What's going on in AI?????
Hi LinkedIn Fam,
Can you believe we’re almost at the end of February ??? Neither can we, felt like it was just yesterday when we released our first newsletter edition.
Starting this month, we will be sending a monthly newsletter featuring a compilation of our top LinkedIn posts and interesting articles we come across on the internet.
Follow us on LinkedIn and subscribe to our newsletter for exclusive content and updates. Let's dive into the exciting future of Data, AI, ML and MLOps together.
But first…
Thank you everyone for hanging in with us for the past 5 years ??
It wouldn’t have been possible without our clients, partners and our amazing team!
Our LinkedIn Roundup
?? AI Opportunity Radar by Gartner
Gartner's AI Opportunity Radar outlines 4 zones of AI use cases based on impact and application, urging enterprises to consider how AI can enhance operations and competitive advantage, whether in everyday productivity or game-changing innovation, for both internal operations and external customer-facing services. The framework prompts businesses to define their AI ambitions, make strategic investments across different zones, and emphasises the importance of consistent, transformative initiatives to stay competitive in the evolving landscape of AI adoption.
Having trouble navigating the AI Opportunity Radar?
Check out the full post and connect with us for a deeper dive into your AI roadmap.
?? More 3-Letter-Acronyms in AI
Every industry has its compilation of TLAs (three-letter acronyms), and Matt Turck posted a brilliant tweet around this time last year. You'd think that a year later things would get better, but it has all gone berserk ??.
New papers are coming out each week and even on the application side, it can get overwhelming to try out all the new ways to optimise [insert one of your many metrics]. It's tough to decide whether it's necessary to build or just wait for a few weeks for some other smart person to destroy your invention.
If you're struggling - don't worry. We got you.
Book a slot with us here for a free 30-minute consultation ???to help you navigate your AI and GenAI future.
???? Insightful Info Bits on Model Deployment
Discover our recently published Info Bits, addressing the challenge of model deployment in AI. This Handbook for ML Deployment breaks down ML deployment into manageable steps, providing practical insights and considerations.
We still have four parts coming later on, but in the interim, you can read all of our past Info Bits here ??
Interesting Reads ??
Let’s look into the latest trends in AI with our curated selection of articles and podcasts. We’re going to share something around data, something about AI/ML, something about MLOps and of course, something about Gen AI.
?? Is the "Modern Data Stack" Still a Useful Idea?
What It Says: The term "modern data stack" was coined in 2016 to describe analytics products designed for the cloud that interacted via SQL. However, as most products are now cloud-native, the term has lost its usefulness. Initially a descriptive term, it gained popularity and fostered an ecosystem. Nevertheless, market shifts and the emergence of AI as a trend have diminished its relevance.
Why It Matters:
The term "modern data stack" is constantly evolving, and this analysis prompts data professionals to revisit the assumptions and trends that have guided their work so far.
By critically reassessing these foundations, data professionals can ensure they are tackling the most relevant problems and utilising the most effective approaches in today's ever-changing landscape. This ultimately positions them to deliver solutions that truly address the needs of the present.
What We Think: While the "modern data stack" term may no longer be descriptively useful, enterprise clients are still looking for integrated, interoperable solutions that work seamlessly together to replace outdated on-prem tools. Focusing procurement on best-of-breed categories alone risks losing the end-to-end vision the MDS ecosystem provided. For these industries in transition, a renewed emphasis on collaboration between vendors could help accelerate transformation at scale.
?? Automate the insurance claim lifecycle using Agents and Knowledge Bases for Amazon Bedrock
What It Says:
The article showcases how generative AI agents, exemplified by Amazon Bedrock, revolutionise businesses by enhancing efficiency, cutting costs, and enabling smarter decision-making.
It emphasises the automation of routine tasks, allowing employees to focus on strategic responsibilities. The blog showcases a practical example involving streamlining insurance claims with Amazon Bedrock-powered agents. Overall, it provides a concise overview of how these agents, especially on platforms like Amazon Bedrock, transform business operations.
Why It Matters:
Agents and Knowledge Bases are the two biggest concepts that are transforming the LLM space from “that’s cute” to saving me 20% of time on the job. This is a great example of applying Generative AI in a real-world scenario using agents and knowledge bases to reduce hallucination and leverage automation.
领英推荐
What We Think:
If you are not thinking about how to increase efficiency within your organisation with this architecture - you should. Start by having just the knowledge base interfacing with your internal team to reduce the risk. Once tested internally then iterate, improve and scale it out ??.
