Once you've trained a model, the next step is to prepare it for deployment to production. Key steps here include: 1?? Serialise the model 2?? Containerise the model 3?? Version the model 4?? Register the model ??? Model serialisation refers to the process of converting a model from its in-memory representation (e.g., objects, data structures) into a format suitable for storage or transmission. It is important because it allows us to standardise the model format, making it easier to deploy and manage across different environments. ?? Model containerisation refers to the process of encapsulating the (often serialised) model and its dependencies within a container. The container includes everything needed to run the model, including the application code, runtime environment, libraries, and dependencies. ?? Model versioning is the process of assigning a unique identifier to each packaged model. This allows us to track changes to the model and ensure we can roll back to the older versions when things go wrong in production. ?? Model versions can become messy to handle, hence many teams use a model registry as a central hub for managing model versions, metadata, and deployment history. Popular model registries often tie the ML experimentations together: ? MLFlow Model Registry centralises metadata like versions, parameters and metrics for easy model tracking and lineage. ? ML Metadata (MLMD) tracks artefacts like datasets, models, executions of pipeline, and the relationships between them to enable lineage tracing and debugging of ML pipelines. Kickstarting your deployment journey begins with proper model packaging by serialising and containerising your models. Then version and register your models to pave the way for repeatable deployment. Next week up: How to Provision Infra & Serve Models #MLOps #ModelRegistry #metadatamanagement #AI #MachineLearning
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AI Engineer ? Follow me to Learn about AI Systems ? Author of SwirlAI Newsletter ? Public Speaker
What is ???????????????????? ???????????????? (????) in MLOps and what steps are needed to achieve it? CT is the process of automated ML Model retraining in Production Environments on a specific trigger. Let’s look into some prerequisites for this: 1) Automation of ML Pipelines. - Pipelines are orchestrated. - Each pipeline step is developed independently and is able to run on different technology stacks. - Pipelines are treated as a code artifact. ? You deploy Pipelines instead of Model Artifacts allowing Continuous Training In production. ? Reuse of components allows for rapid experimentation. 2) Introduction of strict Data and Model Validation steps in the ML Pipeline. - Data is validated before training the Model. If inconsistencies are found - Pipeline is aborted. - Model is validated after training. Only after it passes the validation is it handed over for deployment. ? Short circuits of the Pipeline allow for safe CT in production. 3) Introduction of ML Metadata Store. - Any Metadata related to ML artifact creation is tracked here. - We also track performance of the ML Model. ? Experiments become reproducible and comparable between each other. ? Model Registry acts as glue between training and deployment pipelines. 4) Different Pipeline triggers in production. - Ad-hoc. - Cron. - Reactive to Metrics produced in Model Monitoring System. - Arrival of New Data. ? This is where the Continuous Training is actually triggered. 5) Introduction of Feature Store (Optional). - Avoid work duplication when defining features. - Reduce risk of Training/Serving Skew. ???? ???????????????? ???? ????: ?? Introduction of CT is not straightforward and you should approach it iteratively. The following could be good Quarterly Goals to set: - Experiment Tracking is extremely important at any level of ML Maturity and the least invasive in the process of ML Model training - I would start with ML Metadata Store introduction. - Orchestration of ML Pipelines is always a good idea, there are many tools supporting this (Airflow, Kubeflow, VertexAI etc.). If you are not doing it yet - grab this next, also make the validation steps part of this goal. - The need for Feature Store will wary on the types of Models you are deploying. I would prioritise it if you have Models that perform Online predictions as it will help with avoiding Training/Serving Skew. - Don’t rush with Automated retraining. Ad-hoc and on-schedule will bring you a long way. Let me know your thoughts! ?? #LLM #MachineLearning #MLOps
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AI Engineer ? Follow me to Learn about AI Systems ? Author of SwirlAI Newsletter ? Public Speaker
