Artificial intelligence and machine learning have been the buzzwords for quite some time. One of the most important concepts in AI is the Large Language Model – essential to power the horde of Generative AI tools in the market, and more. Large Language Models or LLMs have transformed the AI space and continue to do so. LLMs offer incredibly powerful tools and utilities for improving workflows as well as boosting productivity across domains.
As a current or aspiring AI & ML professional which tools should you be absolutely fluent with? Read on to discover the top five tools every AI & ML professional must know.
LlamaIndex
LlamaIndex is an advanced data orchestration framework that facilitates the integration of diverse data sources with Large Language Models. Earlier, LlamaIndex was called the GPT Index. The tool works well with both private as well as public data, including APIs, SQL databases, NoSQL databases, and PDFs. Both structured and unstructured data can be easily integrated using LlamaIndex. LlamaIndex uses different indexing techniques and models for efficient data organization as well as retrieval. LlamaIndex also supports natural language querying to interact with data which boosts the utility of the LLM, especially when used in chatbots and knowledge agents.
Ollama
Ollama is another important LLM tool that every AI & ML professional would find handy. It is designed to run open-source LLMs locally. An advantage of using Ollama is that it supports a wide variety of LLMs including Llama 2, Mistral, and Code Llama. Ollama is helpful for users to access advanced AI capabilities without relying on cloud services. Because Ollama is executed directly on the device, it offers better privacy and data control, while reducing latency as well as dependency on external servers. If you have sensitive applications, using Ollama would be a no-brainer. Ollama also offers a simplified setup process since it packages model weights, configurations, and datasets into one package which is defined by Modelfile. This way, when the model is deployed, all components are easily accessible. Additionally, Ollama can be easily integrated into different programming languages and frameworks, making developers’ lives much easier when they want to integrate the LLM into their applications. Real-time interactions using REST API are also supported.
Hugging Face Transformers
Hugging Face Transformers is a comprehensive library for natural language processing and machine learning tasks. It offers the best pre-trained models that can be easily deployed in applications, per requirements. This makes it an incredibly popular tool among the developer as well as the research community. It supports a diverse range of tasks including text classification, named entity recognition, question answering, language modeling, translation, text generation, and summarization. Hugging face transformers also support multimodal capabilities. It can be easily integrated with all major machine learning frameworks including PyTorch, TensorFlow, and JAX. One can even train the model in one framework and then deploy it in another with ease.
NotebookLM
NotebookLM is an AI-powered research and note-taking tool developed by Google Labs. It is designed to help its users gain valuable insights from different sources like their documents. Earlier, NotebookLM was known as Project Tailwind and it later got renamed. It works as a virtual research assistant that can summarize facts, explain concepts, and even discuss ideas based on content that the users provide. Instead of providing content as text, users can also upload documents in various formats for this. NotebookLM will offer audio summaries too that users can listen to like podcasts. It is a dynamic platform enabling users to ask questions and get detailed answers about the content or documents they provide. It is an excellent tool to make collections of different AI-generated artifacts like sources, chats, summaries, FAQs, study guides, etc.
ControlFlow
ControlFlow is a Python framework. It has been developed by Prefect and is used for building agentic workflows. Using ControlFlow, one can create structured, task-oriented workflows that leverage the large language models for automating complex processes while offering complete control and transparency. ControlFlow enables users to assign different tasks to autonomous AI agents so they can make the required decisions while also performing complex tasks as part of larger, more complex workflows. Once the tasks are assigned, ControlFlow also offers top-notch observability features that enable one to monitor & track the decision-making that takes place at the AI agent end. This makes the process of managing AI tasks more flexible as well as more efficient. The tool breaks each workflow into smaller tasks, making the process easier to monitor and debug. Additionally, ControlFlow can easily be integrated into existing tools and machine learning frameworks. So, all the current tech stacks the teams may have can be incorporated without any downtime or disruption.
These are the top five tools that make the lives of every AI and ML professional easier, enabling them to perform with great efficiency & efficacy. Learning these tools can help one excel in their career as an AI-ML professional.
Learn About Working with Large Language Models with Cognixia
Cognixia’s Working with Large Language Models live instructor-led online training and certification course has been designed to offer an in-depth understanding and extensive hands-on experience working with a spectrum of Generative AI models, including Txt2Txt, Img2Img, Multimodal, as well as advanced models like PaLM 2. Large language models are becoming increasingly powerful. It is commonplace now to deploy LLMs to power Generative AI models. They can recognize, summarize, translate, predict, and generate any content as they are trained on very large extensive data sets. Cognixia’s Large Language Models course delves into the intricate world of LLMs, discussing a broad array of topics from unimodal mapping to the advanced features of MakerSuite. This large language model online course is designed to offer learners an in-depth understanding & experience of working with a spectrum of Generative AI models including Txt2Txt, Img2Img, multimodal, and PaLM 2.
The Working with Large Language Models online training & certification course covers:
- Introduction to Txt2Txt Gen AI model
- Exploring statistical language models
- Neural language models
- SLM and PLM in Python and Keras
- Deep dive into Seq2Seq models
- Exploring Hugging Face Transformer pipelines
- Transfer learning in natural language processing
- GPT 3.5 vs. GPT 4
- ChatGPT and OpenAI API
- ChatGPT clone in Google Colab and Streamlit
- Introduction to Img2Img Gen AI model
- Exploring Variational Auto-Encoder
- Coding AE in Keras
- Training GANs
- Introduction to Multimodal GenAI models
- Exploring Clip Drop and Stable Infusion
- LeonardoAI, Midjourney, and OpenAI Dall-E
- Txt2Voice Generation – Evenlabs
- Introduction to PaLM 2
- Compute optimal scaling and model architecture
- Exploring Gemini and PaLM API
- PaLM API in Vertex AI
- Introduction to MakerSuite
- MakerSuite advanced features
To be eligible for this online LLM course, one needs to have:
- Understanding of Machine Learning Concepts
- Deep Learning fundamentals
- Experience with NLP techniques
- Programming skills
- Hands-on experience with data handling
- Background in mathematics
- Solid understanding of hardware
