration.
Agent state and conversation management.
Single-agent and multi-agent architectures.
Supervisor and worker agent patterns.
Agent orchestration and task delegation.
Model Context Protocol (MCP) integration.
Unity Catalog functions and governed tools.
Introduction to Databricks Genie.
Creating and configuring Genie Spaces.
Natural language to SQL generation.
Connecting enterprise data sources.
Integrating Genie with AI agents.
Introduction to MLflow 3 for GenAI.
Agent tracing and experiment tracking.
Evaluating response quality and accuracy.
LLM judges and human feedback.
Monitoring latency, token usage, and performance.
Unity Catalog governance and security.
Authentication, authorization, and guardrails.
Deploying agents using Databricks Agent Framework.
Agent Server and Databricks Apps.
Production monitoring and cost optimization.
The GoLogica certification is widely acknowledged, enhancing the credibility of your resume and opening doors to high-level positions in leading multinational corporations globally.
At the end of this course, you will receive a course completion certificate which certifies that you have successfully completed GoLogica training in Databricks Generative AI technology.
You will get certified in Databricks Generative AI by clearing the online examination with a minimum score of 70%.
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Databricks Generative AI Objectives
LLMs, RAG, vector search, prompt engineering, and MLflow are the elements of this training. The focus of this program is to build applications based on generative AI solutions that can work with business data.
It is the combination of data engineering and machine learning development. It is needed to build applications that search company databases, process documents, and generate answers.
Yes, for people with basic knowledge of Python. It would be nice to have some experience of working with data to start with the RAG pipeline and model integration.
Knowledge of Python and SQL basics is required to start learning. Knowledge of machine learning, APIs, and cloud technologies would be useful.
Machine learning is about prediction and recognition of patterns or outcomes. Generative AI is about generating content like text or code. Both approaches are supported by Databricks development tools.
LLMs, RAG, and Technical Skills
RAG identifies similar data first and generates an answer afterwards inside the AI model. It is particularly helpful for chatbots which are supposed to respond using company documentation.
Mosaic AI Vector Search searches for information based on its meaning rather than on literal word matchings. Hence, it is appropriate for use in document searches and RAG.
The term 'prompt engineering' means creation of prompts for an AI model. Properly designed prompts lead to better answers from the model.
Embeddings are conversion of texts or any other data to numbers. This helps to understand similarities and find similar information even when different words are used.
LLM integration is one of the recommended topics of the Program. It implies integration of language models into a Python app, API, and business knowledge.
MLflow is helpful to monitor experiments, observe application behaviors, and analyze results. It is used by developers to find errors and optimize AI application performance.
The course covers LangChain which is useful in connecting prompts, language models, and retrieval tools during AI application creation.
Mosaic AI Model Serving helps the developer deploy models with the help of serving endpoints that are then used by applications to ask for predictions.
The advanced topics include AI agents and agentic workflows that show how AI applications can search for data, utilize tools, and perform multiple tasks.
Hallucinations can be reduced by using reliable documents, good prompts, and tests, although there may still occur some mistakes despite all precautions.
Practical Projects and Applications
You can develop document search applications, RAG chatbots and knowledge assistants. Please consult with GoLogica about your particular project options within your training program.
Yes, because Databricks allows us to use data processing, document searches, and AI modeling services which will help us build chatbots for our business.
Python can be used to process documents, to integrate with APIs and build AI workflows. Also, it is helpful in generating embeddings and evaluating model outputs.
Databricks allows for working with documents, identifying relevant passages and connecting them with language models, thereby allowing applications to answer questions based on available company documents.
AI applications might seem convincing but provide inaccurate answers to user's questions. That's why model evaluation is essential during development.
Certification and Career Opportunities
Yes. There is a certification from Databricks named Certified Generative AI Engineer Associate. You should look at the official website for more information.
You can see that proposed topics contain rather much technical material, including RAG, LLM and evaluation. You should compare it with official objectives for certification.
Generative AI Engineer, RAG Developer, LLM Application Developer and AI/ML Engineer could be the positions. It depends on your practical knowledge.
Yes. Data engineers will be able to use their skills to prepare documents and data pipelines for applications using the enterprise data.
ChatGPT is an application working through interaction with user. Databricks Generative AI is associated with creation of applications using language models and data.
Online Training and Enrollment
You can get in touch with GoLogica to know the details regarding the availability of online Databricks Generative AI Program, teaching mode, and batches.
The duration of the Program will depend on the course format and the topics being covered. GoLogica can help you with more information regarding the same.
GoLogica can be contacted for more information about the fees related to the Databricks Generative AI program, the batches and the learning resources.
It will be advisable to enquire about it with GoLogica. It might depend upon the Course package that you opt for.
Contact GoLogica to know more about Databricks Generative AI Training Program, the available batches and make a start towards AI development.
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