What is ???????????????????? ???????????????? (????) in MLOps and what steps are needed to achieve it? CT is the process of automated ML Model retraining in Production Environments on a specific trigger. Let’s look into some prerequisites for this: 1) Automation of ML Pipelines. - Pipelines are orchestrated. - Each pipeline step is developed independently and is able to run on different technology stacks. - Pipelines are treated as a code artifact. ? You deploy Pipelines instead of Model Artifacts allowing Continuous Training In production. ? Reuse of components allows for rapid experimentation. 2) Introduction of strict Data and Model Validation steps in the ML Pipeline. - Data is validated before training the Model. If inconsistencies are found - Pipeline is aborted. - Model is validated after training. Only after it passes the validation is it handed over for deployment. ? Short circuits of the Pipeline allow for safe CT in production. 3) Introduction of ML Metadata Store. - Any Metadata related to ML artifact creation is tracked here. - We also track performance of the ML Model. ? Experiments become reproducible and comparable between each other. ? Model Registry acts as glue between training and deployment pipelines. 4) Different Pipeline triggers in production. - Ad-hoc. - Cron. - Reactive to Metrics produced in Model Monitoring System. - Arrival of New Data. ? This is where the Continuous Training is actually triggered. 5) Introduction of Feature Store (Optional). - Avoid work duplication when defining features. - Reduce risk of Training/Serving Skew. ???? ???????????????? ???? ????: ?? Introduction of CT is not straightforward and you should approach it iteratively. The following could be good Quarterly Goals to set: - Experiment Tracking is extremely important at any level of ML Maturity and the least invasive in the process of ML Model training - I would start with ML Metadata Store introduction. - Orchestration of ML Pipelines is always a good idea, there are many tools supporting this (Airflow, Kubeflow, VertexAI etc.). If you are not doing it yet - grab this next, also make the validation steps part of this goal. - The need for Feature Store will wary on the types of Models you are deploying. I would prioritise it if you have Models that perform Online predictions as it will help with avoiding Training/Serving Skew. - Don’t rush with Automated retraining. Ad-hoc and on-schedule will bring you a long way. Let me know your thoughts! ?? #LLM #MachineLearning #MLOps
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Engineering Lead & Manager @ JPMorgan Chase | Regulatory Modeling | AI/ML | Cloud Computing
Short and concise on Continuous Training (CT) in MLOps. #mlops
AI Engineer ? Follow me to Learn about AI Systems ? Author of SwirlAI Newsletter ? Public Speaker
What is ???????????????????? ???????????????? (????) in MLOps and what steps are needed to achieve it? CT is the process of automated ML Model retraining in Production Environments on a specific trigger. Let’s look into some prerequisites for this: 1) Automation of ML Pipelines. - Pipelines are orchestrated. - Each pipeline step is developed independently and is able to run on different technology stacks. - Pipelines are treated as a code artifact. ? You deploy Pipelines instead of Model Artifacts allowing Continuous Training In production. ? Reuse of components allows for rapid experimentation. 2) Introduction of strict Data and Model Validation steps in the ML Pipeline. - Data is validated before training the Model. If inconsistencies are found - Pipeline is aborted. - Model is validated after training. Only after it passes the validation is it handed over for deployment. ? Short circuits of the Pipeline allow for safe CT in production. 3) Introduction of ML Metadata Store. - Any Metadata related to ML artifact creation is tracked here. - We also track performance of the ML Model. ? Experiments become reproducible and comparable between each other. ? Model Registry acts as glue between training and deployment pipelines. 4) Different Pipeline triggers in production. - Ad-hoc. - Cron. - Reactive to Metrics produced in Model Monitoring System. - Arrival of New Data. ? This is where the Continuous Training is actually triggered. 5) Introduction of Feature Store (Optional). - Avoid work duplication when defining features. - Reduce risk of Training/Serving Skew. ???? ???????????????? ???? ????: ?? Introduction of CT is not straightforward and you should approach it iteratively. The following could be good Quarterly Goals to set: - Experiment Tracking is extremely important at any level of ML Maturity and the least invasive in the process of ML Model training - I would start with ML Metadata Store introduction. - Orchestration of ML Pipelines is always a good idea, there are many tools supporting this (Airflow, Kubeflow, VertexAI etc.). If you are not doing it yet - grab this next, also make the validation steps part of this goal. - The need for Feature Store will wary on the types of Models you are deploying. I would prioritise it if you have Models that perform Online predictions as it will help with avoiding Training/Serving Skew. - Don’t rush with Automated retraining. Ad-hoc and on-schedule will bring you a long way. Let me know your thoughts! ?? #LLM #MachineLearning #MLOps
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ex-Transunion, Capgemini, Siemens| Application Architecture , People Management, SAAS,Cloud,AI ML, Cybersecurity. PMP GCP-Cloud architect
#mlops #continuoustraining Thanks Aurimas Griciūnas for interesting article and animated graphics on continuous training in MLOPs and suggested best practice. It's useful recommendation
AI Engineer ? Follow me to Learn about AI Systems ? Author of SwirlAI Newsletter ? Public Speaker