????? MLOps Landscape in 2024: Top Tools and Platforms
What It Says: This article provides an overview of the MLOps and LLMOps (FMOps) landscape in 2024, describing over 90 tools and platforms across different categories like experiment tracking, model deployment, and infrastructure.
Key end-to-end MLOps platforms like Amazon SageMaker, Microsoft Azure ML, and Google Cloud Vertex AI are highlighted. Factors to consider when evaluating MLOps tools and platforms are also discussed.
Why It Matters:
The article explores both open-source and closed-source tools, shedding light on their distinctive features and significant contributions to the field. There are clear pros and cons in using open-source and closed-source tools and technologies to support the MLOps process. Right now, the proliferation of these tools is so vast that you need to think about the benefits they bring and how well they can integrate into your existing workloads.
What We Think:
A flexible and scalable AI/ML platform is crucial for businesses aiming to leverage their data assets in the future. A post like this gives a great overview of what’s out there, but it can still feel incredibly overwhelming when you feel like you have to pick 1 from the haystack. From ML 1.0 to Generative AI, not only did each existing tool expand its offerings and go through a rebranding, but a swarm of new GenAI tools appeared.
If you need some help working through your ML workloads and architecture, take a look at our FLUID maturity assessment framework below ??
?? A Message from Melio AI
With our FLUID Framework, you will get a detailed review of your data and machine learning workloads. The report uncovers major risk factors and mitigation strategies for your project or technology landscape. This enables your team to plan and deliver with confidence. Download the info pack here
?? AI Tidbits 2023 SOTA Report
What It Says:
In the span of just one year, the AI landscape witnessed remarkable progress. Notable events included the emergence of ChatGPT, Anthropic's release of Claude, and Microsoft's unveiling of the first zero-shot voice cloning model.
Over 1,100 curated announcements and papers were featured in AI Tidbits in 2023 alone, highlighting the rapid pace of innovation.
This article explores what the state-of-the-art is today compared to December 2022 across various generative AI verticals.
What We Think:
Advancements in generative AI and state-of-the-art technologies are transforming business models and capabilities across all organisations. AI is no longer restricted to enterprises but is now accessible to individuals with some technical knowledge and a great. It is crucial to understand current state-of-the-art technologies to anticipate future norms.
Embracing generative AI technologies will give companies a competitive advantage in the coming year. Soon, these technologies will become essential components of products and services. This shift will resemble the integration of Google Maps and Apple Maps as default apps in smartphones and tablets, which disrupted the market advantage previously held by companies like Garmin.
?? Operationalise LLM Evaluation at Scale using Amazon SageMaker Clarify and MLOps services
What It Says:
LLMs have emerged as powerful tools with applications ranging from conversational agents to content generation. The responsible and effective use of LLMs requires thorough evaluation to measure quality, mitigate risks of misinformation, biased content, and unethical generation, and enhance security against data tampering.
This blog focused on how to automate and operationalise LLM evaluation at scale using Amazon SageMaker Clarify LLM evaluation capabilities and Amazon SageMaker Pipelines. In addition to theoretical architecture designs, there is an example code with both Llama2 and Falcon-7B FMs.
Why It Matters:
LLM applications are particularly prone to spreading bias and misinformation (and convincing unsuspecting humans with their confidence). Both providers and consumers need to ensure that the outputs of LLMs align with their specific requirements, costs, and performance expectations. By implementing a strong monitoring and evaluation framework, consumers of these models can proactively identify and address any regression, thus ensuring the continued effectiveness and reliability of these models over time.
What We Think:
We are at the dawn of rapid development and adoption of generative AI and LLM-based applications. It’s easy to dive into some of these attractive technologies. We should, however, take a step back and make sure not only are these solutions sustainable, secure, reliable and performant, but the answers that they provide do not create harmful or malicious results.
At Melio we believe in long-term thinking in how we build our AI-based solutions. Incorporating sound evaluation techniques into the model / LLM lifecycle is not only important but critical in providing businesses and users with a level of trust that is currently quite opaque.
Conclusion
These articles are a subset of what we are sharing within our team. As Melio’s core value, we're always on the lookout for intriguing insights and perspectives.
If you have any fascinating articles or topics you'd like to share, join us in the comment section below.
Melio AI, Making AI Frictionless!
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?? Melio AI, 24 Cradock Avenue, Rosebank, Johannesburg, 2196
Co-Founder & CEO @ Melio AI ?? MLOps Evangelist ? Building AI Marketplace | Making AI Frictionless ??
8 个月Cool round-up of what's up in the Data, ML and AI space. So. Much. Is. Happening ??