What is ???????????????????? ???????????????? (????) in MLOps and what steps are needed to achieve it? CT is the process of automated ML Model retraining in Production Environments on a specific trigger. Let’s look into some prerequisites for this: 1) Automation of ML Pipelines. - Pipelines are orchestrated. - Each pipeline step is developed independently and is able to run on different technology stacks. - Pipelines are treated as a code artifact. ? You deploy Pipelines instead of Model Artifacts allowing Continuous Training In production. ? Reuse of components allows for rapid experimentation. 2) Introduction of strict Data and Model Validation steps in the ML Pipeline. - Data is validated before training the Model. If inconsistencies are found - Pipeline is aborted. - Model is validated after training. Only after it passes the validation is it handed over for deployment. ? Short circuits of the Pipeline allow for safe CT in production. 3) Introduction of ML Metadata Store. - Any Metadata related to ML artifact creation is tracked here. - We also track performance of the ML Model. ? Experiments become reproducible and comparable between each other. ? Model Registry acts as glue between training and deployment pipelines. 4) Different Pipeline triggers in production. - Ad-hoc. - Cron. - Reactive to Metrics produced in Model Monitoring System. - Arrival of New Data. ? This is where the Continuous Training is actually triggered. 5) Introduction of Feature Store (Optional). - Avoid work duplication when defining features. - Reduce risk of Training/Serving Skew. ???? ???????????????? ???? ????: ?? Introduction of CT is not straightforward and you should approach it iteratively. The following could be good Quarterly Goals to set: - Experiment Tracking is extremely important at any level of ML Maturity and the least invasive in the process of ML Model training - I would start with ML Metadata Store introduction. - Orchestration of ML Pipelines is always a good idea, there are many tools supporting this (Airflow, Kubeflow, VertexAI etc.). If you are not doing it yet - grab this next, also make the validation steps part of this goal. - The need for Feature Store will wary on the types of Models you are deploying. I would prioritize it if you have Models that perform Online predictions as it will help with avoiding Training/Serving Skew. - Don’t rush with Automated retraining. Ad-hoc and on-schedule will bring you a long way. Let me know your thoughts! ?? #LLM #MachineLearning #MLOps
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Interesting read!!
AI Engineer ? Follow me to Learn about AI Systems ? Author of SwirlAI Newsletter ? Public Speaker
What is ???????????????????? ???????????????? (????) in MLOps and what steps are needed to achieve it? CT is the process of automated ML Model retraining in Production Environments on a specific trigger. Let’s look into some prerequisites for this: 1) Automation of ML Pipelines. - Pipelines are orchestrated. - Each pipeline step is developed independently and is able to run on different technology stacks. - Pipelines are treated as a code artifact. ? You deploy Pipelines instead of Model Artifacts allowing Continuous Training In production. ? Reuse of components allows for rapid experimentation. 2) Introduction of strict Data and Model Validation steps in the ML Pipeline. - Data is validated before training the Model. If inconsistencies are found - Pipeline is aborted. - Model is validated after training. Only after it passes the validation is it handed over for deployment. ? Short circuits of the Pipeline allow for safe CT in production. 3) Introduction of ML Metadata Store. - Any Metadata related to ML artifact creation is tracked here. - We also track performance of the ML Model. ? Experiments become reproducible and comparable between each other. ? Model Registry acts as glue between training and deployment pipelines. 4) Different Pipeline triggers in production. - Ad-hoc. - Cron. - Reactive to Metrics produced in Model Monitoring System. - Arrival of New Data. ? This is where the Continuous Training is actually triggered. 5) Introduction of Feature Store (Optional). - Avoid work duplication when defining features. - Reduce risk of Training/Serving Skew. ???? ???????????????? ???? ????: ?? Introduction of CT is not straightforward and you should approach it iteratively. The following could be good Quarterly Goals to set: - Experiment Tracking is extremely important at any level of ML Maturity and the least invasive in the process of ML Model training - I would start with ML Metadata Store introduction. - Orchestration of ML Pipelines is always a good idea, there are many tools supporting this (Airflow, Kubeflow, VertexAI etc.). If you are not doing it yet - grab this next, also make the validation steps part of this goal. - The need for Feature Store will wary on the types of Models you are deploying. I would prioritize it if you have Models that perform Online predictions as it will help with avoiding Training/Serving Skew. - Don’t rush with Automated retraining. Ad-hoc and on-schedule will bring you a long way. Let me know your thoughts! ?? #LLM #MachineLearning #MLOps
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Generative AI ? AI Project Manager ? Visionary Enterprise Architect ? Software Engineering ? Cloud Migration/Integration ? Project Management ? Business Analysis ? Product Development ? YouTuber ? 20+ Years Exp
Keeping track of experiments during ML model training is essential as it helps to maintain all relevant data for each experiment, keeping machine learning trials organized and enabling reliable conclusions. This process entails tracking the data used, model parameters, and training details to ensure reproducibility and efficiency in model development.
AI Engineer ? Follow me to Learn about AI Systems ? Author of SwirlAI Newsletter ? Public Speaker
What is ???????????????????? ???????????????? (????) in MLOps and what steps are needed to achieve it? CT is the process of automated ML Model retraining in Production Environments on a specific trigger. Let’s look into some prerequisites for this: 1) Automation of ML Pipelines. - Pipelines are orchestrated. - Each pipeline step is developed independently and is able to run on different technology stacks. - Pipelines are treated as a code artifact. ? You deploy Pipelines instead of Model Artifacts allowing Continuous Training In production. ? Reuse of components allows for rapid experimentation. 2) Introduction of strict Data and Model Validation steps in the ML Pipeline. - Data is validated before training the Model. If inconsistencies are found - Pipeline is aborted. - Model is validated after training. Only after it passes the validation is it handed over for deployment. ? Short circuits of the Pipeline allow for safe CT in production. 3) Introduction of ML Metadata Store. - Any Metadata related to ML artifact creation is tracked here. - We also track performance of the ML Model. ? Experiments become reproducible and comparable between each other. ? Model Registry acts as glue between training and deployment pipelines. 4) Different Pipeline triggers in production. - Ad-hoc. - Cron. - Reactive to Metrics produced in Model Monitoring System. - Arrival of New Data. ? This is where the Continuous Training is actually triggered. 5) Introduction of Feature Store (Optional). - Avoid work duplication when defining features. - Reduce risk of Training/Serving Skew. ???? ???????????????? ???? ????: ?? Introduction of CT is not straightforward and you should approach it iteratively. The following could be good Quarterly Goals to set: - Experiment Tracking is extremely important at any level of ML Maturity and the least invasive in the process of ML Model training - I would start with ML Metadata Store introduction. - Orchestration of ML Pipelines is always a good idea, there are many tools supporting this (Airflow, Kubeflow, VertexAI etc.). If you are not doing it yet - grab this next, also make the validation steps part of this goal. - The need for Feature Store will wary on the types of Models you are deploying. I would prioritise it if you have Models that perform Online predictions as it will help with avoiding Training/Serving Skew. - Don’t rush with Automated retraining. Ad-hoc and on-schedule will bring you a long way. Let me know your thoughts! ?? #LLM #MachineLearning #MLOps
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AI Engineer ? Follow me to Learn about AI Systems ? Author of SwirlAI Newsletter ? Public Speaker
What is ???????????????????? ???????????????? (????) in MLOps and what steps are needed to achieve it? CT is the process of automated ML Model retraining in Production Environments on a specific trigger. Let’s look into some prerequisites for this: 1) Automation of ML Pipelines. - Pipelines are orchestrated. - Each pipeline step is developed independently and is able to run on different technology stacks. - Pipelines are treated as a code artifact. ? You deploy Pipelines instead of Model Artifacts allowing Continuous Training In production. ? Reuse of components allows for rapid experimentation. 2) Introduction of strict Data and Model Validation steps in the ML Pipeline. - Data is validated before training the Model. If inconsistencies are found - Pipeline is aborted. - Model is validated after training. Only after it passes the validation is it handed over for deployment. ? Short circuits of the Pipeline allow for safe CT in production. 3) Introduction of ML Metadata Store. - Any Metadata related to ML artifact creation is tracked here. - We also track performance of the ML Model. ? Experiments become reproducible and comparable between each other. ? Model Registry acts as glue between training and deployment pipelines. 4) Different Pipeline triggers in production. - Ad-hoc. - Cron. - Reactive to Metrics produced in Model Monitoring System. - Arrival of New Data. ? This is where the Continuous Training is actually triggered. 5) Introduction of Feature Store (Optional). - Avoid work duplication when defining features. - Reduce risk of Training/Serving Skew. ???? ???????????????? ???? ????: ?? Introduction of CT is not straightforward and you should approach it iteratively. The following could be good Quarterly Goals to set: - Experiment Tracking is extremely important at any level of ML Maturity and the least invasive in the process of ML Model training - I would start with ML Metadata Store introduction. - Orchestration of ML Pipelines is always a good idea, there are many tools supporting this (Airflow, Kubeflow, VertexAI etc.). If you are not doing it yet - grab this next, also make the validation steps part of this goal. - The need for Feature Store will wary on the types of Models you are deploying. I would prioritise it if you have Models that perform Online predictions as it will help with avoiding Training/Serving Skew. - Don’t rush with Automated retraining. Ad-hoc and on-schedule will bring you a long way. Let me know your thoughts! ?? #LLM #MachineLearning #GenAI
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Gen AI | OCI | DevSecOps | Fusion ERP | SaaS Customization | PaaS Development | Oracle Cloud Architecture | SAFe | Agile | Coach | Mentor | Product Developement | 4XOracle | 1X Databricks | 1XSAFe Certified
A great clear explanation about ML Ops. thanks Aurimas Griciūnas for the post.
AI Engineer ? Follow me to Learn about AI Systems ? Author of SwirlAI Newsletter ? Public Speaker
What is ???????????????????? ???????????????? (????) in MLOps and what steps are needed to achieve it? CT is the process of automated ML Model retraining in Production Environments on a specific trigger. Let’s look into some prerequisites for this: 1) Automation of ML Pipelines. - Pipelines are orchestrated. - Each pipeline step is developed independently and is able to run on different technology stacks. - Pipelines are treated as a code artifact. ? You deploy Pipelines instead of Model Artifacts allowing Continuous Training In production. ? Reuse of components allows for rapid experimentation. 2) Introduction of strict Data and Model Validation steps in the ML Pipeline. - Data is validated before training the Model. If inconsistencies are found - Pipeline is aborted. - Model is validated after training. Only after it passes the validation is it handed over for deployment. ? Short circuits of the Pipeline allow for safe CT in production. 3) Introduction of ML Metadata Store. - Any Metadata related to ML artifact creation is tracked here. - We also track performance of the ML Model. ? Experiments become reproducible and comparable between each other. ? Model Registry acts as glue between training and deployment pipelines. 4) Different Pipeline triggers in production. - Ad-hoc. - Cron. - Reactive to Metrics produced in Model Monitoring System. - Arrival of New Data. ? This is where the Continuous Training is actually triggered. 5) Introduction of Feature Store (Optional). - Avoid work duplication when defining features. - Reduce risk of Training/Serving Skew. ???? ???????????????? ???? ????: ?? Introduction of CT is not straightforward and you should approach it iteratively. The following could be good Quarterly Goals to set: - Experiment Tracking is extremely important at any level of ML Maturity and the least invasive in the process of ML Model training - I would start with ML Metadata Store introduction. - Orchestration of ML Pipelines is always a good idea, there are many tools supporting this (Airflow, Kubeflow, VertexAI etc.). If you are not doing it yet - grab this next, also make the validation steps part of this goal. - The need for Feature Store will wary on the types of Models you are deploying. I would prioritise it if you have Models that perform Online predictions as it will help with avoiding Training/Serving Skew. - Don’t rush with Automated retraining. Ad-hoc and on-schedule will bring you a long way. Let me know your thoughts! ?? #LLM #MachineLearning #GenAI
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Data-Philia-ist @ Databricks | Empowering data teams to solve world’s toughest problems !!
“???????????????????? ???????????????? (????)” in MLOps can be useful for some use cases. Databricks platform can be handy here !!
AI Engineer ? Follow me to Learn about AI Systems ? Author of SwirlAI Newsletter ? Public Speaker
What is ???????????????????? ???????????????? (????) in MLOps and what steps are needed to achieve it? CT is the process of automated ML Model retraining in Production Environments on a specific trigger. Let’s look into some prerequisites for this: 1) Automation of ML Pipelines. - Pipelines are orchestrated. - Each pipeline step is developed independently and is able to run on different technology stacks. - Pipelines are treated as a code artifact. ? You deploy Pipelines instead of Model Artifacts allowing Continuous Training In production. ? Reuse of components allows for rapid experimentation. 2) Introduction of strict Data and Model Validation steps in the ML Pipeline. - Data is validated before training the Model. If inconsistencies are found - Pipeline is aborted. - Model is validated after training. Only after it passes the validation is it handed over for deployment. ? Short circuits of the Pipeline allow for safe CT in production. 3) Introduction of ML Metadata Store. - Any Metadata related to ML artifact creation is tracked here. - We also track performance of the ML Model. ? Experiments become reproducible and comparable between each other. ? Model Registry acts as glue between training and deployment pipelines. 4) Different Pipeline triggers in production. - Ad-hoc. - Cron. - Reactive to Metrics produced in Model Monitoring System. - Arrival of New Data. ? This is where the Continuous Training is actually triggered. 5) Introduction of Feature Store (Optional). - Avoid work duplication when defining features. - Reduce risk of Training/Serving Skew. ???? ???????????????? ???? ????: ?? Introduction of CT is not straightforward and you should approach it iteratively. The following could be good Quarterly Goals to set: - Experiment Tracking is extremely important at any level of ML Maturity and the least invasive in the process of ML Model training - I would start with ML Metadata Store introduction. - Orchestration of ML Pipelines is always a good idea, there are many tools supporting this (Airflow, Kubeflow, VertexAI etc.). If you are not doing it yet - grab this next, also make the validation steps part of this goal. - The need for Feature Store will wary on the types of Models you are deploying. I would prioritise it if you have Models that perform Online predictions as it will help with avoiding Training/Serving Skew. - Don’t rush with Automated retraining. Ad-hoc and on-schedule will bring you a long way. Let me know your thoughts! ?? #LLM #MachineLearning #GenAI
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?? Excited to share insights on Integrating RAG with LLM Architecture: Elevating AI Capabilities to New Heights! on level 5 with GTech MuLearn x Pathway RAG, or Retrieval-Augmented Generation, is revolutionizing the landscape of Language Model architectures by seamlessly integrating real-time, verifiable data into generated content. This innovative framework empowers LLMs to enhance their performance, ensuring output is not only accurate but also dynamically updated from external sources. ?? **Benefits of RAG**: 1. **Rich Context**: By tapping into external data sources, RAG provides rich contextual information, enriching the generated content. 2. **Real-Time Info**: Stay ahead with up-to-the-minute data, ensuring your content remains relevant and timely. 3. **Cost Efficiency**: RAG optimizes resource utilization, reducing the need for extensive model training or manual data curation. 4. **Tailored Output**: Customize generated content to specific needs or queries, enhancing user experience and engagement. ?? **Use Cases**: From customer support to content curation, healthcare analysis, and beyond, RAG unlocks a myriad of applications across industries, empowering organizations to deliver insightful, accurate, and timely content. ?? **LLM Architecture Components**: Understanding the architecture behind LLMs is crucial. Key components include: - **User Interface Component**: Enables seamless interaction by posing questions or queries. - **Storage Layer**: Utilizes Vector DB or Vector Indexes to manage and retrieve data efficiently. - **Service, Chain, or Pipeline Layer**: The backbone of the model's operation, often utilizing Chain Library for prompt chaining. ?? **Fine-Tuning Vs. RAG**: While fine-tuning is effective, it has limitations. RAG addresses these drawbacks by offering improved data preparation, cost efficiency, and ensuring data freshness, essential for dynamic content generation. ?? **Prompt Engineering Vs. RAG**: Prompt engineering, though viable, comes with challenges like data privacy concerns and inefficient information retrieval. RAG overcomes these hurdles, ensuring seamless integration of external data while optimizing token limits. In conclusion, the integration of RAG with LLM architecture marks a significant leap in AI capabilities, offering unparalleled accuracy, timeliness, and customization. Embrace the future of AI-driven content generation with RAG! #AI #RAG #LLM #mulearn #pathway #Innovation #ArtificialIntelligence #DataIntegration #TechAdvancement
Co-Founder & CEO @ Melio AI ?? MLOps Evangelist ? Building AI Marketplace | Making AI Frictionless ??
9 个月Model packaging, serialising and containerisation all feel same/same but different. Model versioning, registering, and metadata management are also same/same but different. It's a good review of what each thing means ??